Rate Of Pavement Deterioration Research Articles

Development and Validation of Pavement Performance Models for the State of Maryland

The Maryland State Highway Administration (SHA) is in the process of restructuring and improving its existing program for pavement system preservation. As part of this effort, SHA revised the process for its pavement management system (PMS) and developed a new software tool to handle the data analysis requirements. The authors present the analysis that led to the development of the pavement performance models of the PMS simulation that forecast pavement condition and prescribe maintenance and rehabilitation treatments. SHA had used hundreds of performance models to accommodate every state region, traffic level, and pavement type, treatment, and condition. To reduce the number of existing performance models, a statistical analysis was performed with the international roughness index (IRI) to identify groups with similar characteristics by pavement and treatment type. Once distinct groups were defined, the authors used a novel histogram-based analysis method to develop performance models based on IRI. This type of analysis was necessary to estimate realistic pavement deterioration rates without regard to pavement age. Independent pavement sections with good correlation were used to validate the performance models developed with the histogram-based analysis. In addition, the network-level prediction of pavement performance yielded satisfactory results with the developed performance models.

Iterative Linear Approach for Nonlinear Nonhomogenous Stochastic Pavement Management Models

An iterative linear stochastic pavement management model is proposed that deploys a nonhomogenous discrete-time Markov chain for predicting the future pavement conditions for a given pavement network. A nonhomogenous transition matrix is constructed to incorporate both the pavement deterioration rates and improvement rates. The pavement deterioration rates are simply the transition probabilities associated with the deployed pavement states. The improvement rates are mainly the maintenance and rehabilitation variables representing the deployed maintenance and rehabilitation actions. A decision policy is formulated to identify the optimal set of maintenance and rehabilitation actions and their respective timings, and to provide the optimal level of maintenance and rehabilitation funding over an analysis period. The nonhomogenous Markov chain allows for a distinct maintenance and rehabilitation plan (matrix) for each time interval (transition). However, the total number of maintenance and rehabilitation variables will substantially increase depending on the length of the deployed analysis period. The resulting optimum model is associated with a nonlinearity order that is equal to the number of time intervals within the specified analysis period. Solving a nonlinear model with a large number of variables is a very complex task. Alternatively, instead of solving a single nonlinear problem, a series of linear problems are formulated and iteratively solved wherein the optimal solution for one problem becomes the input for the next one. The sample results obtained from the iterative linear approach indicate the effectiveness of the proposed stochastic management model in predicting future pavement conditions.

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.

Adequate data collection frequency and gauging length for surface condition rating in Alberta

In order to ensure safe and comfortable riding, increase network serviceability, and slow pavement deterioration rate, two different strategies are being used simultaneously by transportation departments: routine maintenance and major rehabilitation. Within Alberta Infrastructure, the decision between these alternatives is made based on the pavement condition. Data are collected frequently for the purpose of pavement evaluation. Experienced engineers and technicians normally decide the most appropriate data collection procedure and gauging length associated with it.Key words: pavement, data collection, evaluation, gauging length, condition rating.

Predicting Pavement Deterioration Modes Using Waveband Analysis

Road roughness is the result of a chain of distress mechanisms, and it combines the effects of various modes of pavement deterioration. The common measure of roughness is the international roughness index (IRI). The IRI represents the functional performance of road pavements and serves as an indicator of the structural performance. However, the use of the IRI in setting priorities for maintenance and rehabilitation programs has some flaws. The IRI cannot detect the mode and rate of pavement deterioration that contribute to the surface roughness. In addition, the IRI may be a poor measure of truck ride and the dynamic wheel loads generated by the interaction of heavy vehicles and rough road surfaces. Roughness spectrum can be divided into certain wavebands, with each waveband corresponding to a certain mode of pavement deterioration. The power spectral density analysis of road profiles is used to view the distribution of the various wavelengths within the profile and to show the contribution of each waveband to roughness. Waveband analysis can help overcome the shortcomings of the IRI, and it provides information that helps road authorities in planning rehabilitation programs and managing the road network.

Effect of Neural Network Topology on Flexible Pavement Cracking Prediction

Pavement surface cracking has long been considered an important criterion for maintenance intervention because of its detrimental effects on pavement performance. Once initiated, cracking increases in severity and extent and allows water to penetrate the pavement. The water weakens the unbound layers and consequently accelerates the rate of pavement deterioration. Cracking prediction and its control are thus key components in determining the timing and cost of pavement maintenance. A neural network–based model is presented in this paper for predicting flexible pavement cracking. One‐, two‐, and three‐hidden‐layer backpropagation neural network (BPNN) topologies are investigated and their cracking‐prediction performances compared. Based on the analysis, it is concluded that for the same optimal number of processing elements, a one‐hidden‐layer BPNN topology may be sufficient in achieving satisfactory results in cracking prediction; increasing the number of layers may not add any significant benefit to the performance of the model.

