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The effects of global climate change are already observed in coastal communities, and one of the key impacts of climate change is sea level rise (SLR). Such increases in sea level are expected to have large impacts on transportation infrastructure and result in region-wide commute disruption. Studies and adaptation strategies in coastal areas will allow decision-makers to take better actions against SLR. The present paper uses the San Francisco Bay Area as a case study. The study quantifies the interregional traffic delay of commuters due to the inundation of shoreline protection segments of the Bay Area. The approach is based on the marginal effect of protection within San Francisco Bay where individual stretches of coastline known as operational landscape units (OLUs) are protected one at a time. In the study, we integrate detailed shoreline scenarios, coastal inundation modeling, and traffic disruption modeling. Important insights from the analysis are obtained: the protection of an OLU (and specifically San Rafael OLU) will produce an average commute time reduction of up to 17% in the neighboring areas or the neighborhoods located across the bay. Five OLUs along with four bridges were identified as critical for minimizing disruptions to commute times. This methodology may be used by decision-makers when proposing adaptation and protection strategies that account for both local and regional areas. [ABSTRACT FROM AUTHOR]

Climate change places additional stress on critical infrastructure systems as demand for resources (e.g., water and electricity) increases and environmental disruptions (e.g., flooding and wildfires) become more frequent and severe. Interconnected infrastructure systems may be particularly vulnerable, as disruptions in one system can cascade to other systems and increase the severity of impacts. To ensure continued functionality, infrastructure systems must be designed or adapted to account for changing environmental conditions, with consideration for the interactions between systems. For this study, we examine the dependencies between shoreline protective infrastructure and transportation infrastructure in the context of sea level rise in the San Francisco Bay Area. Shoreline modifications implemented in one location can cause hydrodynamic feedbacks that exacerbate flooding and associated disruptions elsewhere. On the other hand, the decision not to implement shoreline protection can cause local flooding of roadways that leads to traffic feedbacks and system‐wide travel delays. We compare the magnitude of these feedbacks, in terms of vehicle hours traveled (VHT), across a range of county‐level shoreline scenarios to characterize the effects of one county's shoreline adaptation decisions on its neighbors. Our results show that VHT increases by as much as 7.2% due to hydrodynamic feedbacks and 10.7% due to traffic feedbacks. Comparing these effects for each scenario allows for targeted decision‐making about adaptation approaches that account for both the local effects of flood disruptions and the regional network effects driven by infrastructure interactions. Plain Language Summary: As sea levels rise, coastal communities are considering how to adapt their shorelines to protect themselves from flooding. When one community builds a seawall to protect itself, it may cause increased flooding for its neighbors. On the other hand, if communities do not take action to protect their shorelines, flooding of critical infrastructure systems such as roadways will occur, forcing drivers to find alternate routes. This can lead to longer travel times locally, for drivers who typically use the now‐flooded roads, but also regionally, as these drivers are forced onto other roads that may not have enough capacity to support the higher traffic demand, thus causing additional congestion. In this analysis, we apply a set of computational models to examine the impact of shoreline protection strategies on travel times in the San Francisco Bay Area. We find that travel times can increase by as much as 10.7% for the scenarios considered here, leading to longer delays for commuters. Key Points: Sea level rise leads to dependencies between shoreline protective infrastructure and transportation infrastructure in coastal regionsDecisions about shoreline adaptation lead to hydrodynamic and traffic feedbacks, which both contribute to travel delaysComparing these feedbacks allows for targeted assessment of the local and regional effects of proposed shoreline adaptation scenarios [ABSTRACT FROM AUTHOR]

There is an increasing need for the reduction of greenhouse gas (GHG) emissions within pavement management decisionmaking. Pavement maintenance activities, such as resurfacing, account for millions of tons of the GHG emissions annually in the United States. Optimizing pavement resurfacing activities allows for the potential to reduce the carbon footprint of pavement maintenance. A framework is proposed for estimating the relationship between GHG emissions from pavement resurfacing activities and pavement cracking-threshold policies, where cracking is the trigger distress. Cracking threshold is the maximum percentage cracking level a pavement is allowed to reach before an asphalt overlay is applied. The data set used in the case study was obtained from the Washington State Department of Transportation. The results show that for a planning horizon of 10 years, the optimal cracking thresholds for minimizing costs and GHG emissions are very close to each other. [ABSTRACT FROM AUTHOR]

