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Highway bridges undergo rapid deterioration in both condition and safety under the influence of aggressive environmental factors and increasing traffic loads. Timely and adequate maintenance interventions therefore are crucial to ensure the functionality of bridges in a network. Under budget constraints, it is important to prioritize the maintenance of those bridges most significant to the functionality of the entire network. In this paper, the problem of network-level bridge maintenance planning is formulated as a combinatorial optimization problem. An algorithm is devised to select and allocate maintenance interventions of different types to networked bridges over a specified time under the assumption of annual inspections. The objective function considered was the network probability of failure, expressed by the time-dependent reliability of connectivity between the origin and the destination location, which was maximized while total maintenance costs were constrained under a budget. Various maintenance actions, which differed in unit cost and their effects on bridge performance, were used in the optimization. As an example, the optimization procedure was applied to a heterogeneous, five-bridge network. The proposed maintenance planning procedure can prioritize scarce maintenance needs for use on the deteriorating bridges most crucial to network performance.

Highway bridges undergo rapid deterioration in both condition and safety under the influence of aggressive environmental factors and increasing traffic loads. Timely and adequate maintenance interventions therefore are crucial to ensure the functionality of bridges in a network. Under budget constraints, it is important to prioritize the maintenance of those bridges most significant to the functionality of the entire network. In this paper, the problem of network-level bridge maintenance planning is formulated as a combinatorial optimization problem. An algorithm is devised to select and allocate maintenance interventions of different types to networked bridges over a specified time under the assumption of annual inspections. The objective function considered was the network probability of failure, expressed by the time-dependent reliability of connectivity between the origin and the destination location, which was maximized while total maintenance costs were constrained under a budget. Various maintenance actions, which differed in unit cost and their effects on bridge performance, were used in the optimization. As an example, the optimization procedure was applied to a heterogeneous, five-bridge network. The proposed maintenance planning procedure can prioritize scarce maintenance needs for use on the deteriorating bridges most crucial to network performance.

Deterioration equivalence factors are an essential part of deterioration models used in pavement management systems. For accurate cost estimation, it is important to determine the correct axle load power. The data set from the AASHO Road Test was used to estimate a hazard rate function. The results showed that the axle load power for initiation of fatigue cracking was significantly higher than the power typically assumed. This result has implications for pavement deterioration charging schemes that are based on marginal cost pricing.

Deterioration equivalence factors are an essential part of deterioration models used in pavement management systems. For accurate cost estimation, it is important to determine the correct axle load power. The data set from the AASHO Road Test was used to estimate a hazard rate function. The results showed that the axle load power for initiation of fatigue cracking was significantly higher than the power typically assumed. This result has implications for pavement deterioration charging schemes that are based on marginal cost pricing.

Pavement deterioration models are important inputs for the efficient management of pavement systems, the allocation of cost responsibilities to various vehicle classes for their use of the highway system, and the design of pavement structures. The development of an empirical rutting progression model with an experimental data set from WesTrack is described. The salient features of the model specification are as follows: (a) three properties of the mix are sufficient to model the performance of the asphalt concrete pavement at WesTrack accurately; (b) the model captures the effects of the high air temperatures at WesTrack; and (c) the model predicts rut depths by adding predicted values of the increment of rut depth for each time period, which is particularly advantageous in a pavement management context. The three mix properties are a gradation index, which is obtained from the aggregate gradation; the voids filled with asphalt obtained for the construction mix in the Superpave gyratory compactor; and the initial in-place air voids. The specified model is nonlinear in the variables and the parameters and is estimated by using a random-effects specification to account for unobserved heterogeneity. The estimation results and prediction tests show that the model replicates the observed pavement behavior at WesTrack well.

Pavement deterioration models are important inputs for the efficient management of pavement systems, the allocation of cost responsibilities to various vehicle classes for their use of the highway system, and the design of pavement structures. The development of an empirical rutting progression model with an experimental data set from WesTrack is described. The salient features of the model specification are as follows: (a) three properties of the mix are sufficient to model the performance of the asphalt concrete pavement at WesTrack accurately; (b) the model captures the effects of the high air temperatures at WesTrack; and (c) the model predicts rut depths by adding predicted values of the increment of rut depth for each time period, which is particularly advantageous in a pavement management context. The three mix properties are a gradation index, which is obtained from the aggregate gradation; the voids filled with asphalt obtained for the construction mix in the Superpave gyratory compactor; and the initial in-place air voids. The specified model is nonlinear in the variables and the parameters and is estimated by using a random-effects specification to account for unobserved heterogeneity. The estimation results and prediction tests show that the model replicates the observed pavement behavior at WesTrack well.

