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Studying recurrent mobility perturbations, such as traffic congestions, is a major concern of engineers, planners, and authorities as they not only bring about delay and inconvenience but also have consequent negative impacts like greenhouse gas emission, increase in fuel consumption, or safety issues. In this dissertation, we proposed using the resilience concept, which has been commonly used for assessing the impact of extreme events and disturbances on the transportation system, for high-probability low impact (HPLI) events to (a) provide a performance assessment framework for transportation systems' response to traffic congestions, (b) investigate the role of transit modes in the resilience of urban roadways to congestion, and (c) study the impact of network topology on the resilience of roadways functionality performance. We proposed a multi-dimensional approach to characterize the resilience of urban transportation roadways for recurrent congestions. The resilience concept could provide an effective benchmark for comparative performance and identifying the behavior of the system in the discharging process in congestion. To this end, we used a Data Envelopment Analysis (DEA) approach to integrate multiple resilience-oriented attributes to estimate the efficiency (resilience) of the frontier in roadways. Our results from an empirical study on California highways through the PeMS data have shown the potential of the multi-dimensional approach in increasing information gain and differentiating between the severity of congestion across a transportation network. Leveraging this resilience-based characterization of recurrent disruptions, in the second study, we investigated the role of multi-modal resourcefulness of urban transportation systems, in terms of diversity and equity, on the resilience of roadways to daily-based congestions. We looked at the physical infrastructure availability and distribution (i.e. diversity) and accessibility and coverage to capture socio-economic factors (i.e. equity) to more comprehensively understand the role of resourcefulness in resilience. We conducted this investigation by using a GPS dataset of taxi trips in the Washington DC metropolitan area in 2017. Our results demonstrated the strong correlation of trips' resilience with transportation equity and to a lesser extent with transportation diversity. Furthermore, we learned the impact of equity and diversity can mostly be seen at the recovery stage of resilience. In the third study, we looked at another aspect of transportation supply in urban areas, spatial configuration, and topology. The goal of this study was to investigate the role of network topology and configuration on resilience to congestion. We used OSMnx, a toolkit for street network analysis based on the data from OpenStreetMap, to model and analyze the urban roadways network configurations. We further employed a multidimensional visualization strategy using radar charts to compare the topology of street networks on a single graphic. Leveraging the geometric descriptors of radar charts, we used the compactness and Jaccard Index to quantitatively compare the topology profiles. We use the same taxi trips dataset used in the second study to characterize resilience and identify the correlation with network topology. The results indicated a strong correlation between resilience and betweenness centrality, diameter, and Page Rank among other features of a transportation network. We further looked at the capacity of roadways as a common cause for the strong correlation between network features and resilience. We found that the strong correlation of link-related features such as diameter could be due to their role in capacity and have a common cause with resilience. Doctor of Philosophy Transportation infrastructure systems are among the most fundamental facilities and systems in urban areas due to the role they play in mobility, economy, and environmental sustainability. Due to this importance, it is crucial to ensure their resilience to regular disruptions such as traffic congestions as a priority for engineers and policymakers. The resilience of transportation systems has often been studied when disasters or extreme events occur. However, minor disturbances such as everyday operational traffic situations can also play an important part in reducing the efficiency of transportation systems and should be considered in the overall resilience of the systems. Current literature does not consider traffic performance from the lens of resilience despite its importance in evaluating the overall performance of roads. This research addresses this gap by proposing to leverage the concept of resilience for evaluation of roadways performance and identifying the role of urban characteristics in the enhancement of resilience. We first characterized resilience considering the performance of the roadways over time, ranging from the occurrence of disruptions to the time point when the system performance returns to a stable state. Through a case study on some of the major highways in the Los Angeles metropolitan area and by leveraging the data from the Performance Measurement System (PeMS), we have investigated how accounting for a proposed multi-dimensional approach for quantification of resilience could add value to the process of road network performance assessment and the corresponding decision-making. In the second and third parts of this dissertation, we looked at the urban infrastructure elements and how they affect resilience to regular disruptive congestion events. Specifically, in the second study, we focused on alternative transit modes such as bus, metro, or bike presence in the urban areas. We utilized diversity and equity concepts for assessing the opportunities they provide for people as alternative mobility modes. The proposed metrics not only capture the physical attributes of the multi-modal transportation systems (i.e. availability and distribution of transit modes in urban areas) but also consider the socio-economic factors (i.e. the number of people that could potentially use the transit mode). In the third study, we investigated how urban road networks' form and topology (i.e., the structure of roadway networks) could affect its resilience to recurrent congestions. We presented our findings as a case study in the Washington DC area. Results indicated a strong correlation between resilience and resourcefulness as well as topology features. The findings allow decision-makers to make more informed design and operational decisions and better incorporate the urban characteristics during the priority setting process.

With the ever-growing concerns of environmental and climate concerns for energy consumption in our society, it is crucial to develop novel solutions that improve the efficient utilization of distributed energy resources for energy efficiency and demand response (DR). As such, there is a need to develop targeted energy programs, which not only meet the requirement of energy goals for a community but also take the energy use patterns of individual households into account. To this end, a sound understanding of the energy behavior of customers at the neighborhood level is needed, which requires operational analytics on the wealth of energy data from customers and devices. In this dissertation, we focus on data-driven solutions for customer energy behavior characterization with applications to distributed energy management and flexibility provision. To do so, the following problems were studied: (1) how different customers can be segmented for DR events based on their energy-saving potential and balancing peak and off-peak demand, (2) what are the opportunities for extracting Time-of-Use of specific loads for automated DR applications from the whole-house energy data without in-situ training, and (3) how flexibility in customer demand adoption of renewable and distributed resources (e.g., solar panels, battery, and smart loads) can improve the demand-supply problem. In the first study, a segmentation methodology form historical energy data of households is proposed to estimate the energy-saving potential for DR programs at a community level. The proposed approach characterizes certain attributes in time-series data such as frequency, consistency, and peak time usage. The empirical evaluation of real energy data of 400 households shows the successful ranking of different subsets of consumers according to their peak energy reduction potential for the DR event. Specifically, it was shown that the proposed approach could successfully identify the 20-30% of customers who could achieve 50-70% total possible demand reduction for DR. Furthermore, the rebound effect problem (creating undesired peak demand after a DR event) was studied, and it was shown that the proposed approach has the potential of identifying a subset of consumers (~5%-40% with specific loads like AC and electric vehicle) who contribute to balance the peak and off-peak demand. A projection on Austin, TX showed 16MWh reduction during a 2-h event can be achieved by a justified selection of 20% of residential customers. In the second study, the feasibility of inferring time-of-use (ToU) operation of flexible loads for DR applications was investigated. Unlike several efforts that required considerable model parameter selection or training, we sought to infer ToU from machine learning models without in-situ training. As the first part of this study, the ToU inference from low-resolution 15-minute data (smart meter data) was investigated. A framework was introduced which leveraged the smart meter data from a set of neighbor buildings (equipped with plug meters) with similar energy use behavior for training. Through identifying similar buildings in energy use behavior, the machine learning classification models (including neural network, SVM, and random forest) were employed for inference of appliance ToU in buildings by accounting for resident behavior reflected in their energy load shapes from smart meter data. Investigation on electric vehicle (EV) and dryer for 10 buildings over 20 days showed an average F-score of 83% and 71%. As the second part of this study, the ToU inference from high-resolution data (60Hz) was investigated. A self-configuring framework, based on the concept of spectral clustering, was introduced that automatically extracts the appliance signature from historical data in the environment to avoid the problem of model parameter selection. Using the framework, appliance signatures are matched with new events in the electricity signal to identify the ToU of major loads. The results on ~1500 events showed an F-score of >80% for major loads like AC, washing machine, and dishwasher. In the third study, the problem of demand-supply balance, in the presence of varying levels of small-scale distributed resources (solar panel, battery, and smart load) was investigated. The concept of load complementarity between consumers and prosumers for load balancing among a community of ~250 households was investigated. The impact of different scenarios such as varying levels of solar penetration, battery integration level, in addition to users' flexibility for balancing the supply and demand were quantitatively measured. It was shown that (1) even with 100% adoption of solar panels, the renewable supply cannot cover the demand of the network during afternoon times (e.g., after 3 pm), (2) integrating battery for individual households could improve the self-sufficiency by more than 15% during solar generation time, and (3) without any battery, smart loads are also capable of improving the self-sufficiency as an alternative, by providing ~60% of what commercial battery systems would offer. The contribution of this dissertation is through introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility of the aggregate daily energy load profiles for a community. When combined, the findings of this research can serve to the field of utility-scale energy analytics for the integration of DR and improved reshaping of network energy profiles (i.e., mitigating the peaks and valleys in daily demand profiles). Doctor of Philosophy Buildings account for more than 70% of electricity consumption in the U.S., in which more than 40% is associated with the residential sector. During recent years, with the advancement in Information and Communication Technologies (ICT) and the proliferation of data from consumers and devices, data-driven methods have received increasing attention for improving the energy-efficiency initiatives. With the increased adoption of renewable and distributed resources in buildings (e.g., solar panels and storage systems), an important aspect to improve the efficiency by matching the demand and supply is to add flexibility to the energy consumption patterns (e.g., trying to match the times of high energy demand from buildings and renewable generation). In this dissertation, we introduced data-driven solutions using the historical energy data of consumers with application to the flexibility provision. Specific problems include: (1) introducing a ranking score for buildings in a community to detect the candidates that can provide higher energy saving in the future events, (2) estimating the operation time of major energy-intensive appliances by analyzing the whole-house energy data using machine learning models, and (3) investigating the potential of achieving demand-supply balance in communities of buildings under the impact of different levels of solar panels, battery systems, and occupants energy consumption behavior. In the first study, a ranking score was introduced that analyzes the historical energy data from major loads such as washing machines and dishwashers in individual buildings and group the buildings based on their potential for energy saving at different times of the day. The proposed approach was investigated for real data of 400 buildings. The results for EV, washing machine, dishwasher, dryer, and AC show that the approach could successfully rank buildings by their demand reduction potential at critical times of the day. In the second study, machine learning (ML) frameworks were introduced to identify the times of the day that major energy-intensive appliances are operated. To do so, the input of the model was considered as the main circuit electricity information of the whole building either in lower-resolution data (smart meter data) or higher-resolution data (60Hz). Unlike previous studies that required considerable efforts for training the model (e.g, defining specific parameters for mathematical formulation of the appliance model), the aim was to develop data-driven approaches to learn the model either from the same building itself or from the neighbors that have appliance-level metering devices. For the lower-resolution data, the objective was that, if a few samples of buildings have already access to plug meters (i.e., appliance level data), one could estimate the operation time of major appliances through ML models by matching the energy behavior of the buildings, reflected in their smart meter information, with the ones in the neighborhood that have similar behaviors. For the higher-resolution data, an algorithm was introduced that extract the appliance signature (i.e., change in the pattern of electricity signal when an appliance is operated) to create a processed library and match the new events (i.e., times that an appliance is operated) by investigating the similarity with the ones in the processed library. The investigation on major appliances like AC, EV, dryer, and washing machine shows the >80% accuracy on standard performance metrics. In the third study, the impact of adding small-scale distributed resources to individual buildings (solar panels, battery, and users' practice in changing their energy consumption behavior) for matching the demand-supply for the communities was investigated. A community of ~250 buildings was considered to account for realistic uncertain energy behavior across households. It was shown that even when all buildings have a solar panel, during the afternoon times (after 4 pm) in which still ~30% of solar generation is possible, the community could not supply their demand. Furthermore, it was observed that including users' practice in changing their energy consumption behavior and battery could improve the utilization of solar energy around >10%-15%. The results can serve as a guideline for utilities and decision-makers to understand the impact of such different scenarios on improving the utilization of solar adoption. These series of studies in this dissertation contribute to the body of literature by introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility in energy consumption patterns.

