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Transportation infrastructures and operations can be severely impacted during flood events, leading to significant disruptions to the flow of goods and services. Although numerous studies have evaluated the direct impacts of flood events on the performance of transportation infrastructures, the indirect impacts or cascading effects have been rarely assessed. Hence, this paper examines the cascading effects of floods on transportation infrastructure using data mining algorithms. First, 33 effects of flood events on transportation infrastructure have been identified based on data collected for multiple flood events in New York and New Jersey. Second, association rule mining analysis was implemented to identify the key co-occurrences between flooding and the different events. Third, network analysis was conducted to quantify the co-occurrences or key combinations among the events. Fourth, cluster analysis was used to group or prioritize the cascading effects and co-occurring events into highly connected clusters to identify the most critical ones based on two scenarios: (1) without consideration of co-occurrences (Scenario 1); and (2) with consideration of co-occurrences (Scenario 2). The findings provided insights that while some cascading impacts could be individually critical/frequent (under Scenario 1), other cascading impacts could also result due to a combination of different effects that might not be perceived to be critical on the individual level but rather become critical when combined with other cascading events (under Scenario 2). The outcomes of this paper demonstrate the importance of considering the co-occurrences between the events and cascading effects, rather than analyzing them in isolation. This study adds to the body of knowledge by offering an analytical approach that could be used to identify and prioritize critical cascading effects of flood events on the operations and performance of transportation infrastructures. [ABSTRACT FROM AUTHOR]

A reasonable method for estimating alighting stops can provide a data guarantee for schedule design and vehicle scheduling. However, few studies, to our best knowledge, have addressed this problem using the variable distance threshold for different types of passengers. This paper is based on available smart card data, and a two-stage algorithm is designed to obtain missing alighting stations in the entry-only urban public transportation system. This algorithm divides passengers into seven types, determines varying distance thresholds for different passengers, and includes five assumptions. Based on the first three assumptions, the corresponding subalgorithms are designed, respectively, constituting the first-stage algorithm, and the first match for all passengers' alighting stations is carried out. The second-stage algorithm is designed on the basis of the latter two assumptions. A secondary match is conducted on data that were not successfully matched in the first stage to ensure that alighting stops for all passengers are identified. The proposed methods are verified using urban public transport data from the city of Zhuhai as an example. Research results show that the drop-off stops where approximately 74% of passengers can be determined after matching the first-stage algorithm. The distance thresholds for various passengers are different, and the distance thresholds for the same type of passengers over different days are also different. Compared with the traditional fixed distance threshold, the variable distance threshold for various passengers proposed in this paper can improve the match rate of alighting station estimate, about 9%. By appropriately extending the distance thresholds for different passengers, the match rate for passengers who work or live in the suburbs can be further improved, which is in line with the actual travel status of such passengers. The second-stage algorithm can identify alighting sites for a single or unlinked trip. The efficiency of the methodology proposed in this study can meet the needs for practical applications, which may be applied to urban public transport systems. [ABSTRACT FROM AUTHOR]

Collision accidents of construction vehicles frequently occur during the construction of building projects. It is difficult to warn of collision risk efficiently by using the safety control methods of on-site inspection and viewing surveillance video. Therefore, a novel collision risk warning model for construction vehicles is proposed in this paper. The model comprises a state awareness module and a collision risk assessment module. In the state awareness module, the YOLOv7 algorithm is used to identify and locate construction vehicles. The DeepSORT algorithm is used for the real-time tracking of construction vehicles. Finally, the state awareness module obtains a wealth of sensing information and provides it to the collision risk assessment module. In the collision risk assessment module, the speed assessment method and the minimum safety distance assessment method of construction vehicles are innovatively proposed and compiled. In the speed assessment method, the speed of construction vehicles is calculated and speeding warnings are performed using the transformation matrix between latitude and longitude coordinates and image pixel coordinates. In the minimum safety distance assessment method, the original surveillance picture is converted into a bird's eye view (BEV) of the world coordinate system through inverse perspective mapping (IPM). Then the distance of all construction vehicles is traversed, and the visual warning of the minimum safe distance is provided. The effectiveness and robustness of this collision risk warning model were verified by taking three practical projects in different construction stages as examples. The research results show that the mean average precision (mAP@0.5:0.95) of the construction vehicles collision risk warning model can reach 89.10%, and it has a good tracking effect and an efficient collision risk warning strategy. [ABSTRACT FROM AUTHOR]

Tensegrity structures, which consist of tension cables and compression rods, are widely used in various fields. It is particularly important to find the tensegrity with definite geometric configuration for the following research and application. This paper presents a traversal topology-finding method for tensegrity structures based on dynamic programming algorithm and ground structure method. After the designer has given the cable topology, the dynamic programming is adopted to realize the traversal selection of the rod. In order to reduce the traversal space and realize the constraint of rod length type, the rod is classified according to length. Compared with existing topology-finding methods, the algorithm can not only output all feasible topologies but also incorporate structural constraints, such as rod length type and prestressed stability, into the calculation process. Four tensegrity numerical examples illustrate the feasibility and effectiveness of the topology-finding method. [ABSTRACT FROM AUTHOR]

To improve the current manual and iterative nature of clash resolution on construction projects, current research efforts continue to explore and test the utilization of machine-learning algorithms to automate the process. Though current research shows significant accuracy in automating clash resolution, many have failed to provide clear explanation and justification for the selection of their feature and label space. Since this is critical in developing an effective and explainable solution in machine learning, it is crucial to address this research gap. In this paper, the authors utilize an in-depth literature review and industry interviews to capture domain knowledge on how design clashes are resolved by industry experts. From analysis of the knowledge captured, we identified 23 factors considered by experts when resolving clashes and five alternative solutions/options to resolve a clash. Using a pool of industry experts, a modified Delphi approach was conducted to validate the factors and options and to determine a priority ranking. The authors identified 94 industry experts based on a predetermined qualification matrix to take part in the modified Delphi. Twelve participants responded and took part in the first round, and 11 completed the second round. A consensus was reached on all clash factors and resolution options. Factors including "clashing elements type," "constrained slope," "critical element in the clash," "location of the clash," "code compliance," and "project stage clashing element is in" were ranked as the most important factors, while "clashing element material" and "insulation type" were considered the least important. Participants also showed more preference to the "moving the clashing element with low priority in/along x-y-z directions" option to resolve clashes. These identified factors and options will be utilized to collect specific clash data to train and test effective and explainable machine-learning algorithms toward automating clash resolution. [ABSTRACT FROM AUTHOR]

