Pavement Deterioration Models Research Articles

Seasonal Deterioration of Unsurfaced Roads

Seasonal deformation of unsurfaced roads was observed over several years and was studied using pavement deterioration models and finite-element analysis. The Mathematical Model of Pavement Performance is a model designed for pavement deterioration prediction and was successfully used for seasonal deterioration modeling because of its flexibility in defining the pavement structure, properties, and seasonal impact. However, these types of models are designed for highways and are somewhat limited in soils characterization and manipulation of the forces at the road–tire interface. Therefore, a three-dimensional dynamic finite-element model of a wheel rolling over soil was applied to simulate local vehicle traffic on a secondary unpaved road. These simulations were used to study the effects of vehicle speed, load, suspension system, wheel torque, and wheel slip on rutting and washboard formation. Modeling results are compared to field measurements and observations.

Derivation of Transition Probability Matrices for Pavement Deterioration Modeling

The derivation of transition matrices has traditionally been effected using one of two methods. The standard approach is to observe, from historical data, the way in which a road network deteriorates from one year to the next, and use this to estimate the transition matrix probabilities. Alternatively, a panel of experienced engineers can be used to estimate the probabilities using expert opinion. This paper proposes and describes the development of three further methods for the determination of transition probabilities. The first method assumes that the historical condition data for each of the sites in the network is readily available. The second utilizes the regression curve obtained from the original data, and the third assumes that the yearly distributions of condition are available to assit in the process. In each case, an objective function aims at minimizing the difference between each of the method functions obtained from the original data and the corresponding functions obtained from the transition probabilities. An analysis of the results concluded that although the transition matrix fitted curve for the third method was not always as close to the regression curve as in the other methods, it did yield a distribution not only closer, but comparable, to the original distributions in all tested cases. The third method has therefore been taken forward and encapsulated in an analytical tool to assist the engineer in the formulation of transition matrices.

Investigating Pavement Cracking: Evolutionary Modular Neural Network Approach

Because of the combined practical and theoretical interest in pavement performance modeling, much research has concentrated on this area during the past few years. The objectives of this study are, first, to cluster pavement distress data into homogeneous groups depending on the extent of distress and, second, to identify and assess the influence of a variety of exogenous variables on the extent of pavement distress. To achieve these objectives, a two-step clustering and classification evolutionary modular neural network is developed with a large (more than 1,000 observations) and recent (1998) data set collected from in-service pavements in 15 European countries. The results indicate that the extent of the cracking distress (magnitude of cracking) can be separated into five homogeneous clusters (with Cluster 1 suggesting very low cracking and Cluster 5 very extensive cracking), with different explanatory variables affecting the extent of cracking in each of these clusters.

Investigating Pavement Cracking

Because of the combined practical and theoretical interest in pavement performance modeling, much research has concentrated on this area during the past few years. The objectives of this study are, first, to cluster pavement distress data into homogeneous groups depending on the extent of distress and, second, to identify and assess the influence of a variety of exogenous variables on the extent of pavement distress. To achieve these objectives, a two-step clustering and classification evolutionary modular neural network is developed with a large (more than 1,000 observations) and recent (1998) data set collected from in-service pavements in 15 European countries. The results indicate that the extent of the cracking distress (magnitude of cracking) can be separated into five homogeneous clusters (with Cluster 1 suggesting very low cracking and Cluster 5 very extensive cracking), with different explanatory variables affecting the extent of cracking in each of these clusters.

HDM-4 Pavement Deterioration Models for Indian National Highway Network

The Highway Development and Management model (HDM-4) is a support system for decision making for highway administrators and engineers to predict the economic, social, and environmental impacts that might occur while making investment decisions. The road deterioration models contained in HDM-4 attempt to model the complex interaction between vehicles, the environment, and the pavement structure and surface. The road deterioration models predict the deterioration of the pavement over time and under traffic, which is manifested in various kinds of distress. But as each mode of distress develops and progresses at different rates in different environments, it is important that the HDM-4 relationships be calibrated to reflect local conditions and to ensure their relevance to technico-economic analysis of maintenance and rehabilitation alternatives for a road network constructed in a particular geographical region. This study has attempted to calibrate the HDM-4 pavement deterioration models for a National Highway Network located in the Uttar Pradesh and Uttaranchal states of India. The data have been collected for cracking, raveling, potholing, and roughness, analyzed, and used for calibration of the HDM-4 pavement deterioration model. To test the efficacy of the calibrated model, models have been validated that can be adopted for prediction of distress and development of maintenance management strategies for the Indian National Highway Network. It is expected that the HDM-4 deterioration models calibrated for the Indian National Highway Network can be used for other developing countries with similar traffic characteristics, soil types, climatic conditions, terrain type, and pavement composition.