Advances in Information and Communication Technology (ICT) paved the way for decentralized Heating, Ventilation, and Air-Conditioning (HVAC) HVAC operations. It has been envisioned that development of personal thermal comfort profiles leads to accurate predictions of each occupant's thermal comfort state and such information is employed in context-aware HVAC operations for energy efficiency. This dissertation has three key contributions in realizing this envisioned HVAC operation. First, it presents a systematic review of research trends and developments in context-aware HVAC operations. Second, it contributes to expanding the feasibility of the envisioned HVAC operation by introducing novel sensing technologies. Third, it contributes to shedding light on viability and potentials of comfort-aware operations (i.e., integrating personal thermal comfort models into HVAC control logic) through a comprehensive assessment of energy efficiency implications. In the first contribution, by developing a taxonomy, two major modalities – occupancy-driven and comfort-aware operations – in Human-In-The-Loop (HITL) HVAC operations were identified and reviewed quantitatively and qualitatively. The synthesis of previous studies has indicated that field evaluations of occupancy-driven operations showed lower potentials in energy saving, compared to the ones with comfort-aware operations. However, the results in comfort-aware operations could be biased given the small number of explorations. Moreover, required data representation schema have been presented to foster constructive performance assessments across different research efforts. In the end, the current state of research and future directions of HITL HVAC operations were discussed to shed light on future research need. As the second contribution, moving toward expanding the feasibility of comfort-aware operations, novel and smart sensing solutions have been introduced. It has been noted that, in order to have high accuracy in predicting individual's thermal comfort state (≥90%), user physiological response data play a key part. However, the limited number of applicable sensing technologies (e.g., infrared cameras) has impeded the potentials of implementation. After defining required characteristics in physiological sensing solutions in context of comfort-aware operations (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, Doppler radar sensors, and heat flux sensors were evaluated. RGB cameras, available in many smart computing devices, could be a ubiquitous solution in quantifying thermoregulation states. Leveraging the mechanism of skin blood perfusion, two thermoregulation state quantification methods have been developed. Then, applicability and sensitivity were checked with two experimental studies. In the first experimental study aiming to see applicability (distinguishing between 20 and 30C with fully acclimated human bodies), for 16 out of 18 human subjects, an increase in their blood perfusion was observed. In the second experimental study aiming to evaluate sensitivity (distinguishing responses to a continuous variation of air temperature from 20 to 30C), 10 out of 15 subjects showed a positive correlation between blood perfusion and thermal sensations. Also, the superiority of heat flux data, compared to skin temperature data, has been demonstrated in predicting personal thermal comfort states through the developments of machine-learning-based prediction models with feature engineering. Specifically, with random forest classifier, the median value of prediction accuracy was improved by 3.8%. Lastly, Doppler radar sensors were evaluated for their capability of quantifying user thermoregulation states leveraging the periodic movement of the chest/abdomen area induced by respiration. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). On the other hand, in a transient temperature without acclimation time, it was shown that, some of the human subjects (38.9%) used respiration as an active means of heat exchange for thermoregulation. Lastly, a comprehensive evaluation of comfort-aware operations' performance was carried out with a diverse set of contextual and operational factors. First, a novel comfort-aware operation strategy was introduced to leverage personal sensitivity to thermal comfort (i.e., different responses to temperature changes; e.g., sensitive to being cold) in optimization. By developing an agent-based simulation framework and thorough diverse scenarios with different numbers and combinations of occupants (i.e., human agents in the simulation), it was shown that this approach is superior in generating collectively satisfying environments against other approaches focusing on individual preferred temperatures in selection of optimized setpoints. The energy implications of comfort-aware operations were also evaluated to understand the impact from a wide range of factors (e.g., human and building factors) and their combinatorial effect given the uncertainty of multioccupancy scenarios. The results demonstrated that characteristics of occupants' thermal comfort profiles are dominant in impacting the energy use patterns, followed by the number of occupants, and the operational strategies. In addition, when it comes to energy efficiency, more occupants in a thermal zone/building result in reducing the efficacy of comfort-driven operation (i.e., the integration of personal thermal comfort profiles). Hence, this study provided a better understanding of true viability of comfort-driven HVAC operations and provided the probabilistic bounds of energy saving potentials. These series of studies have been presented as seven journal articles and they are included in this dissertation. Doctor of Philosophy With vision of a smart built environment, capable of understanding the contextual dynamics of built environment and adaptively adjusting its operation, this dissertation contributes to context-aware/decentralized HVAC operations. Three key contributions in realization of this goal include: (1) a systematic review of research trends and developments in the last decade, (2) enhancing the feasibility of quantifying personal thermal comfort by presenting novel sensing solutions, and (3) a comprehensive assessment of energy efficiency implications from comfort-aware HVAC operations with the use of personal comfort models. Starting from identifying two major modalities of context-aware HVAC operations, occupancy-driven and comfort-aware, the first part of this dissertation presents a quantitative and qualitative review and synthesis of the developments, trends, and remaining research questions in each modality. Field evaluation studies using occupancy-driven operations have shown median energy savings between 6% and 15% depending on the control approach. On the other hand, the comfort-aware HVAC operations have shown 20% energy savings, which were mainly derived from small-scale test beds in similar climate regions. From a qualitative technology development standpoint, the maturity of occupancy-driven technologies for field deployment could be interpreted to be higher than comfort-aware technologies while the latter has shown higher potentials. Moreover, by learning from the need for comparing different methods of operations, required data schemas have been proposed to foster better benchmarking and effective performance assessment across studies. The second part of this dissertation contributes to the cornerstone of comfort-aware operations by introducing novel physiological sensing solutions. Previous studies demonstrated that, in predicting individual's thermal comfort states, using physiological data in model development plays a key role in increasing accuracy (>90%). However, available sensing technologies in this context have been limited. Hence, after identifying essential characteristics for sensing solutions (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, heat flux sensors, and Doppler radar sensors were evaluated. RGB cameras, available in many smart devices, could be programmed to measure the level of blood flow to skin, regulated by the human thermoregulation mechanism. Accordingly, two thermoregulation states' quantification methods by using RGB video images have been developed and assessed under two experimental studies: (i) capturing subjects' facial videos in two opposite temperatures with sufficient acclimation time (20 and 30C), and (ii) capturing facial videos when subjects changed their thermal sensations in a continuous variation of air temperature from 20 to 30C. Promising results were observed in both situations. The first study had subjects and 16 of them showed an increasing trend in blood flow to skin. In the second study, posing more challenges due to insufficient acclimation time, 10 subjects had a positive correlation between the level of blood flow to skin with thermal sensation. With the assumption that heat flux sensing will be a better reflection of thermoregulation sates, a machine learning framework was developed and tested. The use of heat flux sensing showed an accuracy of 97% with an almost 4% improvement compared to skin temperature. Lastly, Doppler radar sensors were evaluated for their capability of quantifying thermoregulation states by detecting changes in breathing patterns. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). However, using a transient temperature was proven to be more challenging. It was noted that for some of the human subjects (38.9%), respiration was detected as an active means of heat exchange. It was concluded that specialized artifact removal algorithms might help improve the detection rate. The third component of the dissertation contributed by studying the performance of comfort-driven operations (i.e., using personal comfort preferences for HVAC operations) under a diverse set of contextual and operational factors. Diverse scenarios for interaction between occupants and building systems were evaluated by using different numbers and combinations of occupants, and it was demonstrated that an approach of addressing individual's thermal comfort sensitivity (personal thermal-comfort-related responses to temperature changes) outperforms other approaches solely focusing on individual preferred temperatures. The energy efficiency implications of comfort-driven operations were then evaluated by accounting for the impact of human and building factors (e.g., number of thermal zones) and their combinations. The results showed that characteristics of occupants' thermal comfort profiles are dominant in driving the energy use patterns, followed by the number of occupants, and operational strategies. As one of the main outcomes of this study, the energy saving and efficiency (energy use for comfort improvement) potentials and probabilistic bounds of comfort-driven operations were identified. It was shown that keeping the number of occupants low (under 6) in a thermal zone/building, boosts the energy saving potentials of comfort-driven operations. These series of studies have been presented as seven journal articles, included in this dissertation.

Indoor Localization Systems (ILS), also known as Indoor Positioning Systems (IPS), has been created to determine the position of individuals and other assets inside facilities. Indoor Localization Systems have been implemented for monitoring individuals and objects in a variety of sectors. In addition, ILS could be used for energy and sustainability purposes. Energy management is a complex and important challenge in the Built Environment. The indoor localization market is expected to increase by 33.8 billion in the next 5 years based on the 2016 global survey report (Marketsandmarkets.com). Therefore, this thesis focused on exploring and investigating "depth sensors" application in detecting occupants' indoor positions to be used for smarter management of energy consumption in buildings. An interconnected passive depth-sensor-based system of occupants' positioning was investigated for human-building interaction applications. This research investigates the fundamental requirements for depth-sensing technology to detect, identify and track subjects as they move across different spaces. This depth-based approach is capable of sensing and identifying individuals by accounting for the privacy concerns of users in an indoor environment. The proposed system relies on a fixed depth sensor that detects the skeleton, measures the depth, and further extracts multiple features from the characteristics of the human body to identify them through a classifier. An example application of such a system is to capture an individuals' thermal preferences in an environment and deliver services (targeted air conditioning) accordingly while they move in the building. The outcome of this study will enable the application of cost-effective depth sensors for identification and tracking purposes in indoor environments. This research will contribute to the feasibility of accurate detection of individuals and smarter energy management using depth sensing technologies by proposing new features and creating combinations with typical biometric features. The addition of features such as the area and volume of human body surface was shown to increase the accuracy of the identification of individuals. Depth-sensing imaging could be combined with different ILS approaches and provide reliable information for service delivery in building spaces. The proposed sensing technology could enable the inference of people location and thermal preferences across different indoor spaces, as well as, sustainable operations by detecting unoccupied rooms in buildings. Master of Science Although Global Positioning System (GPS) has a satisfactory performance navigating outdoors, it fails in indoor environments due to the line of sight requirements. Physical obstacles such as walls, overhead floors, and roofs weaken GPS functionality in closed environments. This limitation has opened a new direction of studies, technologies, and research efforts to create indoor location sensing capabilities. In this study, we have explored the feasibility of using an indoor positioning system that seeks to detect occupants’ location and preferences accurately without raising privacy concerns. Context-aware systems were created to learn dynamics of interactions between human and buildings, examples are sensing, localizing, and distinguishing individuals. An example application is to enable a responsive air-conditioning system to adapt to personalized thermal preferences of occupants in an indoor environment as they move across spaces. To this end, we have proposed to leverage depth sensing technology, such as Microsoft Kinect sensor, that could provide information on human activities and unique skeletal attributes for identification. The proposed sensing technology could enable the inference of people location and preferences at any time and their activity levels across different indoor spaces. This system could be used for sustainable operations in buildings by detecting unoccupied rooms in buildings to save energy and reduce the cost of heating, lighting or air conditioning equipment by delivering air conditioning according to the preferences of occupants. This thesis has explored the feasibility and challenges of using depth-sensing technology for the aforementioned objectives. In doing so, we have conducted experimental studies, as well as data analyses, using different scenarios for human-environment interactions. The results have shown that we could achieve an acceptable level of accuracy in detecting individuals across different spaces for different actions.