In infrastructure management, the slow accumulation of facility condition data and incomplete understanding of the deterioration process can lead to inaccurate deterioration models and subsequently, suboptimal maintenance, rehabilitation and reconstruction decisions and increased system costs. State-of-the-art bridge management systems (such as Pontis) address this problem by successively updating deterioration models over time, by using a class of adaptive control methods known as the certainty-equivalent control (CEC). In this paper, we demonstrate that CEC does not necessarily guarantee improvement in deterioration model accuracy or savings in system costs for realistic heterogeneous bridge systems undergoing non-Markovian deterioration. We further apply an alternative, the open-loop feedback control (OLFC), and demonstrate its superiority to CEC in a simulation study, in which we consider a planning agency managing a system of facilities with limited prior knowledge of the deterioration models over a designated planning horizon. We show that OLFC improves model accuracy and reduces system costs. We focus our discussion on bridge decks, the component of bridge structures that undergoes the fastest deterioration, but the methodology presented in this paper is applicable to all bridge components. [ABSTRACT FROM AUTHOR]

Bridge management problems are computationally difficult to solve when considering network connectivity, because all bridges must be analyzed together. Past research has usually adopted simulation-based numerical methods, which are inaccurate and time-consuming. In this paper, an analytical and efficient approach is presented to determine the optimal maintenance, rehabilitation, and replacement (MR&R) plan. The objective is to ensure an adequate level of network reliability at the lowest possible life-cycle maintenance cost. Instead of considering the evolution of network reliability over the whole life cycle, the optimization is formulated with a constraint on the lower bound of network reliability, which reduces the network-level problem to one of optimizing the set of reliability levels for individual bridges. For simple decomposable networks, the network-level optimization can be tackled with standard techniques in polynomial time. For complex networks that are nondecomposable, an algorithm is provided to transform the nonlinear integer programming problem into binary integer linear programming. Finally, the framework is extended to networks with multiple origin-destination pairs. The contribution of this paper is a novel approach to solve the network connectivity problem, which makes it possible to use standard optimization tools (with guaranteed convergence to optimality), as opposed to the heuristic algorithms used in related literature. [ABSTRACT FROM AUTHOR]

Development of deterioration models for pavements is an essential part of maintenance and rehabilitation planning. Often, when pavement-deterioration models are developed for developing countries or states that do not have regularly scheduled condition surveys, the only available data are experimental. Experimental data sets fail to include environmental variables and cannot capture properly the aging process of pavements, so the estimated models suffer from biases. This paper describes the development of a pavement-crack-initiation model by combining experimental and field data to correct for such biases. The American Association of State Highway Officials (AASHO) Road Test is used as the experimental data set, which is combined with field data from the Washington State Department of Transportation. The two models are first estimated separately, correcting for endogeneity biases that may exist in the field model. Joint estimation is used next to quantify the bias in the experimental data set and estimate the parameters of the combined model. This study shows that joint estimation can lead to more robust crack-initiation models compared to those estimated separately by the two data sets. [ABSTRACT FROM AUTHOR]

Research has separately existed for life-cycle costs and environmental assessment of photovoltaic systems. This study provides a framework for the identification of the optimal set of residential photovoltaic system options in terms of costs and selected air emissions by assessing the life cycle of the product from manufacturing to disposal. Analyzed air emissions include carbon, nitrogen oxides, sulfur oxides, carbon monoxide, and particulate matter. The study focuses on general residential installations within the San Francisco Bay Area of California. Pareto frontiers are determined for costs and emissions, and the best options for suppliers are identified in each case, including sensitivity analysis of the installation planning horizon. Choosing multicrystalline silicon modules over thin film modules has the greatest impact on cost reduction. The electricity mix with which the modules are produced has the greatest impact on the emissions. [ABSTRACT FROM AUTHOR]