Over the decades, significant efforts have been made in evaluating pavement condition by mounting cameras on a vehicle and automatically analyzing images by incorporating digital image processing techniques. Despite recent advances, there are still some limitations associated with current automated crack collection and analysis systems such as potential risk to vehicular safety, limited coverage by cameras, traffic blocking camera views, and errors in analyzing images for crack extent and severity. Inspired by current developments of deep learning technology such as object classification and sematic segmentation along with unmanned aerial vehicle (UAV) technology, this paper presents a set of comprehensive automated crack analysis algorithms based on a combination of deep learning and UAV images. To extract pavements from UAV images and segment cracks from extracted pavement images, the contracting encoder path of the U-Net model was modified with various deep learning models such as Pre-trained VGG 16, ResNet 50, Inception V3, and DenseNet 169 models as a backbone. Based on the least false negative and false positive outputs, the Inception V3 model with dice loss using a nonaugmented dataset and the Inception V3 model with focal loss using a nonaugmented dataset model showed the best performance for pavement extraction and crack segmentation, respectively. A tile-based pavement crack analysis system was then developed to measure percent cracking and crack widths from segmented crack images. It can be concluded that the developed pavement extraction and crack analysis system using UAV images will help public agencies evaluate pavement conditions in a systematic and cost-effective manner. [ABSTRACT FROM AUTHOR]

The high pressure of water distribution networks (WDNs) increases leakage, bursts, and water consumption. Pressure-reducing valves (PRVs) are widely used in WDNs because of their flexibility and effectiveness in pressure management. This paper studies pressure management by locating and adjusting PRVs in WDNs, considering the proper objective function and the suitable optimization algorithm. The single-objective algorithms and the EPANET hydraulic simulator in MATLAB programming are combined to manage the pressure. Initially, the proposed method improved by selecting the optimal optimization algorithm and objective function. Both fixed outlets and time-modulated modes of PRVs for two ZJ and Bellingham WDNs as case studies were used. Considering the cost, reliability, hydraulic performance, and leakage, the results were then analyzed and compared by changing the number, location, and adjustment of each PRV. The outcomes show the effect of choosing the best algorithms and cost functions on the results. The results also show that this method increases the reliability and controls the nodal pressure to a desired level in WDNs. Practical Applications: Leakages in water distribution networks (WDNs) are problematic for utilities and other governmental agencies. Pressure management in WDNs is inevitable to reduce the rate of bursts and leaks. Using fixed- and time-modulated pressure-reducing valves (PRVs) is essential for smart pressure management in WDNs. This study presented a method for positioning and adjusting PRVs in WDNs. The proposed method was applied to ZJ and Bellingham WDNs by choosing the best optimization algorithm and objective functions. The grey wolf optimizer (GWO) algorithm is proposed as one of the best algorithms for this subject. The presented method tries to provide the best plan for pressure management by examining the cost, leakage, and reliability. The results show that the presented method effectively increases reliability and reduces leakage in WDNs. [ABSTRACT FROM AUTHOR]

In this paper, a framework of active obstacle avoidance for autonomous vehicles based on the hybrid soft actor-critic (SAC) algorithm is proposed. In the stage of local path planning, a comprehensive cost function considering collision risks, deviation from the global route, road lines crossing, and driving comfortability is developed to provide a local optimal path avoiding both static and dynamic obstacles considering multiple predicting timesteps. Then, a path tracking controller on the foundation of a hybrid SAC algorithm is designed to mitigate the problem of high sample complexity caused by random initialization of parameters in conventional reinforcement learning approaches. Model predictive control (MPC) plays a guiding role by applying its control action to combine with the action of SAC online to obtain a more effective state and reward information for training. The mechanism of the combination of MPC with SAC to balance the exploration and reliability is explained in detail. In order to improve the convergence rate and learning efficiency, a dual actor network structure for two different control actions is adopted. With considerations of various relevant factors influencing the control effect, the reward for the hybrid SAC algorithm is designed carefully. Finally, the results of simulation experiments illustrate that the proposed approach performs effectively with the assurance of safety and driving comfortability. In summary, the hybrid SAC algorithm with dual actor networks performs better than other algorithms for comparison in all test scenarios in this paper. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) provides strong support for bridge safety evaluation by tracking the real-time structural performance. An accurate and efficient imputation method for missing data in the SHM system is of vital importance for bridge management. In this paper, an innovative vertical–horizontal combined (VHC) algorithm is proposed to estimate the missing SHM data by a more comprehensive consideration of different types of information reflected in different time dimensions of the monitoring data. In the vertical time dimension, the variation trends and changing ranges for the missing data are analyzed by the relational analysis from the global view of data. In the horizontal time dimension, the exact value of the missing data is estimated based on the adjacent data inside the missing data set from the local view of data. The proposed VHC algorithm is validated and compared with the current methods in the data imputation for the missing deflection values in the SHM system of an example tie-arch bridge. The results show that the proposed algorithm demonstrates the best imputation performance among all methods for both missing types with the highest imputation accuracy/similarity and satisfying the 95% confidence bound. The application of reward coefficient significantly increases the imputation accuracy for the continuous missing situation and the improvement grows with the missing step number. Loss ratio and sample size also have significant influences on the imputation performance with the proposed method. Data missing in the structural health monitoring (SHM) system affects the continuity of monitoring data and the structural conditions reflected by them. This paper proposed an innovative data imputation algorithm combining information from both vertical and horizontal time dimensions of the monitored data in the bridge SHM system. The proposed vertical–horizontal combined (VHC) algorithm shows better imputation performance when compared with the current methods widely used in engineering practice and shows a higher applicability in a small sample size situation. Thus the proposed algorithm provides an accurate and efficient tool for dealing with the data missing phenomenon in the SHM system and is of great importance to the safety evaluation and further management decision process for bridge structure. Moreover, the methodologies and approaches proposed in this paper are quite general and can be applied to the missing data imputation problems in many research fields with time series data. [ABSTRACT FROM AUTHOR]

Estimating pavement internal forces due to the swelling or shrinking of expansive soils is of vital importance for planning, design, and construction. Such efforts can minimize some possible damage due to expansive soils during pavement services. This study extends an analytical model on the single-layer Euler–Bernoulli beam resting on the Winkler foundation to estimate pavement internal forces in the multilayer pavement structure. Besides, an improved efficient algorithm is proposed in this paper for computing the value of virtual load parameters. The proposed closed-form solution and iterative algorithm are then verified by comparing the degraded solution with the single-layer beam model and the existing iterative algorithm. The influences of the beam model (single layer or multilayer) and foundation parameters on the pavement internal forces have also been investigated through parametric studies. Furthermore, the computational efficiency is compared between the iterative algorithm adopted by the previous researcher and the iterative algorithm proposed in this paper. It can be seen that a single-layer beam model may underestimate pavement internal forces induced by expansive soils. Foundation parameters will affect the value of virtual load parameters. Also, it can be found that the iterative algorithm proposed in this paper can reduce the iterative calculation time considerably. Moreover, it can be observed that with more fitting points and fitting terms, the fitting has a more accurate effect, and the advantages of using the iterative algorithm proposed in this paper are more pronounced. [ABSTRACT FROM AUTHOR]