Infrastructures are the most fundamental facilities and systems serving the society. Due to the existence of infrastructures in economic, social, and environmental contexts, all lifecycle phases of such fundamental facilities should maximize utility for the designers, occupants, and the society. With respect to the nature of the decision problem, two main types of uncertainties may exist: 1) the aleatory uncertainty associated with the nature of the built environment (i.e., the economic, social, and environmental impacts of infrastructures must be described as probabilistic); and 2) the epistemic uncertainty associated with the lack of knowledge of decision maker utilities. Although a number of decision analysis models exist that consider the uncertainty associated with the nature of the built environment, they do not provide a systematic framework for including aleatory and epistemic uncertainties, and decision maker utilities in the decision analysis process. In order to address the identified knowledge gap, a three-phase modular decision analysis methodology is proposed. Module one uses a formal preference assessment methodology (i.e., utility function/indifference curve) for assessing decision maker utility functions with respect to a range of alternative design configurations. Module two utilizes the First Order Reliability Method (FORM) in a systems reliability approach for assessing the reliability of alternative infrastructure design configurations with respect to the probabilistic decision criteria and decision maker defined utility functions (indifference curves), and provides a meaningful feedback loop for improving the reliability of the alternative design configurations. Module three provides a systematic framework to incorporate both aleatory and epistemic uncertainties in the decision analysis methodology (i.e., uncertain utility functions and group decision making). The multi-criteria, probabilistic decision analysis framework is tested on a nine-story office building in a seismic zone with the probabilistic decision criteria of: building damage and business interruption costs, casualty costs, and CO2 emission costs. Twelve alternative design configurations and four decision maker utility functions under aleatory and epistemic uncertainties are utilized. The results of the decision analysis methodology revealed that the high-performing design configurations with an initial cost of up to $3.2M (in a cost range between $1.7M and $3.2M), a building damage and business interruption cost as low as $303K (in a cost range between $303K and $6.2M), a casualty cost as low as $43K (in a cost range between $43K and $1.2M), and a CO2 emission as low as $146K (in a cost range between $133K to $150K) can be identified by having a higher probability (i.e., up to 80%) of meeting the decision makers' preferences. The modular, holistic, decision analysis framework allows decision makers to make more informed performance-based design decisions—and allows designers to better incorporate the preferences of the decision makers—during the early design process. PHD Infrastructures, including buildings, roads, and bridges, are the most fundamental facilities and systems serving the society. Because infrastructures exist in economic, social, and environmental contexts, the design, construction, operations, and maintenance phases of such fundamental facilities should maximize value and usability for the designers, occupants, and the society. Identifying infrastructure configurations that maximize value and usability is challenged by two sources of uncertainty: 1) the nature of the built environment is variable (i.e., whether or not a natural hazard will occur during the infrastructure lifetime, or how costs might change over time); and 2) there is lack of knowledge of decision maker preferences and values (e.g., design cost versus social impact tradeoffs). Although a number of decision analysis models exist that consider the uncertainty associated with the nature of the built environment (e.g., natural hazard events), they do not provide a systematic framework for including the uncertainties associated with the decision analysis process (e.g., lack of knowledge about decision maker preferences), and decision maker requirements in the decision analysis process. In order to address the identified knowledge gap, a three-phase modular decision analysis methodology is proposed. Module one uses a formal preference assessment methodology for assessing decision maker values with respect to a range of alternative design configurations. Module two utilizes an algorithm for assessing the reliability of alternative infrastructure design configurations with respect to the probabilistic decision criteria and decision maker requirements, and provides a meaningful feedback loop for understanding the decision analysis results (i.e., improving the value and usability of the alternative design configurations). Module three provides a systematic framework to incorporate both the random uncertainty associated with the built environment and the knowledge uncertainty associated with lack of knowledge of decision maker preferences, and tests the reliability of the decision analysis results under random and knowledge uncertainties (i.e., uncertain decision maker preferences and group decision making). The holistic decision analysis framework is tested on a nine-story office building in a seismic zone with the probabilistic decision criteria of: building damage and business interruption costs, casualty costs, and CO2 emission costs. Twelve alternative design configurations, four decision makers, and random and knowledge sources of uncertainty are considered in the decision analysis methodology. Results indicate that the modular, holistic, decision analysis framework allows decision makers to make more informed design decisions—and allows designers to better incorporate the preferences of the decision makers—during the early design process.

Ground transportation systems are essential for the mobility of people, goods and services. Thus, making sure these systems are resilient to the impact of natural and man-made disasters has become a top priority for engineers and policy makers. One of the major obstacles for increasing the resilience of ground transportation systems is the lack of a measuring framework. Such measuring framework is critical for identifying needs, monitoring changes, assessing improvements, and performing cost-benefit analysis. This research addresses this problem by developing a traffic-based framework for measuring the resilience of ground transportation systems under normal and extreme conditions. The research methodology consisted of: (1) creating a microscopic traffic model of the road under study, (2) simulating different intrusions and interventions, and (3) measuring the resilience of the system under the different scenarios using the framework developed. This research expanded the current definition of infrastructure resilience, which includes the assessment of system performance versus time, to add a third dimension of resilience for ground transportation system's applications, namely: location. This third dimension considers how the system changes along the different locations in the network, which reflects more accurately the continuous behavior of a ground transportation network. The framework was tested in a 24 km segment of Interstate 95 in Virginia, near Washington, D.C. Four hazard conditions were simulated: inadequate base capacity, traffic incidents, work zones, and weather events. Intervention strategies tested include ramp meters and the use of the shoulder lane during extreme events. Public policy was also considered as a powerful intervention strategy. The findings of this research shed light over the current and future resilience of ground transportation systems when subject to multiple hazards, and the effects of implementing potential interventions. Ph. D.

Urban energy systems are often studied in a very similar way in the sense that the characteristics of the underlying physical infrastructure are weighted as the main determinants of energy use predictions, while the behavior of the human population in relation to this systemthe so-called ``energy consumers''in time and urban spaces is effectively neglected. The spatial and temporal variations in infrastructure-population interactivity greatly complicate urban energy systems; the unremitting growth in population and advances in technology mean that the dynamic interrelationship between the population and urban environment will continue to grow exponentially, resulting in increasing uncertainties, unreliable predictions and poor management decisions given the inadequacy of existing approaches. In this dissertation, I explore the interdependencies of spatiotemporal fluctuations of human mobility as an indicator for human activities and energy use in urban areas in three main studies. First, I show that the fluctuations of intra-urban human mobility and energy use have an underlying structure across both time and space, and that human mobility can indeed be used as a predictor for energy use in both dimensions. Second, I examine how one of the dominant drivers of this structure, namely individuals' location-based activities, influence patterns in energy supply and demand across building types (i.e. residential and commercial buildings) and show how variations in the human mobility networks of two distinct urban populations (the so-called returners and explorers) can explain fluctuations in energy use. Third, I introduce an integrated approach for predicting urban energy use across time and space by incorporating these interdependencies. Generating predictive models that capture the spatiotemporal variations in these determinants in urban settings, as suggested in this research, will contribute to our understanding of how variations in urban population activities for particular times and locations influence can be applied to estimate energy use patterns in surrounding areas. PHD

The limited available budget along with old aging infrastructure in nation magnifies the role of strategic decision making for maintenance of infrastructure. The challenging objective is to maintain the infrastructure asset systems in a state of good repair and to improve the efficiency and performance of the infrastructure systems while protecting and enhancing the natural environment. Decision makers are in need of a decision support system to consider these multiple objectives and criteria to effectively allocate funding and achieve the highest possible return on investment on their infrastructure. The research proposes and validates a framework for such decisions. The proposed model aims at finding optimal techniques for maintenance of multiple roadway asset items while taking into account time, cost, level of service and environmental impacts. Therefore, the goal is to answer what are the optimal combinations of maintenance techniques for roadway assets while more than one objective is being optimized. In other words, the main objective is to develop a decision support system for selecting and prioritizing necessary actions for MRandR (Maintenance, Repair and Rehabilitation) of multiple asset items in order for a roadway to function within an acceptable level of service, budget, and time while considering environmental impacts. To achieve these desirable outcomes, this model creates a two-stage framework for a sustainable infrastructure asset management. First a multi-objective problem based on the multi colony ant colony optimization is analyzed. The objectives of the problem are: (i) Minimizing maintenance costs, (ii) Minimizing maintenance time, (iii) Minimizing environmental impacts and (iv) Maximizing level of service improvement. In the second stage, the results of the multi objective optimization will be prioritized using a Multi Criteria Decision Making (MCDM) process. The proposed approach will simultaneously optimize four conflicting objectives along with using a multi criteria decision-making technique for ranking the resulted non-dominated solutions of multi objective optimization. The results of implementation of the proposed model on a section of I-64 highway are presented for a sub-set of asset items. Moreover, the proposed model is validated using a scalable test problem as well as comparison with existing examples. Results reveal the capability of the model in generation of optimal solutions for the selection of maintenance strategies. The model optimizes decision making process and benefits decision makers by providing them with solutions for infrastructure asset management while meeting national goals towards sustainability and performance-based approach. In addition, provides a tool to run sensitivity analysis to evaluate annual budget effects and environmental impacts of different resource allocation scenarios. Application of the proposed approach is implemented on roadway asset items but it is not limited to roadways and is applicable to other infrastructure assets. Ph. D.

In recent years, the application of Life Cycle Assessment (LCA) databases has enabled architects/engineers to quantify the environmental impact of building materials for whole building analysis and comparative analyses of design alternatives. The application of building information modeling (BIM) has facilitated this process by providing designers and engineers with the detailed bill of materials required for LCA. However three limitations exist: First, LCA assessments have been limited to the design phase of a project delivery or post completion phase. Consequently, it does not help incentivize the choice of suppliers and delivery strategies that minimize the cradle-to-site impacts. Second, majority LCA tools ignore the impact of construction means and methods during the construction phase. Third, there is a lack of metrics and visualization tools that assess environmental impacts of decisions made during pre-construction and construction phase. As a result, little incentive exists for suppliers to provide embodied carbon footprint rates, and similarly, for contractors to balance project costs, schedule objectives with the corresponding environmental impact. To address these challenges, we propose and develop a new framework that applies BIM for reliable, effective benchmarking, monitoring, and visualization of embodied carbon footprint of construction projects. It comprises of a benchmarking module, and a monitoring and visualization module. In the experiments, this framework is implemented on concrete placement activities during the construction of the Center for the Arts facility at Virginia Tech. The developed framework can revolutionize construction by a) a rapid assessment and visualization of the deviations between expected and released carbon footprint, b) incentivizing contractors to request that manufacturers and suppliers gauge and share their carbon footprints as a part of contractor submittal process and c) incentivizing those construction firms that can complete their project with an overall carbon footprint rate lower than what is budgeted during the pre-construction or compared to the values from the design phase, while documenting and using the performance results as a benchmark for future similar projects. Master of Science

Intelligent Transportation Systems (ITS) have emerged to use different technologies to promote safety, convenience, and efficiency of transportation networks. Many applications of ITS depend on the availability of the real-time positioning of the vehicles in the network. In this research, the two open challenges in the field of vehicle localization for ITS are introduced and addressed. First, in order to have safe and efficient transportation systems, the locations of the vehicles need to be available everywhere in a network. Conventional localization techniques mostly rely on Global Positioning System (GPS) technology which cannot meet the accuracy requirements for all applications in all situations. This work advances the study of vehicle positioning in ITS by introducing an integrated positioning framework which uses several resources including GPS, vehicle-to-infrastructure and vehicle-to-vehicle communications, radio-frequency identification, and dead reckoning. These technologies are used to provide more reliable and accurate location information. The suggested framework fills the gap between the accuracy of the current vehicle localization techniques and the required one for many ITS applications. Second, different ITS applications have different localization accuracy and latency requirements. A smart positioning algorithm is proposed which enable us to change the positioning accuracy delivered by the algorithm based on different applications. The algorithm utilizes only the most effective resources to achieve the required accuracy, even if more resources are available. In this way, the complexity of the system and the running time decrease while the desired accuracy is obtained. The adjective Smart is selected because the algorithm smartly selects the most effective connection which has the most contribution to vehicle positioning when a connection needs to be added. On the other hand, when a connection should be removed, the algorithm smartly selects the least effective one which has the least contribution to the position estimation. This study also provides an overview about the positioning requirements for different ITS applications. A close-to-real-world scenario has been developed and simulated in MATLAB to evaluate the performance of the proposed algorithms. The simulation results show that the vehicle can acquire accurate location in different environments using the suggested Integrated framework. Moreover, the advantages of the proposed Smart algorithm in terms of accuracy and running time are presented through a series of comprehensive simulations. Master of Science