In recent decades, pavement management optimization has been designed with the objective of minimizing user and agency costs. However, recent analyses indicate that pavement management decisions also have significant impacts on life-cycle greenhouse gas (GHG) emissions. This study expands beyond minimization of life-cycle costs to also include GHG emissions. Previous work on the single-facility, continuous-state, continuous-time optimal pavement resurfacing problem is extended to solve the multicriteria optimization problem with the two objectives of minimizing costs and GHG emissions. Results indicate that there is a trade-off between costs and emissions when developing a pavement resurfacing policy, providing a range of GHG emissions reduction cost-effectiveness options. Case studies for an arterial and a major highway are presented to highlight the contrast between policy decisions for various pavement and vehicle technologies. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for maintenance optimisation for heterogeneous infrastructure systems, i.e., systems composed of multiple facilities with different characteristics such as environments, materials, and deterioration processes. We present a bottom-up approach: facility-level optimal maintenance policies are first found; these policies are then combined with budget constraints in the system-level optimisation. In the first step, optimal and near-optimal maintenance policies for each facility are found and used as inputs for the system-level optimisation. In the second step, the problem is formulated as a constrained combinatorial optimisation problem, where the best combination of facility-level optimal and near-optimal solutions is identified. Two heuristics, pattern search heuristic (PSH) and evolutionary algorithm (EA), are adopted to solve the combinatorial optimisation problem. Their performance is evaluated using a hypothetical system of pavement sections. Comparison result with real optimal solutions for 20 facilities showed that both algorithms give near-optimal solutions (within less than 0.1% difference from the optimal solution) in 978 (PSH) and 966 (EA) cases out of 1000 executions. The EA performs better in terms of processing time than the PSH. Numerical experiments show the potential of the proposed algorithms to solve the maintenance optimisation problem for realistic heterogeneous systems. [ABSTRACT FROM AUTHOR]

System-level infrastructure management involves determining optimal maintenance, rehabilitation, and reconstruction activities for a system of facilities over a planning horizon to maximize the level of service subject to system constraints. In railway networks, different types of facilities (such as tracks, and bridges) comprise the system, and maintenance of one facility may affect the maintenance of other facilities, both economically and functionally. Therefore, when developing an asset management system for railway networks, it is essential to take into account heterogeneity and interdependency in the network. In this paper, a two-stage, bottom-up methodology is formulated to capture these interdependencies and heterogeneities in railroad infrastructure management. Numerical experiments are presented to examine the effects of economic and functional interdependencies on network optimization and to demonstrate the validity of the proposed model. The results show that accounting for heterogeneity and interdependencies at the system level can greatly influence the choice of activities and the estimated life-cycle cost. [ABSTRACT FROM AUTHOR]

This paper presents an approach to address pavement management decision problems at airports with multiple runways. It relaxes some of the underlying assumptions made in previous studies and explicitly considers time requirements for runway reconstruction, deterioration dependence on traffic levels, and the growth of traffic demand over time. A finite-horizon dynamic program is formulated to investigate the interplays among maintenance and repair (M&R) action time, functional interdependence between runways, and traffic growth. Results from computational studies reveal these interplays, in particular the tradeoff between present M&R action and delay cost and long-term benefits brought by significantly upgrading pavement conditions through reconstructing runways. Sensitivity analyses suggest that baseline demand, demand growth rate, and the parameter differentiating traffic-dependent transition probabilities significantly affect optimal M&R decisions and total expected cost to go. [ABSTRACT FROM AUTHOR]

Research has separately existed for life-cycle costs and environmental assessment of photovoltaic systems. This study provides a framework for the identification of the optimal set of residential photovoltaic system options in terms of costs and selected air emissions by assessing the life cycle of the product from manufacturing to disposal. Analyzed air emissions include carbon, nitrogen oxides, sulfur oxides, carbon monoxide, and particulate matter. The study focuses on general residential installations within the San Francisco Bay Area of California. Pareto frontiers are determined for costs and emissions, and the best options for suppliers are identified in each case, including sensitivity analysis of the installation planning horizon. Choosing multicrystalline silicon modules over thin film modules has the greatest impact on cost reduction. The electricity mix with which the modules are produced has the greatest impact on the emissions. [ABSTRACT FROM AUTHOR]