The estimation of construction quantities is a critical aspect of any construction project, and accuracy in the quantity takeoff (QTO) process is an integral part in achieving project success. Accuracy can be improved by automating the QTO process. However, one of the gaps in automated QTO is found to be the lack of a platform-ignorant computing algorithm regarding different building information modeling (BIM) authoring tools. Available BIM-based commercial cost estimation software programs such as Autodesk Naviswork, Autodesk QTO, iTWO CostX, Innovaya Visual QTO, ConstrucSim, and so forth, are limited in terms of full automation, largely because full interoperability across all the different applications has not yet been achieved. This paper explores interoperable QTO algorithms using industry foundation classes (IFC)-based BIM. To evaluate the IFC-based QTO algorithms, a case study was conducted on a residential development project in Kalamazoo, MI. Comparing with commercial software used in practice, the IFC-based QTO algorithms showed consistent results, whereas were more powerful regarding their independence from BIM authoring tools in extracting the volumetric and areal quantities of building components. The IFC-based QTO algorithms processed the different types of building element configurations in the residential development project smoothly. Furthermore, the QTO algorithms are expected to reduce the manual input required in generating QTO through interfacing with IFC data which is the ISO international standard for BIM. [ABSTRACT FROM AUTHOR]

This paper describes the design and optimization of a cooling strategy based on a battery cooling system with indirect liquid-cooled plate heat exchange. The performance of the battery cooling system was analyzed using single-factor thermal simulation analysis. A cosimulation platform was built, and the rule-based multiparameter control strategy was used to design the heat dissipation logic, which was verified in the cosimulation platform. To improve the heat dissipation performance further, an orthogonal test (OT) was used to analyze the effects of different control parameters on each performance index during the battery discharge process, and the optimal combination of control parameters under a balanced multiobjective strategy was obtained. The results of the particle swarm optimization (PSO) algorithm with different weight coefficients were introduced for comparison. It was found that the OT and PSO can keep the temperature difference within the safe range. The maximum temperature was reduced by 3.57 and 4.10 K, respectively, and the average output power was increased by 11% and 9%, respectively, which indicates that the OT maintained good performance despite the significant workload reduction. The feasibility and reliability of the battery thermal management system control strategy optimized using OT were verified. [ABSTRACT FROM AUTHOR]

The origin and destination (O-D) of public transit passengers are important for the planning and operation of the transit system. However, only 46% of public transit agencies have a smart card system in the US, and most of them require an entry-only tap, which prohibits identifying passenger destinations unless utilizing an estimation model. Therefore, there is a need for a cost-effective and automated solution to facilitate the majority of the US transit agencies in recognizing the origin and destination of passengers as well as capturing passenger transfers. This paper created a novel algorithm for transit agencies to count passengers with the consideration of transfers using a costeffective Wi-Fi sensing--based approach. Two pilot studies were conducted in the city of Louisville, Kentucky on three different bus routes to explore the feasibility of the method. AWi-Fi detector was installed in the bus to detect passengers, and a manual counting was performed to be used as ground truth data. After data collection, the proposed algorithms were applied to optimize the detection radius and to eventually find origins, destinations, and transfers. Analysis revealed that the proposed Wi-Fi--based approach is capable of recognizing 78.7% of the total passengers as well as detecting their boarding and alighting activities. The paper demonstrates the ability of the proposed method to detect passengers with a reasonable detection rate by using Wi-Fi technology on bus routes, which makes it feasible for transit agencies to conduct frequent and low-cost network-level passenger O-D studies. [ABSTRACT FROM AUTHOR]

The stochastic subspace algorithm is one of the most widely used structural identification techniques, which is generally involved with the stabilization diagram for estimating modal parameters. However, the conventional stabilization diagram has an inherent problem: some spurious modes may be identified as stable results, resulting in adverse effects on structural modal identification. To address this critical issue, this paper proposes an improved stochastic subspace algorithm involving a Monte Carlo–based stabilization diagram. Through a numerical simulation study, the good performance of the Monte Carlo–based stabilization diagram for discriminating the poles denoting the physical modes from those representing spurious modes is demonstrated. The numerical simulation results show that the proposed method can estimate structural modal parameters with high accuracy and robustness. Moreover, the proposed method is applied to field measurements on a 600-m-high skyscraper during Super Typhoon Mangkhut, and the results verify the applicability and effectiveness of the proposed method to field measurements. This paper aims to provide an effective tool for accurate estimation of modal parameters of civil structures. [ABSTRACT FROM AUTHOR]

Durations are defined as an essential component of the three primary characteristics of strong earthquakes, which exhibit a remarkable influence on the damage characteristics of a concrete dam body. However, when considering the distinctive definitions of a strong motion duration, the influence of several durations on the seismic analysis of gravity dams remains unclear. In addition, there is no specific conclusion on the optimal choice for the duration of a strong ground motion. The endurance time analysis (ETA) method, which is a high-efficiency seismic performance method, is widely used, because of the high precision and low computation effort when calculating the dynamic responses of structures. However, when using this method it is almost impossible to account for any one type of duration contribution. Therefore, a modified ETA algorithm to correct duration effects will be proposed in this paper based on the three existing types of durations, such as bracketed (Tb), uniform (Tu), and significant (Ts) durations. The dynamic response will be further corrected using a modified ETA method in the numerical application to the Koyna concrete gravity dam, which is one of the largest dams in Maharashtra state, India. A group of 20 endurance time acceleration functions (ETAFs) and 10 sets of two-dimensional (2D) artificial waves with three total durations will be synthesized through the same site design acceleration spectrum. Three types of indices that include the dissipation energy, damage, and deformation indices will be used to measure the seismic performance. The nonlinear analysis results will be compared with those from the ETA and quantitative analysis methods, which show that the modified ETA method could provide an effective estimation to monitor earthquake resistance and structural seismic performance with a certain safety margin. The influence of the duration of a strong ground motion could be considered in the modified ETA method, which could be further applied in practical engineering. [ABSTRACT FROM AUTHOR]