Partial safety factors for designing and assessing flexible pavement performance

In designing and assessing pavement performance, the uncertainty in material properties and geometrical variables of pavement and in traffic and environmental actions should be considered. A single factor is employed to deal with these uncertainties in the current American Association of State Highway and Transportation Officials (AASHTO) guide for design of pavements. However, use of this single factor may not ensure reliability-consistent pavement design and assessment because different random variables that may have different degrees of uncertainty affect the safety and performance of pavement differently. Similar problems associated with structural design have been recognized by code writers and dealt with using partial safety factors or load resistance factors. The present study is focused on evaluating a set of partial safety factors to be used in conjunction with the flexible pavement deterioration model in the Ontario pavement analysis of cost and the model in the AASHTO guide for evaluating the flexible pavement performance or serviceability. Evaluation and probabilistic analyses are carried out using the first-order reliability method and simple simulation technique. The results of the analysis were used to suggest factors that could be used, in a partial safety factor format, for designing or assessing flexible pavement conditions to achieve a specified target safety level.Key words: deterioration, reliability, pavement, serviceability, stochastic process, performance, partial safety factor.

Stochastic Modeling of Pavement Performance

Pavement performance deterioration cannot be predicted precisely because traffic and environmental actions as well as the material properties and geometric variables of pavement systems are uncertain. Therefore, the prediction of the pavement performance should be carried out based on a probabilistic framework. A simple probabilistic approach is developed in this study for predicting pavement performance. The approach is based on a nonhomogenous continuous Markov chain. Its use in conjunction with the flexible pavement deterioration models in the Ontario Pavement Analysis of Cost (OPAC) and in the AASHTO guide is explored. The proposed approach is more efficient than the ones found in the literature since the probability transition matrix in this study depends only on two model parameters, one controlling the intensity of transition and the other controlling the time transformation. The proposed approach seems able to mimic well the pavement degradation process predicted by the OPAC and AASHTO models.

DEVELOPMENT OF A STOCHASTIC MODEL OF PAVEMENT DISTRESS INITIATION

Pavement deterioration models provide useful predictions of distress initiation, for purposes of pavement design and management. A common problem in modeling the initiation is the inappropriate treatment of data censoring. If the censoring is not accounted for properly, the model may suffer from statistical biases. In this paper, an analysis of pavement crack initiation data based on the duration modeling techniques is presented. Duration models enable the stochastic nature of pavement crack initiation to be represented as well as censored data to be incorporated in the statistical estimation of the model parameters. The results show that the model predictions are more accurate than those obtained with the original AASHO model.

Development of Canadian asphalt pavement deterioration models to benchmark performance

The Canadian Long Term Pavement Performance (C-LTPP) study, initiated in 1989, involves 65 sections located at 24 sites constructed with various asphalt overlay rehabilitation treatments. This study investigates the impacts of the various alternative rehabilitation treatments on pavement roughness progression. A series of models are developed for predicting the rate of pavement deterioration occurring for the first 8 years of service. The models examine both within-site factors and between-site factors. Site location is found to be the primary influence on the rate of pavement deterioration. Overlay thickness and the amount of cracking prior to rehabilitation are also determined to influence pavement deterioration at a strong statistical level. Models are provided for benchmarking the performance of pavements across Canada, for comparison with individual project designs, and for estimating the performance of designs with different overlay thickness.Key words: Canadian Long Term Pavement Performance program, roughness, pavement deterioration, site effects, asphalt overlays, benchmark, univariate analysis.

INVESTIGATION OF AUTOMOBILE ROADS PAVEMENT DETERIORATION TRENDS IN LITHUANIA

Currently there are three pavement management systems (PMS) used in Lithuania for planning and management of road maintenance and repair activities: HDM-III, HDM-4 and DAVASEMA (Lithuanian PMS). HDM pavement deterioration models are used in all of them. With the purpose of calibration and adaptation of those models in 1997 Lithuanian Pavement Deterioration Research Project was developed. The research data gathered in four years of the Project gives an opportunity to draw some conclusions on asphalt pavement deterioration in Lithuania. This article presents the main HDM asphalt pavement deterioration models, and describes the most important steps in adaptation of some input data to those models and calibration of the models to the local conditions.