This research developed a process to improve the systematic deployment of best practices in unfamiliar countries in response to rapid globalization in the engineering and construction industry. The engineering and construction industry needs processes, metrics and tools to improve the deployment of best practices in unfamiliar countries to help facilitate project success, as new challenges are encountered. The research identified issues that are commonly encountered when deploying best practices in unfamiliar countries. The issues were identified using content analysis and verified by experts using the Delphi Method. The Analytic Hierarchy Process was used to establish weightings for the importance of each issue. The weightings were then used to create a scoring metric for companies to measure their readiness for projects. In order to overcome the issues identified in the research, a series of processes and mitigation strategies to overcome the issues were developed, through a series of interviews and focus groups. The International Readiness Passport (IRP) is a tool created to support the use of the metric and the mitigation strategies. This tool utilizes a self-scoring section which is applied to the metric. The tool then generates a report with the relevant mitigation strategies related to each issue, based on the score. To ensure that the IRP provides a meaningful benefit to the systematic deployment of best practices in unfamiliar countries, it was validated through a series of retrospective tests. These tests have confirmed the accuracy and relevance of the process, metric, and tool, as well as the tool\'s capabilities. Master of Science

Previous authors have developed many different types of water pipe condition and failure models using the various methodologies available. Contrary, current utilities are struggling to maintain their current water infrastructure system, due to the lack of effective prediction tools at hand. The gap between the methodologies available in academic research and the tools available to current water utilities needs to be addressed. This paper presents a fuzzy inference prediction model used to forecast the performance rating of individual drinking water pipeline sections (node to node) in which utilities can easily apply to their drinking water infrastructure system. Prior to the development of a prediction model, a through literature and current practice review is completed detailing and summarizing all the available mathematical models. Following, an infrastructure overview is presented detailing the various pipe materials, lifecycle and failure modes and mechanisms. A data structure is also detailed which lists all parameters that affect the condition and/or performance of a pipeline. All of these tools are successfully used to develop a fuzzy inference performance model. The fuzzy inference performance model is considered novel in that it considers close to 30 pipe parameters. Moreover, the performance model is applied using the Western Virginia Water Authority (WVWA) and the Washington Suburban Sanitary Commission (WSSC) databases to evaluate and verify the predicting results. Lab testing of several pipe samples is also used to evaluate the model. The testing consists of a ring bearing test which is used to calculate the rupture modulus of the pipe. Comparing the original vs. the current rupture modulus can determine the remaining strength of the pipe. The remaining strength can then be used to assess the performance results predicted by the fuzzy inference model. Further a framework is set forth which utilizes the model's predicted performance ratings to develop deterioration curves which can be used as a tool to forecast and plan future inspection, repair, rehabilitation and replacement of water pipelines. The deterioration model is made up of a Markov chain approach coupled with a non-optimization technique. Ph. D.

This research was completed in two phases; phase-one involved a mini-scan study of the highway maintenance industry to identify the current state-of-practice in performance-based maintenance contracting (PBMC). Phase one gathered information on domestic and foreign agencies currently using performance-based maintenance on highways. Phase two used the mini-scan study information to build, compare and analyze agency timelines (i.e., VDOT to others). Timelines included major milestones at each agency; milestones which enabled innovation in the field of performance-based contracting. The purpose of comparing VDOT to other agencies was to provide VDOT with industry best practices as well as recommendations for future contract evolutions. Timelines were constructed for Florida DOT, Main Roads of Western Australia, England\'s Highways Agency and New Zealand Transport Agency. Connection links were made between VDOT and the other four agencies based on similarities in procurement laws and maintenance milestones (i.e., 1st Design-Build project). The timeline linkages and collection of information on benefits associated with PBMC (compared to traditional method-based maintenance) were used to make five recommendations for VDOT\'s future maintenance program. VDOT recommendations were: Use performance-based contracting on secondary roads, use area-wide contracts to cover addition facilities, shift VDOT TAMS focus from lowest-cost to a best-value approach similar to England\'s Managing-Agent Contractor, devise a strategic network of highways to prioritize maintenance, use key performance indicators to align Maintenance Division objectives with overall VDOT organization. Recommendations also considered the current restrictions imposed by Virginia procurement laws. Master of Science

Globalization impacts the Architecture, Engineering and Construction (AEC) industry; customers in the AEC industry are seeking lower costs, faster construction schedules and higher quality services. In order to keep up with the changing demand and to stay competitive in the global AEC industry, firms are forming joint ventures and outsourcing design and services work. As a result, these new trends in the AEC industry require the collaboration of widely dispersed and diverse project workers and companies. Accordingly, it becomes increasingly important to understand the impact of diversity on performance. In this sense, the initial aim of this study was to find empirical evidence on how differences in national culture and language may affect performance in Global Project Networks (GPNs). According to the results of the first experiment comparing the performance of multi-cultural versus mono-cultural simulated project networks over time, I found cultural and linguistic diversity to have a negative impact on initial performance. However, culturally and linguistically diverse project networks studied achieved better adaptation performance that has long term advantages. Even though GPNs have long term performance benefits, bringing the widely dispersed project participants together is costly. Therefore, firms are seeking ways to employ collaboration technologies to bring together the project participants. Little research exists to examine how to increase the efficiency of GPNs that collaborate using technologies such as virtual workspaces to perform design work. In order to examine collaboration in GPNs utilizing virtual workspaces, I conducted two experiments. In the first study, I investigated the formation and the maintenance of Transactive Memory Systems (TMSs) and cohesive subgroups as a proxy for performance in two facilitated and two non-facilitated global virtual project networks. I found a negative impact on collaboration effectiveness when process facilitators engaged in content facilitation in virtual project networks, which restricts the establishment of TMSs. The findings of the first study revealed inappropriate ways of facilitating GPNs collaborating in virtual workspaces, which motivated the second study. In the second experiment, I observed two global and two domestic virtual project networks that were appropriately facilitated. I examined the interactions between network members in order to identify whether significant differences between the collaboration approaches of global and domestic virtual project networks exist. Facilitators were utilized more frequently in global networks, particularly in the early stages of collaboration. Boundary spanning visualization technologies within the virtual workspace were also utilized more frequently by the global network members; however, this was due more to the spatial richness of the task than the maturity of the collaboration. The overall findings have significant implications in improving the effectiveness of global project network collaborations in virtual workspaces. Ph. D.

This thesis focused on the performance of large-scale renewable energy projects for the United States Air Force. As global energy demands continue to rise, the need to find ways to save energy and produce alternative sources of energy will increase. The Federal Government has begun to address the challenge of energy production and energy security in recent years. In order to increase both the energy production and energy security for the Air Force, there is a trend to increase the amount of renewable energy produced on military installations. The goal of this research was to compare the estimated and actual performance of these large-scale on-site renewable energy projects at Air Force installations. The variables considered for this research were the execution methods and the renewable energy sources. The performance of each project was evaluated against factors identified in previous sustainable construction studies. The study found that actual performance of third party owned and operated projects differed from the expected performance by less than the Air Force owned and operated projects, and that performance of renewable energy projects differed from the expected performance by less than high performance buildings from previous studies. The study also found factors that contributed to the gap between the expected and actual performance including optimistic modeling, unusual weather, operational issues and higher than expected maintenance of the projects. The results of this research were an initial step in understanding the actual performance of large-scale renewable energy projects. Master of Science

Current models that assess infrastructure failure risk are "linear," and therefore, only consider the direct influence attributed to each factor that defines risk. These models do not consider the undeniable relationships that exist among these parameters. In reality, factors that define risk are interdependent and influence each other in a "non-linear" fashion through feedback effects. Current infrastructure failure risk assessment models are also static, and do not allow infrastructure managers and decision makers to evaluate the impacts over time, especially the long-term impact of risk mitigation actions. Factors that define infrastructure failure risk are in constant change. In a strategic manner, this research proposes a new risk-based infrastructure management framework and supporting system, Risk-Based Dynamic Infrastructure Management System (RiskDIMS), which moves from linear to non-linear risk assessment by applying systems engineering methods and analogs developed to address non-linear complex problems. The approach suggests dynamically integrating principal factors that define infrastructure failure risk using a unique platform that leverages Geospatial Information System services and extensions in an unprecedented manner. RiskDIMS is expected to produce results that are often counterintuitive and unexpected, but aligned to our complex reality, suggesting that the combination of geospatial and temporal analyses is required for sustainable risk-based decision making. To better illustrate the value added of temporal analysis in risk assessment, this study also develops and implements a non-linear dynamic model to simulate the behavior over time of infrastructure failure risk associated with an existing network of levees in New Orleans due to diverse infrastructure management investments. Although, the framework and RiskDIMS are discussed here in the context of levees, the concept applies to other critical infrastructure assets and systems. This research aims to become the foundation for future risk analysis system implementation. Master of Science

The condition of water and wastewater pipelines has been deteriorating with time and since this infrastructure is out-of-sight, the assessment has been neglected over the years. The advancement of technology in various fields has provided pathway for development of several technologies for assessment of the condition of pipeline systems. However, there is no standard guidance or tool for the utilities to use these technologies appropriately. The utilities are unaware of the present state-of- the- art technologies. The predictability and condition assessability indices will help utilities predict a probable failure and take steps to prevent it. The predictability index will indicate the inherent, theoretical predictability of key types of pipe failures. The pipe failure predictability index would be a score calculated by identifying high priority pipe types, characterizing their failure modes, mechanism, conditions, and indicators, reliability of indicators, lead-time of the indicators, and other factors. The condition assessability index will indicate the technical and economical methods of preventing key types of pipe failures. The pipe failure condition assessability index is similar to the predictability index, but it takes into account the capability of existing inspection technologies for measuring the required failure indicator parameters. Prestressed Concrete Cylinder Pipes are used in large diameter water pipelines throughout the United States to convey large volumes of water. Prestressed Concrete Cylinder Pipes are complex composite pipes. Therefore, prediction and prevention of failure of these pipelines is complex and requires a better understanding of the system. This research concentrates around development of Predictability and Condition Assessability Indices for PCCP pipelines. Master of Science

Infrastructure management must move from a perspective that may singularly emphasize facility condition assessment to a broader view that involves nonphysical factors, which may substantially impact facility performance and shorten its service life. Socioeconomic, technological, regulatory, and user value changes can substantially increase the service expectations of existing facilities. Based on a theoretical framework drawn from prior work, this research develops a new approach to model infrastructure performance and assess factors that influence the remaining service life of highway bridges. Key parameters that impact the serviceability of highway bridges are identified and incorporated into a system dynamics model. This platform supports parametric scenario analysis and is applied in several cases to test how various factors influence bridge service life and performance. This decision support system provides a new approach for modeling serviceability over time and gives decision-makers an indication of: (a) the gap between society's service expectations and the service level provided and (b) the remaining service life of a highway bridge. Ph. D.

Knowledge of construction duration is pertinent to a number of project planning functions prior to detailed design development. Funding, financing, and resource allocation decisions take place early in project design development and are significantly influenced by the construction duration. Currently, there is not an understanding of the project factors having a statistically significant relationship with highway construction duration. Other industry sectors have successfully used statistical regression analysis to identify and model the project parameters related to construction duration. While the need is seen for such work in highway construction, there are very few studies which attempt to identify duration-influential parameters and their relationship with the highway construction duration. This research identifies the project factors, known early in design development, which influence highway construction duration. The factors identified are specific to their respective project types and are those factors which demonstrate a statistically-significant relationship with construction duration. This work also quantifies the relationship between the duration-influential factors and highway construction duration. The quantity, magnitude, and sign of the factor coefficient yields evidence regarding the importance of the project factor to highway construction duration. Finally, the research incorporates the duration-influential project factors and their relationship with highway construction duration into mathematical models which assist in the prediction of construction duration. Full and condensed models are presented for Full-Depth Section and Highway Improvement project types. This research uses statistical regression analysis to identify, quantify, and model these early-known, duration-influential project factors. The results of this research contribute to the body of knowledge of the sponsoring organization (Virginia Department of Transportation), the highway construction industry, and the general construction industry at large. Ph. D.

