The accurate prediction of pavement performance is important for efficient management of the surface transportation infrastructure. By reducing the error of the pavement deterioration prediction, agencies can obtain significant budget savings through timely intervention and accurate planning.

The goal of this research was to develop a methodology for developing accurate pavement deterioration models to be used primarily for the management of the road infrastructure. The loss of the riding quality of the pavement was selected as the performance indicator. Two measures of riding quality were used: serviceability (Present Serviceability Index, PSI) and roughness (International Roughness Index, IRI).

An acceptable riding quality is important for both the road user and the goods being transported. Riding quality affects the comfort of the user for whom the road is provided, and the smoothness with which goods are moved from one point to another. The vehicle operating costs and the costs of transporting goods increase as the road riding quality deteriorates. These costs are often one order of magnitude more important than the cost of maintaining the road to an acceptable level of service.

The initial incremental models developed in this dissertation predict serviceability as a function of material properties, pavement structural characteristics, traffic axle configuration, axle load, and environmental variables. These models were developed applying nonlinear estimation techniques using an experimental unbalanced panel data set (AASHO Road Test). The unobserved heterogeneity among the pavement sections was accounted for by using the random effects approach.

The serviceability models were updated using joint estimation with a field panel data set (MnRoad Project). The updated model estimates riding quality in terms of roughness. This was possible by applying a measurement error model to combine both data sources.

The main contribution of this research is not the development of a deterioration model itself, but rather the demonstration of the feasibility of using joint estimation and its many advantages, such as: (i) identification and quantification of new variables, (ii) efficient parameter estimates, (iii) bias identification and correction, and (iv) use of a measurement error model to combine apparently incompatible data sources.

A recursive non-linear 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, especially in the case of single axles with single wheels.

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 highly 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, while others will only fail after the survey is concluded. If only the failure events observed during the survey are included in the statistical analysis (disregarding the information on the after and before events), 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.

In this paper, an analysis of the data collected during the AASHO Road Test 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 the original AASHO equation.

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.