A bridge management system (BMS) is a decision support system used by a highway agency in selecting appropriate maintenance and rehabilitation (M&R) activities and in allocating available resources effectively among facilities. BMS decision making is based on the condition of bridge components, their predicted deterioration, and the cost and effectiveness of M&R activities. Traditionally, bridge condition assessments have relied mainly on human inspectors; their results have generally been qualitative and subjective. More detailed inspections requiring some degree of destruction of the bridge, like drilling the deck to inspect for chloride contamination, have also been used. With recent technological developments, methods have been developed to evaluate the condition of bridge structures in a quantitative and objective manner. Associated with the use of these technologies are questions relating to inspection frequency, sample size, and the integration of data from the various technologies and human inspections. The application of a statistical decision-making method, sequential hypothesis testing, to these questions is presented. The mathematical formulation of the sequential hypothesis testing model, the derivation of optimal inspection policies, and the implementation of these policies in the context of bridge component inspection are discussed. A parametric analysis illustrates the sensitivity of the method to the cost structure of the problem, the precision of the technologies used, and the historical information or expert judgment regarding the condition of bridge components.

Predicting pavement performance under the combined action of traffic and the environment provides valuable information to a highway agency. The estimation of the time at which the pavement conditions will fall below an acceptable level (failure) is essential to program maintenance and rehabilitation works and for budgetary purposes. However, the failure time of a pavement is a variable event; terminal conditions will be reached at different times at various locations along a homogeneous pavement section. A common problem in modeling event duration is caused by unobserved failure events in a typical data set. Data collection surveys are usually of limited length. Thus, some pavement sections will have already failed by the day the survey starts; others will reach terminal conditions during the survey period, whereas others will only fail after the survey has been concluded. If only the failure events observed during the survey are included in the statistical analysis (disregarding the information on the events after and before the survey), the model developed will suffer from truncation bias. If the censoring of the failure events is not accounted for properly, the model may suffer from censoring bias. An analysis of the data collected during the Road Test sponsored by the American Association of State Highway Officials (AASHO) is presented. The analysis is based on the use of probabilistic duration modeling techniques. Duration models 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 results, based on sound statistical principles, show that the failure times predicted with the model match the observed pavement failure data better than those from the original AASHO equation.

Predicting pavement performance under the combined action of traffic and the environment provides valuable information to a highway agency. The estimation of the time at which the pavement conditions will fall below an acceptable level (failure) is essential to program maintenance and rehabilitation works and for budgetary purposes. However, the failure time of a pavement is a variable event; terminal conditions will be reached at different times at various locations along a homogeneous pavement section. A common problem in modeling event duration is caused by unobserved failure events in a typical data set. Data collection surveys are usually of limited length. Thus, some pavement sections will have already failed by the day the survey starts; others will reach terminal conditions during the survey period, whereas others will only fail after the survey has been concluded. If only the failure events observed during the survey are included in the statistical analysis (disregarding the information on the events after and before the survey), the model developed will suffer from truncation bias. If the censoring of the failure events is not accounted for properly, the model may suffer from censoring bias. An analysis of the data collected during the Road Test sponsored by the American Association of State Highway Officials (AASHO) is presented. The analysis is based on the use of probabilistic duration modeling techniques. Duration models 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 results, based on sound statistical principles, show that the failure times predicted with the model match the observed pavement failure data better than those from the original AASHO equation.

A bridge management system (BMS) is a decision support system used by a highway agency in selecting appropriate maintenance and rehabilitation (M&R) activities and in allocating available resources effectively among facilities. BMS decision making is based on the condition of bridge components, their predicted deterioration, and the cost and effectiveness of M&R activities. Traditionally, bridge condition assessments have relied mainly on human inspectors; their results have generally been qualitative and subjective. More detailed inspections requiring some degree of destruction of the bridge, like drilling the deck to inspect for chloride contamination, have also been used. With recent technological developments, methods have been developed to evaluate the condition of bridge structures in a quantitative and objective manner. Associated with the use of these technologies are questions relating to inspection frequency, sample size, and the integration of data from the various technologies and human inspections. The application of a statistical decision-making method, sequential hypothesis testing, to these questions is presented. The mathematical formulation of the sequential hypothesis testing model, the derivation of optimal inspection policies, and the implementation of these policies in the context of bridge component inspection are discussed. A parametric analysis illustrates the sensitivity of the method to the cost structure of the problem, the precision of the technologies used, and the historical information or expert judgment regarding the condition of bridge components.

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 assumptions in this methodology are that an inspection is performed at the beginning of every year and that inspections reveal the true condition state of the facility, with no 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. Previous research has developed 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 does not necessarily assume the measurement of facility condition to be free of error. Both a transient and a steady-state formulation of the facility-level LMDP are presented. The methodology is extended 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. In addition, both a transient and a steady-state formulation of the network-level LMDP are presented. 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 scheduling inspection decisions optimally.