The concept of involving the public in the development of transportation solutions was built in the Federal-Aid Highway Act of 1956, the legislation that authorized the construction of the Interstate Highway System. Better plans, transparent process, and public support are some of the benefits that road managers can obtain by educating and involving the general public. During the last two decades the volume of research performed related to the topic of customer-driven highway maintenance suggests an increasing level of interest in the field. Most research concentrates on gathering information from road users to assess the performance level of highways. However, public opinion can also be collected for measuring the quality of the service delivered by maintenance units. Assessing product and service delivery performance is important for determining the overall performance of highway maintenance programs. The present study examines the relationship between road users' overall perceptions of the quality of highway maintenance services and the variables that define the highway maintenance service quality domain. The results of the study indicate that two service dimensions, Safety and Reliability, explain about half of the variance in overall perceptions of highway maintenance service quality. The procedures developed for the study provide an initial step for further improvement of the highway maintenance service quality measurement. Ph. D.

Infrastructure systems are critical to sustaining and improving economical growth. Poor condition of infrastructure systems results in lost productivity and reduces the quality of life. Today's global economy forces governments to sustain and renew infrastructure systems already in place in order to remain competitive and productive (GAO, 2008). Therefore, civil engineers and policymakers have been quite interested in the overall quality of the highways and bridges throughout the US (Miller, 2007). Transportation networks are essential parts of the Nation's infrastructure systems. Deterioration due to age and use is the main threat to the level of service observed in surface transportation networks. Thus, highway agencies throughout the United States strive to maintain, repair and renew transportation systems already in place (Miller, 2007). A recent disaster, the collapse of the Minneapolis I-35 W Bridge, once again revealed the importance of infrastructure preservation programs and resulted in debates as to how state departments of transportation (DOTs) should and can preserve the existing infrastructure systems. Therefore, it is essential to establish effective maintenance programs to preserve aging infrastructure systems. The major challenge facing the state highway maintenance managers today is to preserve the road networks at an acceptable level of serviceability subject to the stringent yearly maintenance and rehabilitation (M&R) budgets. Maintenance managers must allocate such limited budgets among competing alternatives, which makes the situation even more challenging. Insufficient use of available smart decision-making tools impedes eliciting effective and efficient maintenance programs. Hence, this thesis presents the development and implementation of a network-level pavement maintenance optimization model which can be used by maintenance managers as a decision-making tool to address the maintenance budget allocation issue. The network-level optimization model is established with the application of the Linear Programming algorithm and is subject to budget constraints and the agencies' pavement performance goals in terms of total lane-miles in each pavement condition state. This tool is developed with Microsoft Office Excel. The tool can compute the optimal amount of investment for each pavement treatment type in a given funding period. Thus, the model enables maintenance managers in highway agencies to develop alternative network-level pavement maintenance strategies through an automated and optimized process rather than using what-if analysis. Master of Science

For the last two decades, the road maintenance concept has been gaining tremendous attention. This has brought about new institutional changes, predominant of which is the challenge for maintenance managers to achieve maximum performance from the existing road system. Such challenge makes it imperative to implement comprehensive systems that measure road maintenance performance. However, the road maintenance performance measurement systems developed and implemented by researchers and state departments of transportation (DOTs) mainly focus on the effectiveness measures, e.g., the level-of-service. Such measurement systems do not sufficiently elaborate on the efficiency concept, e.g., the amount of resources utilized to achieve such level-of-service. Not knowing how "efficient" state DOTs are in being "effective" can lead to excessive and unrealistic maintenance budget expectations. This issue indicates the need for a performance measurement approach that can take the efficiency concept into account. Another important concept that is not investigated in the current road maintenance performance measurement systems is the effect of the environmental factors (e.g., climate, location, and etc.) and operational factors (e.g., traffic, load, design-construction adequacy, and etc.) on the performance of the road maintenance process. This issue, again, indicates the need for a performance measurement approach that can take such external and uncontrollable factors into account. The purpose of this research is to develop and implement a comprehensive framework that can measure the relative efficiency of different road maintenance strategies given the (i) multiple inputs and outputs that characterize the road maintenance process and (ii) uncontrollable factors (e.g., climate, traffic, etc.) that affect the performance of such process. It is challenging to measure the overall efficiency of a process when such process is a multiple input-multiple output process and when such process is affected by multiple factors. To address this challenge, an innovative approach to efficiency measurement, Data Envelopment Analysis, is used in this research. It is believed that this research, by taking the efficiency concept into account, will significantly improve the ways that are currently used to model and measure the performance of road maintenance. The findings of this research will contribute new knowledge to the asset management field in the road maintenance domain by providing a framework that is able to differentiate effective and efficient maintenance strategies from effective and inefficient ones. Ph. D.

Project schedules should mirror the project, as the project takes place. Accurate project schedules, when updated and revised, reflect the actual progress of construction as performed in the field. Various methods for monitoring progress of construction are successful in their representation of actual construction as it takes place. Progress monitoring techniques clearly identify when we are behind schedule, yet it is less obvious to recognize when we are going to slip behind schedule. This research explores how schedule performance measurement mechanisms are used to recognize construction projects that may potentially slip behind schedule, as well as what type of early warning they provide in order to take corrective action. Such early warning systems help prevent situations where the contractor and/or owner are in denial for a number of months that a possible catastrophe of a project is going to finish on time. This research develops the intellectual framework for schedule control systems, based on a review of control systems in the construction industry. The framework forms the foundation for the development of a schedule control technique for forecasting schedule slippage — the Required Performance Method (RPM). The RPM forecasts the required performance needed for timely project completion, and is based on the contractor's ability to expand future work. The RPM is a paradigm shift from control based on scheduled completion date to control based on required performance. This shift enables forecasts to express concern in terms that are more tangible. Furthermore, the shift represents a focus on what needs to be done to achieve a target completion date, as opposed to the traditional focus on what has been done. The RPM is demonstrated through a case study, revealing its ability to forecast impending schedule slippage. Master of Science

Accurate and reasonable contract time is important to all aspects of a highway construction project. Unreasonably short contract times can raise the bid price, restrict qualified bidders from submitting bids, reduce the quality of the work, and increase the potential for legal disputes. Conversely, unreasonably long contract times encourage less qualified contractors to submit a bid and are a general inconvenience to the traveling public. The Federal Highway Administration (FHWA) recognizes this, and has recommended that all state highway agencies develop a standardized method for estimating contract performance time. To date, the Virginia Department of Transportation (VDOT) does not have an established method. One major portion of this work is the development of the framework for a multi-level time estimating system to aid in the establishment of contract performance times. This system parallels the VDOT cost estimating process, refining estimates as design details become available along the Project Development Concurrent Engineering Process (PDCEP). Three distinct stages exist along the PDCEP that will facilitate the use of a tool for estimating contract time. Sufficient information to begin the conceptual estimate is known as the project enters the six year plan. The parametric estimate may commence as the project enters the scoping phase. Finally, details for the pre-advertisement time estimate are available upon project field inspection. The second major component of this work, the pre-advertisement estimating database system (BIDDS – Bid Item Duration Data System) was constructed during this work. BIDDS uses project information and characteristics to filter through historical performance time data, returning production data from similar projects. Production data is returned at the bid item level to assist in the estimation of production rates, for calculating activity durations. Master of Science

The general conditions of contract play a very important role in the dynamics of the construction phase. The general conditions of contract outline the roles, rights, and obligations of the contracting parties during the construction phase of a project. The American Institute of Architects General Conditions of Contract for Construction (AIA A201) is considered the most widely used standard form of contracts in the U.S. construction industry. Standard contracts, like AIA A201, are being periodically revised and are becoming more cumbersome over time. The purpose and expectations of the revisions of the contract are not always clearly defined in literature or in practice. Changes to the general conditions have been made, which do not advance the performance goals for a successful project, but rather react to negative experiences. Oftentimes, there are conflicts between the parties' interpretation of the general conditions and between the required administrative practices obligatory by the general conditions during the construction phase, thus impacting the contractual relationships between owners, architect/design professionals, and general contractors. The increasing complexity and size of projects, the multiple-contract interfaces, and the changing times have transformed construction practices and contractual relationships. No longer simply following the lead of the AIA, owners seem to be questioning the value of the design professional, and the tone and path of the AIA A201. The three-phase methodology of this dissertation research set out to advance the understanding of change in the AIA A201 and the impact change has had on the value-added benefit of the design professional during the construction phase and contract administration on a project. The results of this research revealed the following primary conclusions: changes to various provisions pertaining to the key provisions have resulted in the Architect assuming less responsibility during the construction phase of a project; changes to the AIA A201 provision concerning the key provisions have not increased the value of the Architect's performance of contract administration services during the construction phase of a project; with regard to the key provisions, Owners or Owners' representatives perceive the value of the Architect's contract administration services during construction have decreased in recent years. In Phase I, this research identified the key contract provisions, which influence the function of the design professional performed in contract administration during construction. This investigation identified that change exists in key provisions over time and that change may have an influence on the function performed by the design professional during construction and contract administration. Subsequently, in Phase II, this research investigated the effect that change has had on the function performed by the design professional. The respective substantive changes to each provision that may have had a material effect on the function of the design professional were identified and these key provisions were thus studied further. Then, whether or not changes made to key provisions of AIA A201 have had a material effect on the function performed by the architect/design professional during the construction phase and contract administration was examined in Phase III. Ultimately, the research led to an enhanced knowledge of the owner-perceived value-added benefit by the design professional during the construction phase of a project. The practical use of the AIA A201 and its future editions has an influence on improved performance and better working relationships, which ultimately leads to improved constructed projects. The conclusions to this research study have demonstrated that changes made to key provisions of AIA A201 from 1951 to 1997 have diminished the role of the architect. Changes to key provisions have had a material effect on the construction phase, contract administration, and the function performed by the architect/design professional during the construction phase of a project. Before one can make effective changes to improve contractual and working relationships, the change must be identified for the purpose of supporting the industry's efforts to reduce adversarial relations, balance risk, and control schedule and cost, impact the contractual relationships and the value-added benefit of the contractual parties. This dissertation research successfully provided an understanding of the process used and the impact of change in the general conditions of contracts. As such, the knowledge gained from this research illuminated the necessary considerations of change and its impacts on the future development of contracts and their revisions in efforts to create better documents, not to just create bigger documents. Ph. D.

This research project investigates individuals' resistance to change brought about by new information technology implementation in the Architecture, Engineering, and Construction (AEC) industry. By understanding how individual participants resist and adapt to change, their resistance can be better accommodated by the organization in the adoption of new information technology within the AEC industry. This enables researchers and practitioners to understand how new technologies should be introduced within organizations. A social architecture factor model associated with impeding/promoting use of information technologies was created based on a literature review of change management theory on resistance to change and attitude-behavior connections. In Phase I of the research, the personality traits and behavioral characteristics individuals included in the original model were reduced to a smaller number of variables indicative of resistance to information technology change. A revised social architecture factor model was created after this reduction. The variable reduction and revised model were based on data collected from a 50-person sample of the AEC population. At the conclusion of Phase I, a Resistance to Change Index (RTCI) was created, enabling estimations of the intensity of resistance an individual is likely to exhibit using the personality traits and behavioral characteristics kept in the revised social architecture factor model. Phase II of the research investigated relationships between the RTCI and demographics of the individual using a 156-person sample of the AEC population. This phase of the research determined whether different demographic groups within the AEC population exhibited differences in their RTCI. The data analysis found several demographic groups that were different in their likelihood of resistance, including profession, gender, computer understanding and experience, and awareness of past or future changes occurring in their company. Age and education level were expected to have relationships with RTCI, based on industry stereotypes. The data analysis found that these stereotypes have no scientific basis. Two other stereotypes, gender and computer understanding and experience, were supported by the data analysis, however. Ph. D.