Rebuilding and maintaining the nation's highway infrastructure will require very large capital outlays for many years to come. While the expenditures involved in the maintenance and construction of highway facilities are large, current methods of pavement design used in common engineering practice do not routinely take advantage of design optimization methodologies. This paper presents an optimization formulation for mechanistic-empirical pavement design that minimizes life-cycle costs associated with the construction and maintenance of flexible pavements. Sensitivity analysis is performed on the model to understand how the optimal design changes with respect to variations in the critical design inputs. Using typical values for the costs associated with the construction of each pavement layer and the reconstruction of failed pavement sections, it is determined that extended-life flexible pavements may provide significant life-cycle cost savings despite their higher initial construction cost. However, perpetual pavements that control critical strains to levels near the fatigue and endurance limits for the hot mix asphalt (HMA) and subgrade soil should be designed only when traffic levels are sufficiently high to warrant them or when sufficient uncertainty exists in the mean values of design input probability distributions. Optimization studies performed under uncertainty have showed that designs for extended-life pavements are robust with respect to physical variability in material properties, but are significantly impacted by a lack of knowledge of probability distributions. [ABSTRACT FROM AUTHOR]

We present the development of an empirical pavement overlay crack progression model using condition survey data for the highway system in the state of Washington. The crack progression model uses random-effect panel data regression techniques, with correction for incidental truncation, endogeneity bias, and unobserved heterogeneity. The parameter estimation results show that existing cracking prior to the overlay, traffic loading, overlay thickness and materials, and some environmental factors play important roles in explaining crack progression. The model developed in this paper is used jointly with a probabilistic model of overlay crack initiation as inputs to a Monte Carlo simulation of overlay cracking trends over time. These trends are realistic for overlays in the estimation database, but caution should be used when applying the model to other states. Specifically, it should only be used in situations where the maintenance policy used is similar to that used by the Washington DOT. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of highway maintenance marginal costs. Highway maintenance marginal cost has been estimated in the literature using the perpetual overlay indirect approach. This approach assumes that pavement overlay costs dominate maintenance costs and ignores other maintenance activities. This paper focuses on two questions. First, is it acceptable to ignore the less costly activities? Second, if multiple maintenance activities are to be considered, is it acceptable to ignore their interdependence? The results show that less costly maintenance activities cannot be ignored. Furthermore, if multiple activities are to be considered, their interdependence should be taken into account. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimizing bridge maintenance and replacement (M&R) decisions for a heterogeneous system of facilities. The objective is to determine optimal M&R policies for each facility over a finite planning horizon based on the knowledge of the current facilities conditions and on the prediction of future conditions. The system-level problem is based on the results of an M&R optimization problem for each facility. The results of the facility-level optimization are incorporated in a reliability-based, bottom-up, system-level formulation that provides recommendations for each individual facility. We derive sufficient conditions for optimality and prove the result for the continuous case. A parametric study shows that the results obtained in the discretecase implementation of the solution are valid approximations of the continuous case results. The computational efficiency of the system-level solution makes the formulation suitable for systems of realistic sizes. [ABSTRACT FROM AUTHOR]

In this paper we present the development of an empirical pavement overlay crack initiation model using condition survey data for the highway system in the state of Washington. We use a semiparametric Cox model and show that its hazard function represents the sample hazard function, which follows a bathtub shape. This is a shape that commonly used parametric models cannot predict. Empirical data complications, such as the endogeneity of overlay design variables and data censoring, are corrected for using appropriate econometric techniques. Our research shows that a specification that captures the main factors responsible for the overlay crack initiation process, combined with careful analysis of the data, can produce models of sufficient realism for maintenance and rehabilitation action selection purposes. [ABSTRACT FROM AUTHOR]