An auxiliary through lane (ATL) is a commonly used congestion relief measure at signalized intersections in which fiscal and right-of-way constraints do not allow for full widening between intersections. Previously developed models have predicted the flow in the ATL as a function of macroscopic elements, such as through-movement demand and the ratio of average green time to cycle length. However, these models explain neither driver behavior nor motivation to use the ATL, nor can such an approach be used in microscopic simulation models. This paper presents empirically developed models for driver lane choice at signalized intersection approaches with one ATL and one continuous through lane (CTL). These models were developed from a calibration data set that covers eight ATL approaches in three U.S. states, for a total of 12 h of observation. The results suggest that the utility of the ATL is a function of each through-movement driver's arrival time (during either the effective red or green phase), and the queue lengths in either lane at the time of arrival. After calibration, the models were evaluated by applying them to data from a validation site from outside the calibration data set. The paper concludes with a discussion of how the models should be implemented within the lane-change algorithms of microsimulation systems. [ABSTRACT FROM AUTHOR]

The paper proposes an improved single-step method of explicit displacement and velocity (SSMEDV) with controllable numerical damping based on the discrete control theory, which can be applied to real-time hybrid simulation (RTHS). The stability, overshoot, and numerical damping characteristics of the proposed algorithms are studied. It is shown that certain algorithms have unconditional stability for linear and softening nonlinear structures. The overshoot phenomenon is small, where the displacement is one power of the time step due to the initial velocity, whereas the velocity does not. The amount of numerical damping is adjusted by a single parameter to control the divergence of false higher-order modes. The analysis of RTHS was performed on a multiple degrees-of-freedom (MDOF) structure and complex building–damper structure to verify the theoretical analysis. The comparison with two typical algorithms of the explicit Newmark and Gui- λ demonstrates the effectiveness of the proposed algorithms in improving stability, accuracy, and computational efficiency, which can be applied to complex RTHS. [ABSTRACT FROM AUTHOR]

Detecting small leaks in time can prevent an accident from getting worse, which plays an extremely important role in safe pipeline transportation. In order to improve the ability to detect small leaks, this paper proposes a new pipeline leak detection method based on the combination of the improved fast differential algorithm and integral algorithm. This paper first reviews and analyzes the advantages and disadvantages of the fast differential algorithm; on this basis, to further improve the signal-to-noise ratio (SNR) of small leaks and the robustness of the leakage monitoring system, an integral algorithm with adaptive initialization is proposed; Next, to improve the location accuracy of small leaks, historical data are used to estimate the wave speed. The application of actual leakage test data shows that the proposed method can detect a leakage rate of about 0.3%; the false alarm rate is significantly reduced, and the positioning accuracy is improved. All of those verify the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) can facilitate effective three-dimensional (3D) earthwork modeling by furnishing insightful information. An earthwork area is generally represented in a cell-based environment for planning purposes such as allocation plans or equipment plans. However, previous studies utilized conventional methods, which are tedious and time-consuming, to create cell-based representations. Therefore, a method that can be applied to automatically represent earthwork BIM models in a cell-based environment should be developed. To address that research gap, this paper proposes a novel method to develop voxel-based representations of earthwork models. The voxel-based method is parametric, and the size, number, and properties of the voxels can be easily varied. This method, validated for accuracy, rapidly creates a parametric voxel model linked with geotechnical information necessary for earthwork operations. A visual programming tool, Grasshopper, is used to develop an algorithm that can automatically divide the earthwork model into voxels. Finally, experiments are conducted to validate the proposed method using an actual earthwork BIM design. The paper contributes to the existing body of knowledge by proposing a voxel-based earthwork representation and algorithm that automatically create a cell-based 3D environment that is flexible enough to integrate geotechnical parameters. The results indicate that the proposed method will help project engineers, planners, and managers create an optimal-size voxel-based earthwork model with customized geotechnical information. [ABSTRACT FROM AUTHOR]

The paper proposes an interactive algorithm for a three-dimensional (3D) surface that can substantially improve its cracking detection performance by adjustments based on the observer's feedback. Since the correct response to the observer's feedback is critical to the performance of an interactive algorithm, the paper presents a series of novel methodologies to accomplish the interaction between the detection system and the observer with a high level of success. A computer program is developed to establish the interactive detection system and implement two levels of detection. The bottom level is automated and can be used to find the majority of cracking. The top level is assisted by the users and can be applied for semi-automated refinement. In particular, the top level is designated to find missed cracks or delete noises within user-defined regions. The techniques of precision recall and confusion matrix are used to determine effectiveness of the developed system of algorithms. The results from the case study on a highly cracked pavement segment indicate that the automated level of the proposed interactive system can achieve high detection accuracy with appropriate training, and the assisted level can retrieve nearly all cracks missed by the automated level and delete almost all noises from the processing using the automated level. [ABSTRACT FROM AUTHOR]

Due to its complex geometry and mechanical behavior, the prestress design of the cable–pylon anchorage zone is difficult to achieve but critical. In this paper, the use of the strut-and-tie model (STM) is evaluated to simplify the cable–pylon anchorage zone design. The traditional Evolutionary Structural Optimization (ESO) method is first used to generate the STM of a common one-box two-cell cable–pylon anchorage zone. However, the resulting STM is shown to be irrational by comparison with the real distribution of principal stress in the cable–pylon anchorage zone. Therefore, this paper proposes a tailored ESO method using a modified algorithm for the removal criterion based on the strain energy of the element. The proposed method is shown to overcome the drawback of the classical ESO method that some elements playing key roles in the distribution of principal stress, but having small von Mises stresses, may be deleted mistakenly. The prestress design of the Cao'e River Bridge is then presented using the correct STM generated by the tailored ESO method as a practical example. Finally, the rationality of the resulting prestress design of the cable–pylon anchorage zone is substantiated using the results of a full-scale model test. [ABSTRACT FROM AUTHOR]

For a pilot, the angle of attack (AoA) estimation may represent a possibility to cross-check the traditional cockpit indicators and also to obtain information whose benefits are widely recognized in many inflight situations. This paper proposes an AoA algorithm—named ACF—capable of detecting fast changes in the actual signal, that is a useful stall prevention capability, for example. The solution is based on the complementary filtering methodology and does not involve any airdata sensor output, with consequent saving of weight and cost reduction. In particular, it does not use the airspeed measurement, easily subject to failure and has been identified as the root cause of several fatal accidents. Consequently, the ACF shows the same (high) reliability as the used inertial sensors. Furthermore, the ACF algorithm requires neither information on the aircraft aerodynamic coefficients nor forecast wind information to be preloaded onboard, thus being time and cost effective. The ACF is also characterized by a low computational load, only needing a few dozen of algebraic operations. In this work, the ACF performances have been assessed using both a detailed simulation environment and real flight data. In the former case, an airliner following a sample maneuver is assumed, and a mean root mean square (RMS) less than 3° is guaranteed under all possible conditions of wind, gust, and turbulence. In the latter case, data collected in two different general aviation flights are used, providing a mean RMS of less than 0.3°. During piloting, the angle of attack (AoA) readings may represent a possibility to cross-check the traditional indicators and to obtain information whose benefits are recognized in many inflight situations. Unfortunately, the AoA sensors are exposed to several possible failures, often common to the other airdata such as airspeed and altitude. In addition, especially for small aerial vehicles, the systems cannot accommodate direct sensors due to size, weight, and power constraints. For these reasons, having an AoA sensorless estimation would be extremely important. A software solution (ACF) is proposed to estimate the AoA without using any external sensor, saving weight and costs, and with a strong improvement of reliability since the angle vanes—and especially the air probes—are generally identified as the root cause of several inflight accidents. Due to the fact that the ACF algorithm relies on inertial measurements only, it could even be implemented in a pilot's personal device because it does not require any tuning or flight calibration. Furthermore, in spite of its simplicity, it can achieve good accuracy by detecting—in any weather and regardless of particular airplane or maneuver—fast changes in the actual AoA that may endanger the safety of the flight. [ABSTRACT FROM AUTHOR]