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 assumptions in this methodology are that an inspection is performed at the beginning of every year and that inspections reveal the true condition state of the facility, with no 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. Previous research has developed 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 does not necessarily assume the measurement of facility condition to be free of error. Both a transient and a steady-state formulation of the facility-level LMDP are presented. The methodology is extended 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. In addition, both a transient and a steady-state formulation of the network-level LMDP are presented. 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 scheduling inspection decisions optimally.

Pavement distress progression models predict the extent of a distress on pavement sections as a function of age, design characteristics, traffic loads and environmental factors. These models are usually developed using data from in-service facilities to calibrate the parameters of mechanistic deterioration models. The data used for the statistical estimation of such models consist of observations of pavements for which the distress has already appeared. Unfortunately, common statistical methods, when applied to such data sets, produce biased and inconsistent model parameters. This type of bias is known as selectivity bias, and it results from the fact that less durable pavement sections are over-represented in the sample used for model estimation. A joint pavement distress initiation and progression model, consisting of a discrete model of distress initiation and a continuous model of pavement progression is presented. This approach explicitly accounts for the self-selected nature of the sample used in developing the progression model, through the use of appropriate correction terms. Moreover, previous research is extended by accounting for the potential presence of unobserved heterogeneity in the model, which is related to the use of a panel data set for model estimation. This is achieved by using a random effects specification for both the discrete and continuous models. An empirical case study demonstrates the application of this approach for highway pavement cracking models.

Pavement distress progression models predict the extent of a distress on pavement sections as a function of age, design characteristics, traffic loads and environmental factors. These models are usually developed using data from in-service facilities to calibrate the parameters of mechanistic deterioration models. The data used for the statistical estimation of such models consist of observations of pavements for which the distress has already appeared. Unfortunately, common statistical methods, when applied to such data sets, produce biased and inconsistent model parameters. This type of bias is known as selectivity bias, and it results from the fact that less durable pavement sections are over-represented in the sample used for model estimation. A joint pavement distress initiation and progression model, consisting of a discrete model of distress initiation and a continuous model of pavement progression is presented. This approach explicitly accounts for the self-selected nature of the sample used in developing the progression model, through the use of appropriate correction terms. Moreover, previous research is extended by accounting for the potential presence of unobserved heterogeneity in the model, which is related to the use of a panel data set for model estimation. This is achieved by using a random effects specification for both the discrete and continuous models. An empirical case study demonstrates the application of this approach for highway pavement cracking models.

Recent research in advanced traffic management systems has emphasized incident detection and response to mitigate nonrecurring congestion. Existing incident response decision-making algorithms do not account for the expected losses associated with false alarms, undetected incidents, and delayed incident response. A freeway incident response decision-making system based on sequential hypothesis testing techniques is presented. The primary feature of this decision-making system is that it minimizes the sum of the expected losses associated with false response, nonresponse, and delayed responses to incidents through a dynamic programming algorithm. The results of simulation tests indicate that this algorithm performs better than typical Bayesian incident response algorithms for mean response time, false response rate, and nonresponse rate.

An enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway origin-and-destination (O-D) matrices is presented. The effects of traffic congestion and traffic diversion information on the O-D distribution pattern are explicitly captured through a behavioral model of route switching. Moreover, in view of the time-varying nature of traffic variables, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation and prediction of dynamic freeway O-D demands.

Recent research in advanced traffic management systems has emphasized incident detection and response to mitigate nonrecurring congestion. Existing incident response decision-making algorithms do not account for the expected losses associated with false alarms, undetected incidents, and delayed incident response. A freeway incident response decision-making system based on sequential hypothesis testing techniques is presented. The primary feature of this decision-making system is that it minimizes the sum of the expected losses associated with false response, nonresponse, and delayed responses to incidents through a dynamic programming algorithm. The results of simulation tests indicate that this algorithm performs better than typical Bayesian incident response algorithms for mean response time, false response rate, and nonresponse rate.

An enhanced Kalman Filtering algorithm for the dynamic estimation and prediction of freeway origin-and-destination (O-D) matrices is presented. The effects of traffic congestion and traffic diversion information on the O-D distribution pattern are explicitly captured through a behavioral model of route switching. Moreover, in view of the time-varying nature of traffic variables, the proposed algorithm updates the model parameters by using on-line traffic measurements. Preliminary simulation results demonstrate the importance of using time-dependent model parameters and accounting for the effect of traffic information in the estimation and prediction of dynamic freeway O-D demands.