Bridge maintenance and replacement optimization methods use deterioration models to predict the future condition of bridge components. The purpose of this paper is to develop a framework for bridge maintenance optimization using a deterioration model that takes into account aspects of the history of the bridge condition and maintenance, while allowing the use of efficient optimization techniques. Markovian models are widely used to represent bridge component deterioration. In existing Markovian models, the state is the bridge component condition, and the history of the condition is not taken into account, which is seen as a limitation. This paper describes a method to formulate a realistic history-dependent model of bridge deck deterioration as a Markov chain, while retaining aspects of the history of deterioration and maintenance as part of the model. This model is then used to formulate and solve a reliability-based bridge maintenance optimization problem as a Markov decision process. A parametric study is conducted to compare the policies obtained in this research with policies derived using a simpler Markovian model. [ABSTRACT FROM AUTHOR]

We present an application of systematic probing for selecting optimal maintenance, repair, and reconstruction (MR&R) policies for systems of infrastructure facilities under model uncertainty. We use an open-loop feedback control approach, where the model parameters are updated sequentially after every inspection round. The use of systematic probing improves the convergence of the model parameters by ensuring that all permissible actions are applied to every condition state. The results of the parametric analyses demonstrate that the MR&R policies converge earlier when systematic probing is used. However, the savings in the expected total costs as a result of probing are minor, and are only realized when the optimal probing fractions are used. On the other hand, the additional costs incurred when the wrong probing fractions are used are significant. The major conclusion from this work is that state-of-the-art adaptive infrastructure management systems, that do not use probing, provide sufficiently close to optimal policies. [ABSTRACT FROM AUTHOR]

Asset management systems help public works agencies decide when and how to maintain and rehabilitate infrastructure facilities in a cost-effective manner. Many sources of error, some difficult to quantify, can limit the ability of asset management systems to accurately predict how built systems will deteriorate. This article introduces the use of robust optimization to deal with epistemic uncertainty. The Hurwicz criterion is employed to ensure management policies are never “too conservative.” An efficient solution algorithm is developed to solve robust counterparts of the asset management problem. A case study demonstrates how the consideration of uncertainty alters optimal management policies and shows how the proposed approach may reduce maintenance and rehabilitation (M&R) expenditures. [ABSTRACT FROM AUTHOR]

We present an adaptive optimization model to find joint inspection and maintenance policies for infrastructure facilities under performance model uncertainty. The objective in the formulation is to minimize the total expected social cost of managing facilities over a finite planning horizon. Recent optimization models account for the uncertainty in the selection of facility performance models through an adaptive control approach. In this paper, we relax the assumptions of fixed and error-free inspections. We present a parametric study to analyze the effect of initial performance model uncertainty and bias on the expected total cost of managing a facility. The main observation is that reducing the initial variance in model uncertainty may be more important than reducing the initial bias. Our study also shows that cost savings can result from relaxing the constraint of a fixed inspection schedule. [ABSTRACT FROM AUTHOR]

A recursive nonlinear model was developed for the prediction of pavement performance as a function of traffic characteristics, pavement structural properties, and environmental conditions. The model highlights some of the advantages of relaxing the linear restriction that is usually placed on the specification form of pavement performance models. First, a functional form that better represents the physical deterioration process can be used. Second, the estimated parameters are unbiased, owing to a proper specification and the use of sound statistical techniques. Finally, the standard error of the prediction is reduced by half that of the equivalent existing linear model. This improved accuracy has important economic implications in the context of pavement management. The model developed as part of this research enables the determination of an unbiased exponent of the so-called power law and of the equivalent loads for different axle configurations. The estimated exponent confirms the value of 4.2 traditionally used. However, it should be noted that this exponent is only to be used for determining damage in terms of serviceability. On the other hand, equivalent loads estimated for different axle configurations tend to differ from traditionally used values. [ABSTRACT FROM AUTHOR]

Sound infrastructure deterioration models are essential for accurately predicting future conditions that, in turn, are key inputs to effective maintenance and rehabilitation decision making. The challenge central to developing accurate deterioration models is that condition is often measured on a discrete scale, such as inspectors’ ratings. Furthermore, deterioration is a stochastic process that varies widely with several factors, many of which are generally not captured by available data. Consequently, probabilistic discrete-state models are often used to characterize deterioration. Such models are based on transition probabilities that capture the nature of the evolution of condition states from one discrete time point to the next. However, current methods for determining such probabilities suffer from several serious limitations. An alternative approach addressing these limitations is presented. A probabilistic model of the time spent in a state (referred to as duration) is developed, and the approach used for estimating its parameters is described. Furthermore, the method for determining the corresponding state transition probabilities from the estimated duration models is derived. The testing for the Markovian property is also discussed, and incorporating the effects of history dependence, if found present, directly in the developed duration model is described. Finally, the overall methodology is demonstrated using a data set of reinforced concrete bridge deck observations. [ABSTRACT FROM AUTHOR]