This paper focuses on the track-to-object association problem based on the two-line element (TLE) set. A five-step track-to-object association algorithm is designed for the radar observation data. First, for all objects in the TLE set, their orbits are propagated and stored with a step of 10 min. Then the orbital position at an arbitrary epoch can be obtained through the interpolation algorithms. Second, for tracklets longer than 60 s, the extended Laplace method is implemented to determine the orbital elements. Third, the semimajor axis, eccentricity, inclination, and the right ascension of the ascending node are used to filter the TLE sets. More than 90% of the objects can be filtered, improving the association efficiency significantly. Fourth, a first-order association process is implemented to get candidate objects, with the difference between the observed positions and the TLE orbit positions as a metric. Finally, considering the measurement error and the TLE orbit uncertainty, an uncertainty-based final association process checks the candidate objects and gives the final association result. The proposed approach is tested with simulated and observed data. The true-positive rate is higher than 99.5%, while the false-positive rate and the false-negative rate are both lower than 0.1%. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of particle swarm optimization (PSO) to the programming of pavement maintenance activities at the network level. Furthermore, the application of the PSO technique and its relevance to solving the programming problem in a pavement management system (PMS) is discussed. The robustness and quick search capability of PSO enables it to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of PSO in accommodating different forms of objective functions and comparing the results with the genetic algorithm (GA). This paper compares PSO and GA with respect to rate of convergence and accuracy of modeling PMS using an example problem. The results of this paper confirmed the potential of PSO to successfully model the PMS. [ABSTRACT FROM AUTHOR]

Bat algorithm (BA) is one of the well-established metaheuristic algorithms based on the echolocation characteristics of microbats. Literature studies demonstrate that BA provides good results in a wide range of optimization problems due to its simple structures, easy implementation, and effectiveness. However, the standard BA has two general shortcomings when applied to complex optimization problems. The first is the high probability of falling in local optima, and the second is insufficient population diversity, leading to unwanted premature convergence. This paper proposes an improved bat algorithm based on the Doppler effect (IBA-DE) to improve the performance of the standard BA. In the proposed IBA-DE, a new equation with some idealized rules is driven from the Doppler effect and adopted for updating the bat velocities in the algorithm body. The local search part of the standard BA is also improved via searching around the better bat individuals. This part is also equipped with Lévy flight to maintain the population diversity. The performance of IBA-DE is evaluated on three real-world engineering design problems. The results obtained by these problems show that IBA-DE is superior to the basic BA and other considered algorithms. For further investigation, IBA-DE is applied to the optimal design of four special truss structures. Comparing the optimization results found by the proposed algorithm with those of some other state-of-art metaheuristics indicates that the proposed IBA-DE can be an efficient optimizer for the optimal design of real-size structures. [ABSTRACT FROM AUTHOR]

Existing methods for estimating origin–destination (O–D) matrix from link traffic counts cannot consider multiple-vehicle information, which is easily obtained from a field survey. Those methods use a single-vehicle information and it may decrease the performance of an O–D matrix estimator. Up until now, there is little research regarding multiple-vehicle O–D matrix estimation from multiple-vehicle link counts. This paper suggests a multiple-vehicle O–D matrix estimation from traffic counts using a genetic algorithm (GAMUC). From the numerical tests, we found that the performance of GAMUC was better than those of the previous methods for the error of target O–D matrix and for the number of link counts. In this paper, we also found that the method provided good results on the error of target O–D matrix and on the varied O–D matrix structure, which occurred when the target O–D matrix was different from the true one. [ABSTRACT FROM AUTHOR]

In an environment of fierce market competition, companies commonly implement several projects simultaneously and employ programs to manage these multiprojects. The program is scheduled at the tactical layer to coordinate multiple projects, each scheduled independently by its project leader. The discrete time-cost trade-off problem (DTCTP) is one of the most significant project scheduling problems, and numerous studies have been conducted on DTCTP for several decades. However, there is still a notable gap in research on DTCTP in multiproject scenarios. A program is a popular approach for managing multiple projects that are related to each other. This paper proposes a new hierarchical discrete time-cost trade-off problem (HDTCTP) for the program that integrates the scheduling problems at the different layers. We clarify the interactions between different layers in program scheduling and introduce the model of a program cascading scheduling process. The program at the upper layer allocates budgets and targets to projects at the lower layer, where each project is scheduled independently. The scheduling results at the lower layer are fed back to the program for further coordination and optimization. This program iteratively coordinates, redistributes budgets, and assigns targets until the overall optimization is achieved. Then the HDTCTP is described and formulated based on analytical target cascading (ATC). To solve the presented HDTCTP, a top-down iterative algorithm is proposed according to the characteristics of HDTCTP in the frame of ATC. A real-life program case verifies the usability of the presented approach, and the results of experiments show that the presented algorithm based on ATC can efficiently solve the presented HDTCTP for programs. [ABSTRACT FROM AUTHOR]

Only a small minority of road incident detection algorithms (IDAs) have been designed for use in urban road networks. A review of the literature conducted by the authors revealed that approximately 10% of published papers on novel IDAs are designed specifically for urban networks. Urban networks present many challenges that are not faced on highways, such as signalized junctions causing queuing similar to that of an incident. This paper reviews the progress made in urban incident detection research and highlights where improvements are needed. It is found that few algorithms have been implemented or tested on real-world data, and so few have been sufficiently evaluated or compared. Those that have been implemented often find difficulty in differentiating disruption from incidents and contextual factors such as sporting events or public holidays. This has caused unnecessary false alerts to be triggered, leading to dissatisfied operators. Progress could be made if more research data and IDAs were published, allowing for thorough evaluations and comparisons of algorithm performance on realworld data. Further research is required to improve IDAs' ability to differentiate between incidents and contexts, particularly in complex urban networks. [ABSTRACT FROM AUTHOR]

Presents a response to the comments made on an article about an algorithm for hydraulic calculation in irrigation laterals. Problems identified in the article; Types of SBS simulations proposed in the paper; Reason for the disagreement pointed out by the discussers.