INVESTIGATION OF AUTOMOBILE ROADS PAVEMENT DETERIORATION TRENDS IN LITHUANIA

Currently there are three pavement management systems (PMS) used in Lithuania for planning and management of road maintenance and repair activities: HDM-III, HDM-4 and DAVASEMA (Lithuanian PMS). HDM pavement deterioration models are used in all of them. With the purpose of calibration and adaptation of those models in 1997 Lithuanian Pavement Deterioration Research Project was developed. The research data gathered in four years of the Project gives an opportunity to draw some conclusions on asphalt pavement deterioration in Lithuania. This article presents the main HDM asphalt pavement deterioration models, and describes the most important steps in adaptation of some input data to those models and calibration of the models to the local conditions. First Published Online: 26 Jul 2012

Analysis of Deterioration Models for Lightly Loaded Thin Seal Flexible Pavements

A set of deterioration models is required to manage local government authority (LGA) pavements in a cost-effective manner; yet, most existing deterioration models have been derived for the major highways of the State Road Authority system. LGA pavements are different in terms of pavement life, the effect of the environment, loading, and expectations of performance with respect to riding quality. There is a greater emphasis on sustained light routine maintenance in LGAs. There is a need to develop models that more closely represent LGA pavements so they can be used to forecast the deterioration of the asset and to provide better guidance for rehabilitation planning. Existing pavement deterioration models, such as Highway Development Management-III (HDM-III), were examined for application to the Australian LGA pavement set. In general, such models were too complex for use in LGAs, and they also used roughness as a performance measure. Roughness is not commonly measured in LGA pavement networks. Other studies have shown that HDM-III could overestimate the deterioration of lightly loaded pavements. For this study, data on deterioration of several LGA pavements were collected and analyzed. Visual assessment data on pavement condition were captured in a “snapshot” survey of pavements of different ages. Thus, the data represent an age cross-section sample. Little or no correlation was found between any of the performance indicators and age when the full data sets were used for the analyses. Some correlation was apparent with the averaged data for each age. Even then, some of the trendlines observed indicated a performance with age, contrary to normal expectations. It appears that factors besides age have a significant influence on the behavior of LGA pavement.

ADAPTATION OF PAVEMENT DETERIORATION MODELS TO LITHUANIAN AUTOMOBILE ROADS

The article presents three-system modelling of road pavement deterioration used in Lithuania: HDM-III (Highway Design and Maintenance Standards Model), HDM-4 (Highway Development and Management System) and DAVASEMA—Lithuanian pavement management system developed by Lithuanian Road Administration and based on HDM-III models. Using research data gathered in four years of the programme the authors analyse possibilities of adapting the pavement deterioration models to Lithuanian conditions. The article describes suggested procedures for calculating of calibration coefficients for the pavement deterioration models of the highest importance: road roughness component incremental model, structural cracking initiation model and structural cracking progression model.

A steady-state solution for the optimal pavement resurfacing problem

This paper presents a solution approach for the problem of optimising the frequency and intensity of pavement resurfacing, under steady-state conditions. If the pavement deterioration and improvement models are deterministic and follow the Markov property, it is possible to develop a simple but exact solution method. This method removes the need to solve the problem as an optimal control problem, which had been the focus of previous research in this area. The key to our approach is the realisation that, at optimality, the system enters the steady state at the time of the first resurfacing. The optimal resurfacing strategy is to define a minimum serviceability level (or maximum roughness level), and whenever the pavement deteriorates to that level, to resurface with a fixed intensity. The optimal strategy does not depend on the initial condition of the pavement, as long as the initial condition is better than the condition that triggers resurfacing. This observation allows us to use a simple solution method. We apply this solution procedure to a case study, using data obtained from the literature. The results indicate that the discounted lifetime cost is not very sensitive to cycle time. What matters most is the best achievable roughness level. The minimum serviceability level strategy is robust in that when there is uncertainty in the deterioration process, the optimal condition that triggers resurfacing is not significantly changed.