Editorial. Provides information on several associate editors of the periodical 'Journal of Infrastructure Systems.' Arpad Horvath of University of California in Berkeley; Frannie A. L é autier of World Bank Institute; Andrew C. Lemer of the MATRIX Group LLC; Christopher Leung of Hong Kong University of Science and Technology.

The objective of this paper is to incorporate seismic hazard and risk analysis considerations, which are concerned with the occurrence of earthquakes and the vulnerability of structures, into bridge management systems. We develop a decision model for optimizing bridge maintenance, repair, and reconstruction (MR&R) policies that takes into account the occurrence of earthquake events. The model presented in this paper is not meant to be very detailed or comprehensive, but rather to allow us to obtain qualitative implications of including seismic considerations in bridge management systems. Based on four different case studies, we found that accounting for the probability of earthquake occurrence in a bridge MR&R decision-making model had a significant impact on the probability distribution of the bridge condition state, the optimal policies, and their total cost. Furthermore, we found that ignoring the probability of destruction due to earthquakes would lead to errors in budgeting. [ABSTRACT FROM AUTHOR]

In the first part of this paper, an analysis of the data collected during the American Association of State Highway Officials (AASHO) road test, based on probabilistic duration modeling techniques, is presented. Duration techniques enable the stochastic nature of pavement failure time to be evaluated as well as censored data to be incorporated in the statistical estimation of the model parameters. The second part of this paper presents the use of economic optimization principles for determining the optimal design of flexible pavements. We study the effect of deterioration model accuracy on optimal design and lifecycle costs by comparing three models. The first is a simple regression model developed by the AASHO, which forms the basis of design standards in use today. The second is a regression model that was developed with the same AASHO data set, but that includes a correction for data censoring. The third model is the probabilistic model developed in the first part of this paper. The results show that the AASHO model, when used as an input to lifecycle cost minimization, produces a pavement structural number that is lower than that produced by using the other two deterioration models. This results in shorter pavement lives and higher costs due to more frequent resurfacing. The savings in lifecycle cost accrued by using optimal structural number are shown to be quite significant, offering a sound basis for revising current design practices. [ABSTRACT FROM AUTHOR]

Presents information on a study which demonstrated the effectiveness of the estimation of rutting models by combining the information from two data sources, the AASHO and the WesTrack road tests. Essential element for the efficient management of pavement systems; Model specifications; Estimation results for individual models; Joint estimation results; Conclusions.

The development of models to predict the distribution of times between changes in condition-state for reinforced concrete bridge decks is presented in this paper. The authors used semiparametric hazard rate modeling techniques to develop stochastic duration models for bridge decks in the Indiana Bridge Inventory database. The advantage of semiparametric methods is that they do not constrain the hazard rate to follow a prespecified probability density function. This approach is flexible enough to accommodate different types of deterioration processes, and can account properly for both censored and uncensored data. Furthermore, it relates observed deterioration to relevant explanatory variables. Finally, because this approach quantifies the time-dependent hazard rate function, it facilitates the computation of time-dependent transition probabilities for use in bridge management systems. The models presented in this paper are characterized by realistic trends. The estimated parameters are statistically significant and have, for the most part, intuitively correct signs. However, the models include only a small number of explanatory variables, and thus do not capture the entire array of relevant factors that contribute to bridge deck deterioration. This is especially true when they are compared to the state-based models obtained in earlier research using the same data. The reasons are that the deck conditions are observed at infrequent points in time (every 2 years) and that the window of observation is relatively short (10 years). [ABSTRACT FROM AUTHOR]

Provides information on a study which examined the development of an empirical pavement rutting progression model using experimental data. Factors affecting pavement rutting; Model specification; Conclusions.