This paper proposes an equivalent plane algorithm of spatial stay cables using spatial rigid arms and the stay cable corotational coordinate system. The equivalent equations for the cable displacements and force elements are established, and the equivalent plane-element stiffness matrix of the spatial-cable element and its transfer matrix are derived. The matrix is automatically transformed into the plane-cable element model with rigid arm when the cable plane of the cable-stayed bridge is parallel. The spatial characteristic parameters of the cable plane are proposed, and the spatial effect of the spatial-cable plane on the internal force and deformation of the cable-stayed bridge is also analyzed. This equivalent plane algorithm is programmed into the bridge design and construction monitoring system procedure, and a comparison analysis with MIDAS software is also conducted. The proposed method can simplify the calculation model for the entire construction process of cable-stayed bridges with spatial cables and yield a good calculation accuracy. The equivalent plane algorithm has been successfully applied to the Jiayu Yangtze River Bridge (a kilometer-scale cable-stayed bridge). The results show that the number of stay-cable elements in the calculation model is reduced by 50%. The difference between the proposed method and the calculation result of a spatial-cable plane model is very small for a cable-stayed bridge with a spatial-cable plane, whether the initial tension is used or an unstressed cable is used. The proposed method provides an efficient and reliable calculation method for the design and construction of spatial-cable-stayed bridges. [ABSTRACT FROM AUTHOR]

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers. [ABSTRACT FROM AUTHOR]

In this paper, the problem of longitudinal free vibration of nonuniform rods is investigated for the first time by using the discrete singular convolution (DSC). This method is a relatively novel and powerful numerical approach especially in analyzing vibration problems. The governing equation of this problem is nonlinear and has analytical solutions only in a few cases, so the analysis uses DSC as a numerical method. The regularized Shannon delta kernel is used in the present algorithm. The nondimensional natural frequencies of rods with various-area cross sections have been studied in this paper. Results show good agreement with the previous analytical solutions. This study indicates the efficiency of the DSC in solving nonuniform rod problems. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are generally regarded to behave as black-box systems. Recent research explores various methods that can provide an insight into the internal connections and relationships existing within the network. Various methodologies that understand the input variable contribution are analyzed in detail, and rule extraction approaches for a trained artificial neural network are addressed. To understand the contribution of input variables to rainfall-runoff model combination systems, this paper for the first time investigates knowledge extraction from artificial neural network, which is used to combine the results obtained from different competing rainfall-runoff models, using three different approaches: (1) Garson's algorithm; (2) neural interpretation diagram (NID); and (3) sensitivity analysis (SA). For the purpose of investigating knowledge extraction techniques, the trained multilayer perceptron neural network to combine the results from four different rainfall-runoff models for the Brosna Catchment located in Ireland has been chosen. The results of the three approaches obtained in this study indicate that they can be used to reduce the complexity of rainfall-runoff model combination systems by eliminating the least significant contributing input variables. Based on these approaches, the paper helps to provide guidance in the optimal number of rainfall-runoff models that best perform in a combination system. [ABSTRACT FROM AUTHOR]

A noncrossover dither creeping mutation-based genetic algorithm (CMBGA) for pipe network optimization has been developed and is analyzed in this paper. This CMBGA differs from the classic genetic algorithm (GA) optimization in that it does not utilize the crossover operator; instead, it only uses selection and a proposed dither creeping mutation operator. The creeping mutation rate in the proposed dither creeping mutation operator is randomly generated in a range throughout a GA run, rather than being set to a fixed value. In addition, the dither mutation rate is applied at an individual chromosome level rather than at the generation level. The dither creeping mutation probability is set to take values from a small range that is centered about (where = number of decision variables of the optimization problem being considered). This is motivated by the fact that a mutation probability of approximately previously has been demonstrated to be an effective value and is commonly used for the GA. Two case studies are used to investigate the effectiveness of the proposed CMBGA. An objective of this paper is to compare the performance of the proposed CMBGA with four other GA variants and other published results. The results show that the proposed CMBGA exhibits considerable improvement over the considered GA variants, and comparable performance with respect to other previously published results. Two big advantages of the CMBGA are its simplicity and the fact that it requires the tuning of fewer parameters compared with other GA variants. [ABSTRACT FROM AUTHOR]

This paper reports the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multiobjective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives, as follows: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age, and (6) fire-fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This paper demonstrates that a many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision-making in the WDS design process. [ABSTRACT FROM AUTHOR]

Several state DOTs utilize the statistics presented in the Federal Highway Administration (FHWA)'s version of the traffic monitoring guide (TMG) to conduct weigh-in-motion (WIM) data quality checks for DARWin-ME traffic load spectra data preparation. Because the data check statistics in the guide were originally developed in the early 1990s, there is a need to reexamine its continuing use in up-to-date truck traffic streams. This paper aims to validate the TMG data check algorithms using WIM data from the long-term pavement performance (LTPP) special pavement study (SPS) traffic pooled-fund study. Cluster analysis is performed to identify similar traffic patterns, and the statistical summary for each data check criterion is obtained. Subsequently, the statistics based on the pooled fund data are compared to those recommended in the TMG. The results of the paper conclude that the data check criteria in the TMG guide are still valid for most of the sites; however, users should be alert that the guide also acknowledges WIM stations with exceptional patterns. This study presents a framework to check the suitableness of using the guide for such cases. [ABSTRACT FROM AUTHOR]

The nonlinear Muskingum model is a hydrologic flood-routing method useful when the storage flow relation departs from the classic linear assumption. This paper extends versions of the nonlinear Muskingum model by introducing a parameterized initial storage condition. The extended nonlinear Muskingum values have an increased number of degrees of freedom that allows an enhanced capacity to accurately predict outflow hydrographs provided that parameter estimation is optimized as proposed in this work. The parameters of the nonlinear Muskingum models are estimated with the weed optimization algorithm (WOA), and the excellent performance of the extended nonlinear Muskingum models is demonstrated with several types of hydrographs using several criteria of statistical efficiency. The implementation results show that the nonlinear Muskingum model's predictions outperform those of the best results reported with other routing models for the examples presented in this paper. [ABSTRACT FROM AUTHOR]