Performance based contracting in the transportation arena is a rather recently implemented concept which has a few number of applications in the United States up to date. Nonetheless, the US Department of Transportation has vigorously promoted (by issuing memorandums to be distributed to the Highway Agencies and State Departments of Transportation) the conversion of traditional contracts to performance based contracts to reach a goal of 80% of the service contracts and actions being performance based by the end of fiscal year 2004 (Tomanelli 2003.) Virginia Department of Transportation (VDOT) has been one of the first state agencies that took the initiative of using a performance based contract for the maintenance of a portion of its interstate highway system. This initiative resulted in the establishment of the 'Comprehensive Agreement for Interstate Highway Asset Management Services' between VDOT and a private contractor in 1996 which resulted in the privatization of highway maintenance services. One of the important aspects of this contract related to this research is that it does not encompass any warranty clauses. In other words, the contractor does not provide any warranty to the VDOT for the services it is obligated to undertake. The contract is mute in this subject, making the contractor incur no liability for the underperformance of the maintenance activities. According to this research, this might lead to some cases which VDOT would not be willing to encounter. Just like performance based contracting, the use of warranties in highway contracts in the United States is a quite new concept which mainly dates back to 1995. There are different views possessed by different parties about the possible outcomes of implementing warranties in highway contracts. Nonetheless, this research proposes that the incorporation of warranty clauses into the VDOT issued performance based road maintenance contracts is vital to ensure the quality and durability of the work after the project completion. According to this research, warranty clauses, if developed carefully, give the contractor the motivation to implement superior maintenance practices to improve the overall conditions of the assets during the contract period. This is done in order to lengthen the life of each asset item and achieve the long term performance. Ultimately, the incorporation of warranty clauses is believed to reduce the risk imposed upon VDOT. Within the context of the discussion presented above, this research first illustrates that the complete satisfaction of one of the parties (VDOT) may be in question due to the absence of the warranty clauses within the contract, then lists and explores the benefits that would/may be gained by the incorporation of warranty clauses into the contract, and finally develops a warranty clause template and proposes it to be used in the future performance based road maintenance contracts issued by VDOT. At the bottom line, this study aspires to help the party in question (VDOT) to reduce the risk imposed upon it and to improve the future performance based road maintenance contracts it will issue. This research uses the VDOT contract as a baseline to define the problem and to propose a solution (i.e. the developed warranty clause template) for that. Nonetheless, some of the principles and approaches used during the development of the warranty clause template can also be referred to for the development of warranty clauses for the performance based road maintenance contracts issued by the other state DOTs. Master of Science

The Critical Path Method (CPM) is a widely used tool throughout the construction industry. Since its creation, the use and application of the Critical Path Method has grown tremendously. Up to date, three studies have been performed on how Engineering News Record's (ENR) Top 400 Contractor use CPM. The first study was performed by Edward Davis in 1974 and the second was performed by Amir Tavakoli and Roger Riachi in 1990. This paper is a summary of the third survey, which took place in 2003. The results from the three studies indicate that CPM use by the Top 400 Contractors is growing and the areas of use are expanding. The number one use of CPM throughout the years is detailed planning before the start of construction. Periodic control during construction is another large area of CPM use and has been a large area of growth due to the advances in technology, which make updating a schedule during construction easier and faster than was possible with a mainframe computer in the past. Another area of CPM growth is estimating and bidding for several reasons: use in this area has jumped from 19% to 54% in the past 30 years. An area of concern lately in CPM is the use of precedence diagramming instead of arrow diagramming. Despite the debate, the responding companies found precedence diagrams easier to read, provide more flexibility, and allow for easier use of "smart" relationships (i.e. start-start). Master of Science

In the late 1980s and early 1990s few transportation agencies around the world considered performance-based specifications as an alternative to improve the efficiency of the services provided to the public. These initiatives are better known as Performance-Based Road Maintenance (PBRM). PBRM calls for performance-based work, in which a desired outcome is specified rather than a material or method. This type of specification promises to be an excellent tool to improve government efficiency in maintaining transportation networks; however, without proper monitoring, it could likely yield adverse outcomes. Since PBRM is relatively new, the availability of reliable and comprehensive sets of guidelines to evaluate the effectiveness and efficiency of this type of specifications in the roadway maintenance arena is limited. Transportation agencies currently rely on criteria and procedures they have had developed from their traditional methods used to evaluate performance. Unfortunately, some of these procedures cannot appropriately assess the benefits, if any, accrued by the government as a result of implementing performance-based specifications for the maintenance of the roadway system. This research presents the development of a framework for monitoring PBRM more comprehensively and accurately. The framework considers the assessment of five main areas -- Level of Service Effectiveness, Cost-Efficiency, Timeliness of Response, Safety Procedures, and Quality of Services -- in order to guarantee the comprehensiveness and reliability of the evaluation process. The major contribution of this framework is to provide transportation agencies with guidelines for evaluating the effectiveness and efficiency of PBRM as an alternative delivery method to maintain and preserve the roadway system. Ph. D.

Many construction contracts require contractors to use the Critical Path Method (CPM) scheduling technique as a management tool. In such projects, many participating parties commonly attempt to appropriate float time shown in the CPM schedules in order to advance their own interests. Under current scheduling practices, float time is considered "free" and therefore does not belong to any one party in the construction process. As a result of this conception, when a project delay occurs, float ownership and its utilization can become a major source of dispute. This ambiguous interpretation of total float ownership can be clarified by improving contract language with regard to scheduling specifications in the area of total float management. The purpose of this research dissertation is to introduce a comprehensive practice of float pre-allocation and management terms, for the application of scheduling specifications in the CPM-based construction contract. The proposed concept for managing "total float" involves pre-allocating a set amount of total float on the same non-critical path of activities to the two contractual parties - owner and contractor. For the sake of equity, this research adopts an equal (50-50) allocation concept, which allocates to each party one-half of the total float. This new concept for pre-allocating and managing "total float" involves recommending contract clauses to direct its use and to explain the manner in which responsibility for any resulting delay will be assigned. Six examples of factual situations are provided to illustrate the assigning of responsibility for delays. The features of proposed concept are then compared to those of other theories presently being used. Such a comparison provides insight as to which features have not worked well in the past - and how those of the proposed concept can change this. A Delphi survey is used to validate the total float pre-allocation concept of equal allocation. The survey shows that the concept could significantly increase involved parties' awareness of total float consumption and thus help resolve any potential disputes regarding it. This dissertation considers suggestions obtained from the survey and recommends them for future study. The simple step of inserting new scheduling language into the construction contract documents assures that all participants will become more aware of the fact that when they consume floats, they introduce the potential of increasing project completion times. Ph. D.

Residual value is defined as the price for which a used piece of equipment can be sold in the market at a particular time. It is an important element of the owning costs of equipment and needs to be estimated by equipment managers for making investment decisions. The purpose of this study is to gain insights into the residual value of selected groups of heavy construction equipment and to develop a mathematical model for its prediction. Auction sales data were collected from two online databases. Manufacturer publications and an online source provided size parameters and manufacturers suggested retail prices matching the auction records. Macroeconomic indicator values were collected from a variety of sources, including government agencies. The data were brought into the same electronic format and were matched by model name and calendar date, respectively. Data from auctions in the U.S. and in Canada were considered for this study. Equipment from four principal manufacturers of up to 15 years of age at the time of sale was included. A total of 35,542 entries were grouped into 11 different equipment types and 28 categories by size as measured by horse power, standard operating weight, or bucket volume. Equipment types considered were track and wheel excavators, wheel and track loaders, backhoe loaders, integrated toolcarriers, rigid frame and articulated trucks, track dozers, motor graders, and wheel tractor scrapers. Multiple linear regression analyses of the 28 datasets were carried out after outliers had been deleted. Explanatory variables for the regression model were age in years, the indicator variables manufacturer, condition rating, and geographic region, and selected macroeconomic indicators. The response variable was residual value percent, defined as auction price divided by manufacturers suggested retail price. Different first, second, and third-order polynomial models and exponential and logarithmic models of age were examined. A second-order polynomial was selected from these functional forms based on the adjusted coefficient of determination. Coefficients for the 28 models and related statistics were tabulated. A spreadsheet tool incorporating the final regression model and its coefficients was developed. It allows performing the residual value prediction in an interactive and intuitive manner. Ph. D.

This research presents a Resource-constrained Critical Path Method (RCPM) technique that capitalizes on and improves the existing Critical Path Method (CPM) and Resource-Constrained Scheduling (RCS) techniques. A traditional CPM schedule is not realistic since it assumes unlimited resources, some of which are highly limited in practice. Although traditional RCS techniques can consider resource limitations, they do not provide correct floats and the critical path as the CPM does. The difference between the theoretical remaining total float and the real remaining total float is referred to as "Phantom Float" in this study. Another disadvantage of the traditional RCS techniques is that work sequence in the schedule could be considerably changed with a schedule update resulting in high costs to reorganize it. These problems are caused by the fact that, in addition to technological relationships, a resource-constrained schedule contains resource dependencies between activities that are neglected in traditional RCS techniques. This study proposes a step-by-step RCPM algorithm to consider those resource-constrained relationships. Hence, the method can identify real floats and correct critical paths, considering both technological and resource-dependent relationships. RCPM also provides a certain level of stability with a schedule update due to the newly identified resource relationships. Based on the RCPM algorithm, a prototype RCPM system has been developed using Visual C++, Visual Basic, and Ra (Primavera Project Planner API). The system is integrated with P3, so that it reads project information directly from a P3 project, performs necessary RCPM procedures, and updates the P3 project to contain identified resource relationships. To make the system more practical, functions to handle multiple project calendars and progressed schedules have been included as well. Ph. D.

In the construction industry, the major project participants include the owner, the architect, the contractor and the subcontractors. Traditionally, each participant's activity in the construction process has been isolated to its particular organization's goals. In recent years, one of the leading trends of project deliveries has been a movement toward a more collaborative, teaming environment. Successful collaboration requires effective interaction among project participants. A fundamental understanding of barriers and challenges to effective interaction among a diverse project team is necessary towards creating truly collaborative project teams in construction. Emotional intelligence reflects an individual's emotional awareness and emotional regulation, both important factors of social interaction. An initial understanding of the project participant's emotional intelligence would provide meaningful direction for the construction industry in regards where improvements should be focused. As such, the present research study investigates the 21 components of the Bar-On Emotional Quotient Inventory (EQ-i) within the construction industry. This investigation creates an emotional intelligence yardstick by which to measure and compare future research. This study also explores several trends and relationships within this benchmarking in regards to demographics and General Contractor characteristics. In addition, this current research explores the General Contracting sector's perception of other A/E/C industry sectors. This research study adds to the already growing research that is focusing on the impact of social factors on the success of the A/E/C industry. The results of this study provide a meaningful initial understanding of the emotional intelligence levels of the General Contracting sector. This initial understanding provides academia and industry with several key findings and directions for future research. Master of Science

In the domain of operations design and analysis, the ability to see a 3D animation of processes that have been simulated allows for three very important things: 1) The developer of a simulation model can ascertain that there are no errors in the coding (Verification); 2) The experts, field personnel, and decision makers can discover differences between the way they understand the operation and the way the model developer understands it (Validation); and 3) A model can be communicated effectively which, coupled with verification and validation, makes it "credible" and thus used in making decisions. In the case of simulated construction operations, the existent body of knowledge and understanding did not generally permit modeled processes to be accurately visualized in 3D. The purpose of this research was to remedy this situation and find methods of describing animated 3D worlds that show how construction operations modeled using Discrete-Event Simulation were/can be carried out, using simple text statements and references to 3D CAD drawings. The fundamental question the work addressed was how to achieve accurate, dynamic, smooth, and continuous 3D animation of arbitrarily-complex simulated construction processes, based on meager pieces of operational information that can only be communicated when discrete events occur in simulation runs. The end result of this effort is VITASCOPE, an acronym for VIsualizaTion of Simulated Construction OPErations. VITASCOPE is a simple, parametric-text animation description language that is meant to be written out by end-user programmable software such as discrete-event simulation tools. Sequential instructions written in this language allow a computer to create a 3D virtual world that is accurate in time, space, and appearance; and that shows people, machines, and materials interacting as they build constructed facilities. Ph. D.