State-of-the-art infrastructure management systems use Markov decision processes (MDPs) as a methodology for maintenance and rehabilitation (M&R) decision making. The underlying assumption in this methodology is that inspections are performed at predetermined and fixed time intervals and that they reveal the true condition state of the facility, with no measurement error. As a result, after an inspection, the decision maker can apply the activity prescribed by the optimal policy for that condition state of the facility. In previous research, the second author has applied a methodology for M&R activity selection that accounts for the presence of both forecasting and measurement uncertainty. This methodology is the latent Markov decision process (LMDP), an extension of the traditional MDP that relaxes the assumptions of error-free annual facility inspections. In this article we extend this methodology to include network-level constraints. This can be achieved by extending the LMDP model to the network-level problem through the use of randomized policies. We present both finite-horizon (transient) and infinite-horizon (steady-state)formulations of the network-level LMDP. A case study application demonstrates the expected savings in life-cycle costs that result from increasing the measurement accuracy used in facility inspections and from optimal scheduling of inspections. [ABSTRACT FROM AUTHOR]

This paper presents an approach for the joint optimization of maintenance and improvements of the components of a network of infrastructure facilities. In the literature, these two problems have often been handled separately, probably because the problems seem quite different. However, these decisions (maintenance and improvement) are not independent due to the presence of trade-offs between the two sets of policies. We develop a Markov decision model for the joint optimization of maintenance and improvement, thus improving the budget allocation among facilities in the network between the two sets of activities and within each set. The model is used to solve for steady-state policies, but relaxes the assumption of age-homogeneous condition-state transition probabilities, which has been criticized in the literature. Moreover, the model allows for the possibility of not exhausting the annual budget available every year, so that part of it can be spent more efficiently in later years. The paper includes a case study that demonstrates that substantial savings can be achieved through the joint optimization of maintenance and improvement policies. [ABSTRACT FROM AUTHOR]

Markovian transition probabilities have been used extensively in the field of infrastructure management, to provide forecasts of facility conditions. However, existing approaches used to estimate these transition probabilities from inspection data are mostly ad hoc and suffer from several statistical limitations. In this paper, econometric methods for the estimation of infrastructure deterioration models and associated transition probabilities from inspection data are presented. The first method is based on the Poisson regression model and follows directly from the Markovian behavior of infrastructure deterioration. The negative binomial regression, a generalization of the Poisson model that relaxes the assumption of equality of mean and variance, is also presented. An empirical case study, using a bridge inspection data set from Indiana, demonstrates the capabilities of the two methods. [ABSTRACT FROM AUTHOR]

State-of-the-art decision-making models in the area of infrastructure maintenance and rehabilitation (which are based on the Markov Decision Process) do not take into account the uncertainty in the measurement of facility condition. This paper presents a methodology, the Latent Markov Decision Process (LMDP), which explicitly recognizes the presence of random errors in the measurement of the condition of infrastructure facilities. Two versions of the LMDP are presented. In the first version, the inspection schedule is fixed, which is the usual assumption made in state-of-the-art models. The second version of the LMDP minimizes the sum of inspection and M & R costs. An empirical comparison of the two versions of the LMDP and the traditional MDP illustrates the importance of incorporating measurement uncertainty in decision-making and of optimizing the inspection schedule. [ABSTRACT FROM AUTHOR]

Studies of pipe water demand in developing countries have traditionally analyzed household connection decisions to the pipe water system. On the other hand, empirical observations have revealed that often, after connecting, households do not use their pipe water supply, or augment it with alternative sources. Due to deficiencies in pipe water quality, pressure, or availability, households invest in coping strategies in the form of alternative supplies and storage facilities. Because these strategies have important economic implications, there is a need to develop an understanding of households' water demand that goes beyond connection decisions. This paper presents a model system of household water supply choices. The system accounts for the fact that households may use different supply systems for different uses of water. Moreover, the relation between households' choices of water supply and their connection decisions is explicitly modeled. The approach is illustrated using data from Faisalabad, Pakistan. [ABSTRACT FROM AUTHOR]