Underfunding and the continuous deterioration of pavement assets have been among the major challenges for transportation agencies in the US. These challenges motivate the use of different optimization techniques such as integer programming to perform maintenance and rehabilitation planning at the network level. However, one of integer programming's limitations is the massive computational power requirement associated with the large number of solutions searched by the algorithm to get the optimal solution, limiting the size of the network it can optimize. In this paper, the full mathematical derivation of the binary integer programming problem is shown in terms of budget constraints only and in terms of both budget and percentage of poor assets (PPA) constraints. An alternative approach including the PPA constraint is therefore suggested along with a new practical way of reducing the search space based on the statistical bootstrap approach. The proposed approach's adequacy is validated through a comparison of different optimization approaches applied to a small asphalt concrete (AC) pavement network. The suggested approach shows promising results because it significantly reduced the time required to allocate budget funds over the test network of 400 asphalt concrete (AC) segments. [ABSTRACT FROM AUTHOR]

Predictions of fundamental traffic variables in the short-term or near-term future are vital for any successful dynamic traffic management application. Univariate short-term traffic flow prediction algorithms are popular in literature. However, to facilitate the operationalities of advanced adaptive traffic management systems, there is a necessity of developing multivariate traffic condition prediction algorithms. A new multivariate short-term traffic flow and speed prediction methodology is proposed in this paper where the traffic flow and speed observations from uncongested (or linear) and congested (or nonlinear) regimes are regime-adjusted to ensure consistent system dynamics. The prediction methodology is developed by using artificial neural networks (ANN) algorithms in conjunction with adaptive learning rules. These learning rules demonstrate significantly improved accuracy and simultaneous reduction in computation times. Additionally, the paper attempts to identify the most suitable adaptive learning rule from a chosen pool of rules. The validation of the prediction methodology is performed by using traffic data from multiple locations in the United Kingdom (U.K.). The results indicate that the proposed multivariate forecasting algorithm is effective and computationally parsimonious to simultaneously predict traffic flow and speed in freeway or highway networks. [ABSTRACT FROM AUTHOR]

Transport networks in many cities are generally very seriously congested. Consequently, travel time, number of stops, unexpected delays, transport costs, level of air pollution, noise, and traffic accidents are increased. In addition to daily congestion, there may be congestion and traffic jams as a consequence of reconstruction of the roads' lanes. During the past decade, different strategies for transport demand management have been developed, with the aim to decrease existing traffic congestion. One of the available strategies is a ride matching (sharing) concept. This strategy means that several participants share only one private car when traveling from origin to destination. A model for grouping drivers into cars for ride matching, according to similarities of a place of living and working, working hours, and type of car license plate, is developed in this paper. The writers apply the ride matching concept on congested traffic during the reconstruction of the main bridge in Belgrade, Serbia, which is the Gazela Bridge. The basis of the proposed model is the K-means method and even-odd concept. To solve the ride matching problem, the writers present improvement of the traditional K-means algorithm, in which the numbers of centers is unknown in advance and the number of clusters may not be the same as the number of centers. This modification of the traditional K-means algorithm, called advanced K-means, for solving a specific ride matching problem is the main contribution of the paper. [ABSTRACT FROM AUTHOR]

Structure-dependent integration algorithms have been implemented for pseudodynamic testing because they are very promising for this type of test due to the combination of unconditional stability and explicit formulation simultaneously. Although their numerical properties have been assessed and their feasibility for pseudodynamic tests has been corroborated, the choice of an appropriate structure-dependent pseudodynamic algorithm might be a difficult task. This is because some undiscovered, adverse properties were found for specific structure-dependent algorithms, although their basic properties have been well explored. Because the adverse properties are not well disclosed for these structure-dependent pseudodynamic algorithms, they might lead to inaccurate test results. This paper summarized both the favorable and adverse numerical properties of the pseudodynamic algorithms so that appropriate pseudodynamic algorithms can be identified. This paper assessed and compared most structure-dependent pseudodynamic algorithms, and recommended some structure-dependent pseudodynamic algorithms for engineering practice. Both recommended nondissipative and dissipative structure-dependent algorithms are very suitable for pseudodynamic tests because they possess favorable properties but exhibit no adverse properties. It was also substantiated that numerical damping cannot be used to overcome an overshoot caused by weak instability. [ABSTRACT FROM AUTHOR]

Evaluating the statistical moments of performance functions from the perspective of balancing accuracy and efficiency remains a challenge. This paper proposes an efficient algorithm for evaluating the statistical moments of performance functions. The main procedure of the proposed method consists of three steps. First, based on the bivariate dimension-reduction method, the performance function is approximated by a summation of one-dimensional and two-dimensional functions. Next, according to the criterion of delineating the existence of cross terms, the two-dimensional functions are decomposed as functions including and excluding cross terms. Third, the one-dimensional point estimate method is used to evaluate the statistical moments of the one-dimensional functions and the two-dimensional functions without cross terms, whereas the two-dimensional sparse grid stochastic collocation method is applied to estimate the statistical moments of the two-dimensional functions with cross terms. Several numerical examples are presented to illustrate the efficiency, accuracy, and applicability of the proposed method. The results demonstrate that the proposed method achieves a good balance between accuracy and efficiency and provides a useful tool for evaluating the statistical moments of performance functions. [ABSTRACT FROM AUTHOR]

This paper presents a detailed numerical algorithm for time-domain flutter analysis of elongated bridges, emphasizing in particular some problematic issues with regard to the application of the indicial functions involved wherein. Some typical characteristics of the indicial functions adopted early in airfoil aeronautics and those in bridge aerodynamics are first reviewed. The paper then presents the indicial-function-expressed aerodynamic forces and the recursive functions involved in the integral of the memorial terms in them. The theoretical description and numerical results indicate that the aeroelastic forces expressed with indicial functions are, if the model parameters are well identified, equivalent to those with flutter derivatives. However, this is merely an equivalence of frequency spectrum, and the transient characteristics of the indicial functions so identified, therefore, may not be physically true. This may result in excessively distorted aeroelastic forces and extraordinary time-consuming attenuation of the incorrect transient responses. To avoid such problems, a methodology of linear searching of the indicial-function parameters in predefined ranges is developed, on the basis of which the influence of some important factors, such as the time step size and numerical ranges of model parameters, are investigated. Numerical results show that large time step size may induce nonnegligible additional phase differences between the simulated aeroelastic forces and the structural motions and thus affect the total work accumulation and, consequently, the flutter threshold. Therefore, care should be taken in selecting an appropriate time integral step size. [ABSTRACT FROM AUTHOR]

Considerable research has been carried out on the optimization of water distribution systems (WDSs) over the last three decades. In previous research, attention has mainly focused on the minimization of cost, due to the high expenditure associated with the construction and maintenance of such systems. However, the impacts of WDSs on the environment usually have not been considered adequately. The recent increasing awareness of sustainability and climate change, especially global warming, has led to research where greenhouse gas (GHG) emissions are considered. In the study described in this paper a multiobjective genetic algorithm for WDS optimization has been used as an explorative tool to investigate the trade-offs between the traditional economic objective of minimizing costs and an additional environmental objective of minimizing GHG emissions. The impacts of minimizing GHG emissions on the results of WDS optimization have been explored for a case study in this paper. The results indicate that the inclusion of GHG emission minimization as one of the objectives results in significant trade-offs between the economic and environmental objectives. Furthermore, a sensitivity analysis has been conducted by using different discount rates in a present value analysis for computing both ongoing costs and GHG emissions. The results obtained show that the Pareto-optimal front is very sensitive to the discount rates used. As a result, the selection of discount rates has a significant impact on final decision making. [ABSTRACT FROM AUTHOR]