The global positioning system (GPS) makes use of extremely accurate measures of the time to determine position. The times required for electronic signals to travel at the speed of light from at least four orbiting satellites to a receiver on earth is measured precisely and used to calculate the distances from the satellites to the receiver. The calculated distances are used to determine the position of the receiver through triangulation. This research takes an approach opposite the original GPS research, focusing on the use of position to determine the time at which events occur. Specifically, this work addresses the question: Can the information pertaining to position and speed contained in a GPS record be used to autonomously identify the times at which critical events occur within a production cycle? The research question was answered by determining the hardware needs for collecting the desired data in a useable format an developing a unique data collection tool to meet those needs. The tool was field evaluated and the data collected was used to determine the software needs for automated reduction of the data to the times at which key events occurred. The software tools were developed in the form of Time Identification Modules (TIMs). The TIMs were used to reduce data collected from a load and haul earthmoving operation to duration measures for the load, haul, dump, and return activities. The value of the developed system was demonstrated by investigating correlations between performance times in construction operations and by using field data to verify the results obtained from productivity estimating tools. Use of the system was shown to improve knowledge and provide additional insight into operations analysis studies. Ph. D.

Graphical representation for construction control information--processes such as scheduling, budgeting and RFIs--follows no formalized method. Many graphics neglect relevant information necessary to highlight trends in or relationships between processes. The principles of data graphics offer visual capabilities beyond those currently employed by the construction industry to display appropriate information in a manner that enhances comprehension of control processes. This paper describes a method that incorporates four tasks; those of structuring and filtering data, editing for density and communicating efficiently; as necessary to creating effective data graphics. In addition to an evaluation technique, these tasks are outlined in a coherent framework. Several construction control processes are then described with respect to these four tasks. Focused application of the framework to the budgeting process produces four graphics that are subsequently evaluated by industry professionals. Conclusions detailed at the end of this document draw together lessons learned from the process of creating data graphics as well as from quantitative and qualitative evaluations of the visual cost report. Master of Science

With the recent developments of several bridge managements systems and their wide-spread use, bridge engineers are realizing the importance of systematic and well planned investments and appropriate management. However the results are far from satisfactory. Bridge management systems need more effective policy analysis tools that can take advantage of the vast amounts of available information to be more efficient. The objective of this research is to develop a policy analysis tool, which is generic in nature and can be applied to any bridge management system provided all the appropriate data is available. In particular, this policy analysis tool is geared to suit policy making, planning and budgeting for the interstate bridges in the state of Virginia. The policy analysis tool developed in this research is a discrete event simulation model capable of extracting information from text files in the Pontis Data Interchange format and simulate user defined element level policies. The model testing was performed using the interstate bridges of the Salem district in Virginia. All the relevant information was extracted from their PONTIS databases. Several scenarios with varying network policies were simulated. The results indicate the validity and the accuracy of the model. The policy analysis tool is a useful addition to the existing policy analysis tools and is capable of handling probabilistic distributions of data instead of single value averages. This will enable the tool to capture more information thereby making the simulation model more realistic. The general framework that was developed here can be applied to any infrastructure problem, and eventually it should be possible to achieve a discrete event simulation based integrated infrastructure management system. Master of Science

State Departments of Transportation (DOT's) have established Bridge Management Systems (BMS) with procedures to aid in estimating service lives of bridge components and elements. Service life estimates, together with cost information, are used to develop life-cycle costs of bridges. These estimates are necessary to prioritize and optimize bridge improvement programs within budgetary constraints. Several factors, including age, traffic, and environment have been identified in current BMS literature as being directly responsible for the deterioration of bridge components or elements. However, no formal methodology exists to determine the effects of the environment. Estimating bridge elements service lives, without considering the effect of environmental factors, could potentially lead to biased estimates. A methodology is proposed using statistical analysis to determine the effects of environmental regions on service life estimates of steel girder bridge component (concrete deck) and element/protective system (girder paint) using bridge inspection field data collected by bridge inspectors. Further, existing deterioration models are incapable of using the non-numeric element level inspection data, which most state DOT's have been collecting for nearly thirty years per Federal Highway Administration guidelines. The data format used were the numerical condition appraisal scale (9 through 0) for concrete deck component, and the letter condition appraisal (G-F-P-C) for steel girder paint element. The methodology proposed an environmental classification system for use in BMS programs. In addition, least squares mean and corresponding standard errors and also means and corresponding standard deviations of service lives at the component and element/protective system levels were estimated. The steel girder paint estimated service lives can be used in scheduling maintenance, repair and rehabilitation operations, and also in life-cycle costs analysis at the project and network levels. Because of limitations in the concrete deck data sets, the estimated concrete deck service lives are not true estimates of their service lives but do reflect the influence of environmental exposure characteristics on their performance. Ph. D.

Concrete construction methods and practices influence the final in-place quality of concrete. A low permeability concrete mixture does not alone ensure quality in-place concrete. If the concrete mixture is not transported, placed and cured properly, it may not exhibit the desired durability and mechanical properties. This study investigates the in-place permeation properties of low permeability concrete bridge decks mixtures used in the Commonwealth of Virginia. Permeation properties were assessed in both the laboratory and in the field using 4-point Wenner array electrical resistivity, surface air flow (SAF), and chloride ion penetrability (ASTM C 1202-97). Laboratory test specimens consisted of two concrete slabs having dimensions of 280 x 280 x 102-mm (11 x 11 x 4-in) and twelve 102 x 204-mm (4 x 8-in) cylinders per concrete mixture. Specimens were tested at 7, 28 and 91-days. Thirteen cylinder specimens per concrete mixture underwent standard curing in a saturated limewater bath. The simulated field-curing regimes used wet burlap and plastic sheeting for 3 (3B) and 7 days (7B) respectively and was applied to both slabs and cylinder specimens. Slab specimen were tested on finished surface using the SAF at 28 and 91 days, and 4-point electrical resistivity measurements at 1, 3, 7, 14, 28 and 91 days. Compressive strength (CS) tests were conducted at 7 and 28 days. Chloride ion penetrability tests were performed at 7, 28, and 91 days. Statistical analyses were performed to assess the significance of the relationships for the following: Total charge passed and initial current (ASTM C 1202-97); 3B resistivity and 7B resistivity; Slab and cylinder resistivity; Slab resistivity and ASTM C-1202-97 (Total Charge and Initial current); and Surface Air Flow and ASTM C-1202-97. Field cast specimens, test slabs and cylinders, were cast on-site during concrete bridge deck construction. The slab dimensions were 30.5 x 40.6 x 10.2-cm (12 x 16 x 4 in.), and the cylinders were 10.2 x 20.4-cm (4 x 8-in). In-situ SAF and resistivity measurements were taken on the bridge deck at 14, 42 and 91 days. In-place SAF and resistivity measurements on laboratory field cast slabs were taken at 7, 14 and 28-days. ASTM C 1202-97 specimens were prepared from field cast cylinders and tested at 7 and 28 and 42-days. The relationship between in-place permeation measures from field specimens was compared to laboratory data. Results indicated no difference in chloride ion penetrability (Figures 7.4 and 7.5) and 28-day compressive strength (Figure 7.2) with regard to differing simulated field curing regimes, for same age testing. There was no significant difference at the 95 % confidence level between 3B resistivity and 7B resistivity specimens tested at the same age (Figures 7.9 and 7.10). A well defined relationship was observed between total charge passed and initial current (Figure 7-6). An inverse power function was found to describe the relationship between charge passed/initial current and electrical resistivity for all laboratory mixtures used in this study (Figure 7.17 – 7.22). Field data was used to validate laboratory established models for charge passed/initial current and electrical resistivity. Laboratory established models were able to predict 30 to 50% of the field data (Figures 7.31 – 7.34). Results indicate that the SAF lacked the sensitivity to classify the range of concretes used in this study (Figure 7.24). Ph. D.

One of the prominent trends in business organizations today is the attention placed on individual personality traits to predict job performance. Distinct personality characteristics of members of various work groups must be carefully considered so that the assignment of individuals to work teams results in successful behaviors and performance improvements. The particular task assignment to various work groups, and thus assignments to individuals, will affect performance. It is critical that these individuals possess both the abilities and behavioral preferences to create conditions that promote the highest probability for success. Contingent variables will always be present in any decision-based action, particularly in complex situations, however, when an individual holds a native preference for a successful pattern of behaviors in a certain task's realm, higher overall organizational performance may be expected. As such, the current study investigates the impact of various personality traits and patterns on critical success behaviors in the Engineering and Architectural profession's project design services. The four project service categories measured in the Critical Success Factors Questionnaire are: Planning (Conceptual Design), Design (Contract Documents), Construction (Administration) and Firm Management duties. The measurement of the individual personalities is accomplished in this investigation through the Myers-Briggs Type Indicator ® (MBTI).. This psychometric instrument measures one's attitudes in dealing with the outside world, as well as one's preferences for data collection and decision making. On an individual basis, it was predicted that persons with personalities whose preferences were towards openness to new ideas, and resistant to closure of the discovery process, would perform well on planning or conceptual design tasks. It was predicted that those with a preference towards compliance with rules, regulations and thorough adherence to established standards, would outperform on tasks of detailed design. The research work also offered a prediction of high performance from persons with a preference for innovative ideas and openness to alternatives in the administration of construction, and predicted a contrast with personalities that vary from this pattern. Of the 85-person sample, it was found that those possessing a preference for Intuitive data collection (MBTI Dichotomy, N) and Perceiving structure, (MBTI Dichotomy, P), outperformed individuals with preferences for Sensing and Judging, (MBTI Dichotomies S and J), in both Planning and Construction Administration. However, professionals with a personality favoring Judging, outperformed in the duties associated with the Design Phase. Contrary to predictions, the decision processes captured in the Thinking/Feeling MBTI® dichotomy (MBTI, T/F) did not meditate the performance in any of the four service categories. The results of the Research indicate a greater utility for personality measures as a diagnostic tool for team and individual performance interventions, rather than a tool for team selection or team-building. The implications of the results of this research, and recommendations for future investigations are discussed. Ph. D.

