The objective of this paper is to demonstrate the development of pavement performance models by combining experimental and field data. A two step approach was used. In the first step a riding quality model based on serviceability consideration is developed. The data set of the American Association of State Highways Officials (AASHO) Road Test is used to this effect. Due to the experimental nature of the AASHO Road Test data set, some of the estimated parameters of the model may be biased when the model is to be applied to predict performance in the field. In the second step, the original model parameters are reestimated by applying joint estimation allowed for with the incorporation of field data set. This data set was collected through the Minnesota Road Research Project (MnRoad). The final model is referred to as the joint model, and it can be used to predict the performance of in-service pavement sections. Joint estimation allowed for the full potential of both data sources to be exploited. First, the effect of variables not available in the first data source were identified and quantified. Further, the parameter estimates had lower variance because multiple data sources were pooled, and biases in the parameters of the experimental model were corrected. Finally, different measurements of the same property were incorporated by using a measurement error model. Thus, the methodology proposed in this paper makes optimum use of available data and yields models of improved statistical properties compared with techniques such as ordinary least squares.

In this research, a reliability-based optimization model of bridge maintenance and replacement decisions is developed. Bridge maintenance optimization models use deterioration models to predict the future condition of bridges. Some current optimization models use physically-based models taking into account the history of deterioration. However, due to the complexity of the deterioration models, the number of decision variables in these optimization models is limited. Some other optimization models consist of a full set of decision variables; however, they use simpler deterioration models. Namely, these deterioration models are Markovian, and the state of the Markov chain is limited to the condition of the facility.

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.

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.

This paper presents a reliability-based optimization model of inspection, maintenance, and replacement for a system of several highway bridges. The objective in the formulation is to minimize the total expected social cost, including the expected cost of failure. The frequency of inspections is included as a decision variable. The probability of failure is explicitly taken into account in the constraints. A bottom-up approach is used, which allows for bridge-specific details to be taken into account. Most existing system level models assume that component deterioration is memoryless; however, this assumption is relaxed in this paper, and history-dependent deterioration models are used. The formulation is flexible enough to accommodate different types of facilities, deterioration processes, and failure modes. A parametric study is conducted to demonstrate the model’s response to different assumptions on the deterioration rates, and maintenance costs and efficiency.

In this paper we present two adaptive control formulations that explicitly include uncertainty in characterizing a facility's deterioration rate in the process of developing maintenance and repair policies. The formulations use condition information that stems from the operation of a facility to improve the characterization over a finite planning horizon. We discuss issues related to the implementation and computational complexity of the formulations. Through a computational study, we show that the economic benefits can be achieved by implementing the adaptive control formulations. These benefits are most significant in situations where the initial expected deterioration rate of the facility is not a good representation of its actual deterioration rate.

A Pavement Management System (PMS) is a decision-support tool that aids public agencies in planning maintenance activities of their facilities. A complete PMS involves the following tasks: inspecting facilities and collecting data, predicting the deterioration of facilities through performance models, and optimizing the Maintenance, Rehabilitation, and Reconstruction (MR&R) policies over the planning horizon. Performance models are a core component of PMS. These models are also used to calibrate facility design procedures.

The main objective of this project was to develop Empirical-Mechanistic (E-M) performance models using data from Washington State’s PMS databases. Four models were developed from that data:

* A model for predicting the initiation of overlay cracking in asphalt concrete (AC) pavements * A model for predicting the progression of roughness for AC pavements * A model for predicting the initiation of cracking in portland cement concrete (PCC) pavements * A model for predicting the progression of roughness for portland cement concrete pavements

At the start of the project, models using pavement maintenance data from the Washington State Department of Transportation (WSDOT) and the Arizona Department of Transportation (ADOT) were attempted. The initial reasoning for using PMS data from those states is that they have very measured pavement conditions consistently over a long period of time, and they have topographic and climate regions similar to parts of California. Therefore, Caltrans could use models developed using data from those states to manage a subset of California’s pavement infrastructure until the department develops the database needed to support model development. However, the research team found that the ADOT data were inappropriate for developing the type of performance models needed in this project, so only WSDOT pavement data were used.

Infrastructure management systems assist agencies in making decisions regarding maintenance, repair, and reconstruction of the facilities under their jurisdiction. The objective in these decision-making tools is to minimize the total expected cost of managing a system of facilities over a given 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 extend the methodology to jointly determine when to inspect and what maintenance activity to perform, while taking into account uncertainty in measuring facility condition. A parametric study is performed to analyze the effect of the initial performance model uncertainty and bias on the expected total cost of managing a facility over a finite horizon. The parametric study shows that reducing model uncertainty leads, as expected, to lower costs. The results also indicate that reducing the initial variance in model uncertainty is more important than reducing the initial bias. In addition, our study shows that cost savings can result from relaxing the constraint of a fixed inspection schedule.

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.

Skid resistance is one of the important serviceability indicators related to safety on wet pavements. There is a need to manage skid resistance systematically to maintain the level of safety performance of roadway surfaces. This study focused on the development of a skid resistance deterioration model based on the analysis of skid data inventory collected in California. The California Department of Transportation (Caltrans) has collected skid resistance data across the complete state highway network over the past two decades using a standard locked-wheel skid trailer, ASTM E-274. This study utilizes skid data collected on more than 300 miles of asphalt concrete freeway in California over a period of twenty years. Most of the possible factors found in previous studies to influence skid resistance were considered. Panel data parameter estimation methods were used. The results indicate that factors with the largest effects on skid resistance are the age of pavement, ADT, temperature, precipitation, and the length of the period since the last significant precipitation.