In selecting the type of maintenance or rehabilitation (M&R) activity to apply to highway pavement sections, engineers rely on several items of information. Foremost among these is the effectiveness of the various M&R activities in slowing pavement deterioration and improving its condition. A number of researchers have sought to quantify the effectiveness of M&R activities by developing separate models of pavement deterioration for each type of M&R activity. Often, the results of such analyses have not been satisfactory. For example, some researchers have reported models with poor fits to data or counterintuitive signs of important variable coefficients. These results may be due to the fact that the sample used for model estimation was self-selected. The observations used to analyze the effectiveness of a certain M&R activity are not representative of the population of highway pavement sections, because these observations consist mainly of sections for which that particular activity was believed to be most effective. This paper presents a structured econometric approach for estimating the effectiveness of pavement M&R activities. This approach consists of a discrete model describing the choice of M&R activity by the highway agency, and a set of continuous models representing pavement response, with one equation for each activity. This model system accounts for the self-selected nature of the sample, and consequently obtains consistent model parameter estimates. A case study that demonstrates the applicability and value of the methodology is presented. The data set used for model estimation was provided by the Indiana Department of Transportation. [ABSTRACT FROM AUTHOR]

Statistical models of infrastructure facility deterioration are typically estimated using panel data sets of in-service facilities. For example, biannual ratings of bridges have been used to develop discrete models of component deterioration by a number of researchers. Unfortunately, these models have not accounted for the presence of heterogeneity in the panel data, which may lead to biased coefficient estimates. Furthermore, researchers have usually imposed a Markovian specification in the development of such models, implying that the probabilistic deterioration in a given period is independent of history. This assumption may be unrealistic for some types of facilities in which early stress initiation leads to accelerated deterioration in later stages of their lives. In this paper, we adopt a random-effects specification to control for heterogeneity in a probit model of bridge-deck deterioration and extend the model to investigate the presence of state dependence. The proposed model yields improved results in comparison with a simple probit model and provides evidence that is inconsistent with the Markovian assumption in bridge-deck deterioration. An implication of this study is that both heterogeneity and state dependence may need to be accounted for in developing probabilistic infrastructure deterioration models. [ABSTRACT FROM AUTHOR]

Infrastructure distress models predict the initiation and progression of distress on a facility over time as a function of age, design characteristics, environmental factors, and so on. Examples of facility distress included cracking, potholing, and rutting. Facility condition survey data sets typically include a large number of structural zeros indicating absence of distress at the time of observation. Most distress progression models in the literature are simple regression models that are estimated using the sample of observations for which distress has been initiated. These models are statistically erroneous because they suffer from selectivity bias due to the nonrandom nature of the estimation sample used. In this paper, we apply two econometric methods to estimate joint discrete-continuous models of infrastructure distress initiation and progression while correcting for selectivity bias. These methods are Heckman's procedure and the full information maximum likelihood method. An empirical case study demonstrates these methods for the case of highway-pavement-cracking models. It is shown that selectivity bias can be a very serious problem in such models. [ABSTRACT FROM AUTHOR]

Markovian transition probabilities have been used extensively in the field of infrastructure management to provide forecasts of facility conditions. However, existing approaches used to estimate these transition probabilities from inspection data are mostly ad hoc and suffer from important methodological limitations. In this paper, we present a rigorous econometric method for the estimation of infrastructure deterioration models and associated transition probabilities from condition rating data. This methodology, which is based on ordered probit techniques, explicitly treats facility deterioration as a latent variable, recognizes the discrete ordinal nature of condition ratings, and, as opposed to state-of-the-art methods, explicitly links deterioration to relevant explanatory variables. An empirical case study using a bridge inspection data set from Indiana demonstrates the capabilities of the proposed methodology. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of a conference on transportation infrastructure preservation held in 2010. The conference, which was sponsored by the U.S. Transportation Research Board, attracted infrastructure owners and decision makers. Topics tackled include inventory and condition assessment methods, strategies for repair and rehabilitation of transportation structures, and model deterioration processes and life-cycle costs.

Presents an update on the changes in the "Journal of Infrastructure Systems." Addition of associate editors; Increase in the number of papers published; Focus on the field of automated inspection of infrastructure systems.

Announces changes in the publication of the 'Journal of Infrastructure Systems.'