Simulation modeling and visualization can substantially help in designing complex construction operations and in making optimal decisions where traditional methods prove ineffective or are unfeasible. However, there has been limited use of simulation in planning construction operations due to the unavailability of appropriate visual communication tools that can provide users with a more realistic and comprehensible feedback from simulation analyses. Visualizing simulated construction operations in 3D can significantly help in establishing the credibility of simulation models. In addition, 3D visualization can provide valuable insight into the subtleties of construction operations that are otherwise non-quantifiable and presentable. New software development technologies emerge at incredible rates that allow engineers and scientists to create novel, domain-specific applications. This study capitalized on a computer graphics technology based on the concept of the "Scene Graph" to design and implement a general-purpose 3D Visualization System that is Simulation and CAD-software independent. This system, the "Dynamic Construction Visualizer", enables realistic visualization of modeled construction operations and the resulting products in 3D and can be used in conjunction with a wide variety of simulation tools. This thesis describes the "Dynamic Construction Visualizer" as well as the "Scene Graph" architecture and the Frame Updating algorithms used in its design. Master of Science

Automated data acquisition has revolutionized the reliability of product design in recent years. A noteworthy example is the improvement in the design of aircrafts through field data. This research proposes a similar improvement in the reliability of process design of earthmoving operations through automated field data acquisition. The segment of earthmoving operations addressed in this research constitutes the truck-loader operation. Therefore, the applicability of this research extends to other industries involving truck-operation such as mining, agriculture and forest logging and is closely related to wheel-based earthmoving operations such as scrapers. The context of this research is defined by data collection needed to increase the validity of the results obtained by analysis tools such as simulation, performance measures and graphical representation of variance in an activity's performance, and the relation between operating conditions and the variance in an activity's performance. The automated cycle time data collection is facilitated by instrumented trucks and the collection of information on operating conditions is facilitated by image database and paper forms. The cycle time data and the information on operating conditions are linked together to form the experience database. This research developed methods to extract, quantify and understand the variation in each component of the earthmoving cycle namely, load, haul and return, and dump activities. For the load activity, the simultaneous variation in payload and load time is illustrated through the development of a PLT (PayLoad Time) Map. Among the operating conditions, material type, load area floor, space constraints and shift are investigated. A dynamic normalization process of determining the ratio of actual travel time to expected travel time is developed for the haul and return activities. The length of the haul road, sequence of gear downshifts and shift are investigated for their effect on the travel time. The discussion on the dump activity is presented in a qualitative form due to the lack of data. Each component is integrated within the framework of the experience database. The implementation aspects with respect to developing and using the experience database are also described in detail. The practical relevance of this study is highlighted using an example. Ph. D.

Different means and methods exist in the construction industry to erect bridge superstructures. In planning and execution of the complex construction operations the effects of the chosen erection method need to be considered to achieve a safe and economical process. Failures of bridges under construction have underlined the importance of this issue. Hence, constructability issues need to be considered from the very beginning of projects. Structural analysis mathematically models geometry, boundary conditions, and other structural details, material properties, and so-called actions and incorporates factors of safety. Aforementioned actions, i.e. loads or restraints of deformations may act only temporarily during construction, depending on the method and sequence of erection. However, these construction loads can create considerable stresses in the unfinished structure prior to completion when it still lacks additional redundancy against failure. Furthermore, time-dependent material properties such as creep, shrinkage, and relaxation play a major role, especially in segmental construction. A case study is provided as an example of how constructability issues are dealt with in engineering practice. The Wilson Creek Bridge is a five-span cast-in-place concrete segmental bridge that was erected with Balanced Cantilever Construction. The bridge superstructure incorporated a camber to account for time-dependent deflections in final alignment. Form travelers were used in an alternating manner about the bridge piers to construct cantilever arms that were finally connected at midspan. These travelers remained in place until the box girder segments had reached sufficient strength to be post-tensioned to their predecessors. Casting cycle duration on this project was one week. Master of Science

The benefits of the team building process have been examined for many years by several organizations. The Army Corp of Engineers, Project Management Institute, and Construction Industry Institute studies provided the foundation for this research. Cost growth and schedule growth are two areas that were suspected to be improved by the use of team building. A thorough statistical and graphical analysis was conducted to evaluate the effects of the team building process on cost growth and schedule growth. The project phases and demographic slices were examined for both the contractors and owners. The analysis of the relationship between the team building process and reduction of cost growth and schedule growth provides overwhelming support for implementing the process on construction projects. Although results of particular projects are not always supportive, the overall trends indicate that both budget overruns and schedule extensions can be reduced by the use of a team building process. Particular project categories have been identified as very susceptible to the positive influences of the team building process. Early implementation appears to be crucial in maximizing the benefits of the team building process. Master of Science

Work changes are alterations to the original scope of a contract. Work changes are inevitable in a typical construction project due to the complex nature of the construction industry. It is not possible to foresee these changes at the pre-construction or bidding stages. The changes may affect originally planned means and methods and, thus, may impact the quantity and type of the originally allocated resources such as time, labor, and equipment. In other words, work changes may involve time, money or both. Proper quantification of the impacts solely introduced by changes is therefore vital to all parties and to the success of a project. Failing to quantify impacts due to changes may have adverse consequences that may sometimes result in litigation. Several techniques are currently employed by the construction industry to quantify the impacts of changes. There are also a few simulation case studies used for productivity-related impact analysis. Certain simulation techniques and capabilities, such as modeling flexibility, involvement of historical data, and capability to compare alternatives enable simulation to be a promising tool for quantifying impacts due to changes. Despite the fact that simulation is becoming popular in the construction industry, up to this point in time, there are no established guidelines for the use of simulation in quantifying impacts due to work changes. Simulation still needs exploration with further research and applications that are specific to the problems of the construction industry. There is a solid need to explore the opportunities for employing simulation in quantifying impacts. The objective of this work is to explore the use of simulation in quantifying work change impacts. The study consists of a literature review on the relevant topics. It also analyzes and quantifies a case study in depth. The lessons learned from the case study showcase the procedure and potential of the methodology. The output of the research is valuable for practitioners who wish to employ simulation in quantifying impacts due to work changes. Master of Science

In this study, a block manufacturing process is investigated using simulation as a tool to optimize resource utilization. First, the problem statement and the objectives of the study are established. Second, various data are collected by means of time studies, interviews, and physical observation of the operation. Activity durations are stripped and their distributions are obtained by using computer technology. When all required data are collected, STROBOSCOPE is used to create a model of the entire production process. The model is verified and validated to ensure that it performs as intended before running experiments. Results from experiments are analyzed by the use of spreadsheets and graphs. Recommendations with respect to the current operation and future changes are proposed. In both instances, the unit cost and the production rate are used as the criteria to determine the most effective resource usage. In conclusion, the minimization of production costs increases profit for manufacturers, reduces the construction cost for contractors, and results in lower prices to the end users. Note: for more information about required softwares to run the simulation and the animation, please contact: Julio C. Martinez, Ph.D., Assistant Professor of Civil Engineering (Construction), Virginia Tech, Blacksburg, VA 24061-0105, voice: (540) 231-9420, fax: (540) 231-7532, e-mail: julio@vt.edu, http://strobos.ce.vt.edu Master of Science

The management of heavy construction equipment is a difficult task. Equipment managers are often called upon to make complex economic decisions involving the machines in their charge. These decisions include those concerning acquisitions, maintenance, repairs, rebuilds, replacements, and retirements. The equipment manager must also be able to forecast internal rental rates for their machinery. Repair and maintenance expenditures can have significant impacts on these economic decisions and forecasts. The purpose of this research was to identify a regression model that can adequately represent repair costs in terms of machine age in cumulative hours of use. The study was conducted using field data on 270 heavy construction machines from four different companies. Nineteen different linear and transformed non-linear models were evaluated. A second-order polynomial expression was selected as the best. It was demonstrated how this expression could be incorporated in the Cumulative Cost Model developed by Vorster where it can be used to identify optimum economic decisions. It was also demonstrated how equipment managers could form their own regression equations using standard spreadsheet and database software. Ph. D.

The construction industry has been plagued with an increasing number of claims and high litigation costs. How do we reduce conflict and litigation in the construction process? On one hand, leaders of the construction industry in the United States (US) focused their efforts on improving alternative dispute resolution mechanisms. For instance, the American Society of Civil Engineers has introduced the Dispute Review Board (DRB) as a complementary provision to standard US construction practices. The establishment of the DRB to solve construction disputes on the job, avoid claims, and reduce project costs has proven considerable success. On the other hand, construction industry leaders in the United Kingdom (UK) have focused some of their efforts on improving general contract conditions. The Council of the Institution of Civil Engineers of the UK has introduced the New Engineering Contract (NEC) to the construction industry as an alternative to presently used contracts. The NEC proposes to be an innovative, non-adversarial mechanism to resolve disputes on the job, avoid and reduce claims, and to assuage rising litigation costs in the construction industry. It too has proven considerable success in its efforts. This research concentrates on the DRB and the NEC as attempts by construction leaders to modernize and improve construction practices. In summary, the research compares the success stories of the DRB and the NEC as approaches to combating the adversarial nature, increasing number of disputes and rising litigation costs in the construction industry. The main conclusions ascertained in this research are as follows. Despite coming from similar business environments, construction industry leaders in the US and the UK embarked on different methods to address the issues plaguing the industry and to improve construction practices. Both in the US and the UK, construction leaders were mostly influenced to proactively seek and implement change in construction practices by experts from within the engineering and construction industry vanguard. The undertaking of these changes have shown similar success stories and the results have produced substantial impacts on the construction process. In conclusion, the efforts of construction leaders to implement the DRB and the NEC have provided effective mechanisms in improving communication and relations, and managing disputes in a timely fashion at the job site level. Master of Science

The realization that the U.S. infrastructure is deteriorating and that there is a need to establish a strategy to prevent an infrastructure catastrophe have propelled the development of various infrastructure management systems. Often, the expansion of transportation facilities is regarded as a means to the improvement of the condition of transportation infrastructure. However, building more infrastructure than can be properly maintained causes serious deterioration of the existing infrastructure. Sustainable development from a highway management perspective can be equated with qualitative development, which improves the current condition of the highway system, rather than expanding its physical resources. The objective of this research is to develop a highway management strategy to help achieve sustainable development for the Commonwealth of Virginia. This research is performed by developing a transportation planning model for state highway management (TPMSHM) within the framework of a decision support system (DSS). The planning model consists of ten subsystems, including pavement and bridge management subsystems. These subsystems encompass various socioeconomic parameters that influence the physical status of highways. In the dynamic simulation model, these parameters are expressed in causal relationships using a system dynamics methodology. The types of trajectories for highway conditions that lead to sustainable development are provided. This research proposes a state-dependent prioritization strategy for calculating efficient budget shares by hierarchical levels of highway conditions. In this strategy, the proportions of the highway budget allocated to each level of management activity are determined by the physical conditions of the highways. Highways in the worst condition are given the first priority to receive the budget allocations. The model also addresses the policy of raising fuels tax to increase the state's transportation revenue. The adverse impact of a fuels tax increase is discussed in terms of revenue, the physical sufficiency of highways, and user benefits. The TPMSHM constitutes a leading component of the DSS and governs the building processes of other two components, which include a Data Base and a Display Base. A Data Base is constructed by listing all the parameters needed by the TPMSHM within a frame designed in terms of the records and fields of the parameters. A Display Base is demonstrated in a possible form using system dynamics' Powersim software. The graphical capability of representing the simulation results and the interactive user interface inherent in the software are examined. The emphasis of this research is placed on the development of the TPMSHM, which strives to manage the physical condition of the state highway system at an acceptable level through a state-dependent prioritization strategy to achieve sustainable development. Ph. D.

Strategic procurement items, including complex engineered equipment and systems essential for project performance, are frequently designed, manufactured, and delivered by suppliers who are outside the circle of cooperation between owner, engineer, and contractor. When suppliers are excluded from the design and planning stages of a project, much of the knowledge needed for successful design and integration is lost or underutilized. This research was done as part of a Construction Industry Institute sponsored project to develop and quantify a step change to the EPC process that will bring the supplier into the circle of cooperation between the owner, engineer, and contractor. The result was a step change entitled PEpC (Procure, Engineer, procure, and Construct). This research also sought to provide implementation guidelines for the recommended step change. Through an examination of the literature, a survey of industry experts, and the review of four case studies, this research found that PEpC, the step change recommended by the Construction Industry Institute research team, may reduce both the time and cost required to complete a project. The anticipated savings in project duration is expected to fall between 10 and 15 percent, while the anticipated reduction in project direct labor cost is expected to fall between 4 and 8 percent. Master of Science