Development of a new asphalt pavement performance prediction model

The Ontario Pavement Analysis of Costs system has been in service for Ontario asphalt flexible pavement design and performance prediction since the early 1970s. It uses a deflection-based deterministic model for selecting the best pavement structural design alternative in terms of pavement functional and structural performance and the total life-cycle costs. However, because of the existence of uncertainties and variations in pavement design variables and parameters in the pavement deterioration models, it is not adequate to apply deterministic models to all situations of pavement management. It is therefore necessary to predict pavement performance by employing probabilistic-based models. In this paper, a new concept of system conversion between a deterministic model and a probabilistic model is discussed first. A method by which a deterministic pavement performance prediction model, such as the Ontario asphalt pavement deterioration model, can be converted into a probabilistic model is presented. A transformed probabilistic model is constructed by generating a set of time-related nonhomogeneous Markovian transition probability matrices, which is determined by Monte Carlo simulation. Each of the transition probability matrices characterizes the pavement deterioration rate for the given pavement age and traffic characteristics. A Bayesian technique is then employed to update the predicted pavement performance in terms of the pavement condition state vectors and the expected pavement condition state values by integrating additional information such as the actually measured performance data of the pavement.

Crack Sealing in Flexible Pavements: A Life-Cycle Cost Analysis

Pavement cracks are usually sealed to prevent water from entering base course layers. Experience suggests that water in pavement layers hastens pavement deterioration and, therefore, increases rehabilitation costs. Initially the Ministry of Transportation, Ontario (MTO), Canada, had limited success in sealing cracks because inappropriate materials and installation procedures were used. This setback during the initial stages of development in crack sealing raised questions about the effectiveness of crack sealing in reducing the rate of pavement deterioration. In the 1970s and 1980s MTO carried out several field studies to develop an effective crack sealing procedure and to study the influence of crack sealing on pavement distress and performance. The cost-effectiveness of this treatment was investigated as well. The results of the studies indicate that sealing cracks is a viable and cost-effective preventive maintenance treatment and can extend the service life of asphalt pavements by at least 2 years. However, for optimum benefits the crack sealing program must be implemented according to guidelines proposed. The guidelines provide a basis for the selection of suitable pavements and cracks, sealant materials, and application procedures.

Reliability-Based Processing of Markov Chains for Modeling Pavement Network Deterioration

Accurate prediction of pavement deterioration is the most important factor in the determination of pavement repair years and optimization programming of highway network maintenance. The Nonhomogeneous Markov Probabilistic Modeling Program, developed to determine pavement deterioration rates in different stages, is described. In this program the transition probability matrices (TPMs) are considered as a time-related transition process. Each element of the TPMs is determined on the basis of a reliability analysis and a Monte Carlo simulation technique. This avoids the use of the existing conventional methods, which involve taking an average subjective opinion of pavement engineers or observing a large number of multiyear pavement performance data and conducting a number of statistical calculations. As a result a series of TPMs for an individual pavement section for different stages can be determined by running the program. Furthermore, the pavement condition state in terms of a probability vector at each stage (year) is calculated. In applying the models both the predicted actual traffic (in terms of equivalent single axle loads) at each stage and the maximum traffic that the pavement can withstand at each defined pavement condition state interval are considered to be random variables. In addition, the sensitivities of pavement deterioration rates to pavement design parameters, such as traffic growth rate, subgrade strength, and material properties, are studied. Finally, an example of calculating the TPMs for a pavement section located in southeastern Ontario, Canada, is demonstrated. It shows that the sensitivities of the TPMs to traffic growth rate, subgrade deflection, and pavement thickness are significant.

Reliability-Based Processing of Markov Chains for Modeling Pavement Network Deterioration

Accurate prediction of pavement deterioration is the most important factor in the determination of pavement repair years and optimization programming of highway network maintenance. The Nonhomogeneous Markov Probabilistic Modeling Program, developed to determine pavement deterioration rates in different stages, is described. In this program the transition probability matrices (TPMs) are considered as a time-related transition process. Each element of the TPMs is determined on the basis of a reliability analysis and a Monte Carlo simulation technique. This avoids the use of the existing conventional methods, which involve taking an average subjective opinion of pavement engineers or observing a large number of multiyear pavement performance data and conducting a number of statistical calculations. As a result a series of TPMs for an individual pavement section for different stages can be determined by running the program. Furthermore, the pavement condition state in terms of a probability vector at each stage (year) is calculated. In applying the models both the predicted actual traffic (in terms of equivalent single axle loads) at each stage and the maximum traffic that the pavement can withstand at each defined pavement condition state interval are considered to be random variables. In addition, the sensitivities of pavement deterioration rates to pavement design parameters, such as traffic growth rate, subgrade strength, and material properties, are studied. Finally, an example of calculating the TPMs for a pavement section located in southeastern Ontario, Canada, is demonstrated. It shows that the sensitivities of the TPMs to traffic growth rate, subgrade deflection, and pavement thickness are significant.

Crack Sealing in Flexible Pavements: A Life-Cycle Cost Analysis

Pavement cracks are usually sealed to prevent water from entering base course layers. Experience suggests that water in pavement layers hastens pavement deterioration and, therefore, increases rehabilitation costs. Initially the Ministry of Transportation, Ontario (MTO), Canada, had limited success in sealing cracks because inappropriate materials and installation procedures were used. This setback during the initial stages of development in crack sealing raised questions about the effectiveness of crack sealing in reducing the rate of pavement deterioration. In the 1970s and 1980s MTO carried out several field studies to develop an effective crack sealing procedure and to study the influence of crack sealing on pavement distress and performance. The cost-effectiveness of this treatment was investigated as well. The results of the studies indicate that sealing cracks is a viable and cost-effective preventive maintenance treatment and can extend the service life of asphalt pavements by at least 2 years. However, for optimum benefits the crack sealing program must be implemented according to guidelines proposed. The guidelines provide a basis for the selection of suitable pavements and cracks, sealant materials, and application procedures.