The importance of considering three-dimensional (3D) sight distance in geometric design has been demonstrated by several researchers. However, little progress has been made in the use of 3D analysis in the design of highways. This can be mainly attributed to the complexity of the computations required for 3D analysis. This paper presents a new algorithm for calculating the 3D sight distance. The algorithm is considered more efficient, less computationally intensive, and more flexible than previously developed approaches. The algorithm is based on a parametric representation of the roadway and roadside features without any implicit approximation of the roadway surface. The paper presents analysis results of various alignment configurations to demonstrate the use of the algorithm and to examine the influence of different geometric elements on the 3D sight distance. The paper also provides an analytical tool that can investigate and determine the conditions under which a 3D analysis is considered necessary. [ABSTRACT FROM AUTHOR]

In view of the importance of percolation of the interfacial transition zones (ITZs) in concrete to its transport properties, it is necessary to determine the ITZ percolation threshold for any aggregate gradation and ITZ thickness. This paper presents a numerical method for predicting the ITZ percolation threshold of concrete. To represent the mesostructure of concrete as realistically as possible, a simulation algorithm for the distribution of aggregate particles and ITZs in concrete with periodic boundary conditions is proposed. By analyzing and studying the numerical results, it is shown in this paper that, for a given aggregate volume fraction and element size, the ITZ percolation probability tends to be a constant for a sufficient number of simulations and that the slope of the ITZ percolation probability curve becomes vertical as the element size increases. Finally the results produced by the proposed numerical method are verified by experimental results obtained from the research literature. Based on the results presented in this paper, the effects of the ITZ thickness, the maximum aggregate diameter, and the aggregate gradation on the ITZ percolation threshold are evaluated in a quantitative manner. It can be concluded that the numerical method developed can be used to predict the ITZ percolation threshold with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Previous research on canal automation has dealt with the control of single, in-line canals, while canal operators typically have to control an entire network of canals. Because the branches in a network are hydraulically coupled with each other, control of a branching canal network based on separate controllers for each branch may not be the most effective control strategy. A methodology by which existing automatic control systems could be modified to control branching canal networks is provided in a companion paper. This paper presents results of hydraulic simulations of the new methodology to estimate the controllability of a large portion of the branching canal network operated by the Salt River Project (SRP). Two types of controllers were used for this study: (1) linear quadratic regulator (LQR) and (2) model predictive control (MPC). Both controllers used the same underlying process model [integrator-delay (ID) model], and both controllers were capable of feedback and feedforward control. Under feedback control alone, both controllers gave similar performance, but were unable to effectively control the overall system because of the long delay times. When feedforward control was added to the feedback controller, both of these control systems were able to effectively control the branching canal network operated by SRP. For the LQR controller, the volume compensation method for routing known demand change was used as the feedforward controller. For the MPC controller, the ID model was used as the feedforward controller. Slight differences were noted between the performance of the two feedforward controllers. [ABSTRACT FROM AUTHOR]

This paper shows the iterative approach to solving transit network design problem, particularly with variable transit demand under a given fixed total demand. Although recent studies, which use a simplified combinatorial search approach, showed their capability of building optimal transit networks and handling the complicated transit travel time characteristics, only this iterative approach is believed to properly handle the dynamic characteristics of the relationship between variable transit trip demand and optimal transit network design. Since transit demand depends on the configuration of the transit network and frequencies of the routes, this approach is more desirable for transit network planning than combinatorial approach. The basic approach generates the optimal transit network from the initial network, which requires the shortest in-vehicle travel time, through iterating the assignment procedure and the improvement procedure until there is no more improvement in the network. With variable transit demand, the modal split procedure is added to the basic model to generate the optimal transit network and to estimate transit demand simultaneously. This paper also shows the relationship between optimal transit network design and critical design inputs, such as transit operating speed, total demand size, and transfer penalty. As results of the analysis, synergistic effect of variable transit demand and the optimal transit network are discussed. [ABSTRACT FROM AUTHOR]

Superblocks are city blocks whose size is significantly larger than average for a city block. They are considered to be close if they only have a few gates connecting the pedestrian and car traffic inside with that outside of the block. The gate setting of superblocks, namely the number and location of gates, despite playing a very important role in the overall traffic performance, has attracted limited attention in research. This paper narrows this gap by proposing a bilevel optimization model to calculate the optimal gate setting for superblocks. The lower-level model involves the traffic assignment, paying attention to travelers' mode and route choice behavior. The upper-level model computes the optimal number and location of gates to minimize the total cost, considering both travelers' cost and infrastructure cost. To solve this model efficiently, we also develop a solution algorithm, whose output is the optimized gate setting. A case study is used to illustrate the impact of the gate-setting problem on the performance of the transportation network, and the applicability of the proposed algorithm. Results indicate that the number and location of gates significantly impact traffic performance. For example, it is possible to have a lower cost with three gates that are well located than with nine gates that are poorly located. Furthermore, for the specific network studied, the optimal gate setting solution reduces the total cost by 17% and leads to a 3% mode shift from private car to metro when compared with the existing conditions. [ABSTRACT FROM AUTHOR]

A standard interchangeable data format was recently developed through the Transportation Pooled-Fund Study TPF-5(299) to meet different requirements and needs for two- and three-dimensional (2D/3D) pavement image data storage and exchange. Considering pavement images occupy large amounts of storage space, efficient compression algorithms for both 8-bit 2D and higher-bit 3D images become one of the key components for a successful implementation of the standard. Highway agencies and industry vendors exclusively use the Joint Photographic Experts Group (JPEG) or its successor JPEG 2000 standard for 2D image compression, while the compression of 3D 16-bit images remains to be proprietary. The goal of this paper is to evaluate suitable algorithms for compressing 3D pavement images. An overview of the state-of-the-art pavement image data collection technologies was performed, and the relevant image data formats were identified. Subsequently, five compression algorithms suitable for 16-bit image data were selected based on results from a comprehensive survey of the TPF-5(299) technical community. The compressions of several representative field images were implemented and evaluated in terms of the computation efficiency and retaining of image quality. The expected benefits of this study include facilitating standard and efficient pavement image data collection and accelerating the development of consistent pavement condition evaluation. [ABSTRACT FROM AUTHOR]