Estimating Heavy Vehicle Road Wear Costs for Bituminous-Surfaced Arterial Roads

This paper provides the latest estimates of the attributable heavy vehicle road wear cost (load-related road wear)—which is an approximation for the marginal cost of road wear—for thin bituminous-surfaced arterial roads in Australia. These estimates were based on the following approaches: (1) a statistical relationship between the road maintenance costs and a heavy vehicle road use variable; and (2) a pavement deterioration model that forms the basis of a load-related road wear model. These recent estimates of the attributable road wear cost all suggest that the fourth power law-based equivalent standard axle load (ESAL) road use variable can be used for attributing the road wear costs. This variable is in the form of the ESAL-km parameter for attributing construction and rehabilitation costs as well as maintenance (road wear) costs. Because all of these road costs are being attributed by the same parameter, this will bring attention to the deficiency of the traffic loads being simply characterized by the fourth power law that forms the basis of ESAL estimation. However, no simple replacement for the fourth power law is currently available in Australia for the attribution of pavement wear costs.

ADAPTATION OF PAVEMENT DETERIORATION MODELS TO LITHUANIAN AUTOMOBILE ROADS

Abstract The article presents three-system modelling of road pavement deterioration used in Lithuania: HDM-III (Highway Design and Maintenance Standards Model), HDM-4 (Highway Development and Management System) and DAVASEMA—Lithuanian pavement management system developed by Lithuanian Road Administration and based on HDM-III models. Using research data gathered in four years of the programme the authors analyse possibilities of adapting the pavement deterioration models to Lithuanian conditions. The article describes suggested procedures for calculating of calibration coefficients for the pavement deterioration models of the highest importance: road roughness component incremental model, structural cracking initiation model and structural cracking progression model.

Artificial Neural Network Modeling of Pavement Performance using Expert Judgement

ABSTRACT The application of Artificial Neural Networks (ANN) in pavement performance modeling for a case where the period of data collection is relatively short is presented in this report. For this purpose, the Canadian Long-Term Pavement Performance (C-LTPP) database is used to develop a roughness prediction model. To overcome the deficiency of poor predictive results beyond the period of the available data, a simple approach based on expert judgements is proposed. The result of the proposed deterministic approach is compared with those of the Bayesian probabilistic method. The latter is developed by the Canadian Strategic Highway Research Program. It is shown that, for the case under study, the neural network roughness prediction model utilizing expert judgements seems to be more realistic when compared to the Bayesian model. The reason is attributed to the fact that for the neural network model, the expert judgements are used only as a complement to the existing data. Therefore, neither data is extrapolated nor a judgement is used when actual data exists.

Artificial Intelligence–Based Decision Support Technologies in Pavement Management

The field of road pavement engineering has seen an explosion of artificial intelligence–based applications since the late 1980s. Such applications are found at key stages of the decision process involved in pavement management. In the analysis phase, they perform pavement diagnosis and deterioration modeling tasks. In the design phase, they enable rehabilitation needs to be assessed and contribute to the identification and selection of maintenance actions. Finally, in the choice phase, they are applied for priority programming of rehabilitation and maintenance. They either represent alternative approaches to existing systems or collaborate to make the overall system more efficient. The purpose of this article is to summarize the findings of up‐to‐date research articles concerning the application of artificial intelligence to pavement management and to illustrate the potential such tools can offer to pavement engineers. Artificial intelligence techniques include expert systems, artificial neural networks, fuzzy logic, genetic algorithms, and hybrid systems.

Statistical Model of Pavement Rutting in Asphalt Concrete Mixes

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.

Development of a Pavement Rutting Model from Experimental Data

Properly specified pavement deterioration models are an important input for the efficient management of pavements, the allocation of cost responsibilities to various vehicle classes for their use of the highway system, and the design of pavement structures. However, most empirical deterioration progression models developed to date have had limited success. This paper is concerned with the development of an empirical rutting progression model using experimental data. The data used in this paper comprise an unbalanced panel data set with more than 14,000 observations taken from the AASHO Road Test. The salient features of the model specification are (1) the model eschews conventional (predefined) axle load equivalencies and structural numbers in favor of relationships determined entirely by the data itself; (2) a thawing index variable has been incorporated to capture the effects of the environmental factors in the AASHO Road Test; and (3) the model predicts incremental changes in rut depth, which is particularly advantageous in a pavement management context. The specified model is nonlinear in the variables and the parameters and is estimated using both fixed-effects and random-effects specifications to account for unobserved heterogeneity. The estimation results show that the model replicates the pavement behavior well, that the inclusion of an environmental variable is important to avoid biases in other parameters, and that the size of the unobserved heterogeneity nonlinear specification leads to significant differences between parameter estimates among the two wheel paths rutting models.