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 the remaining service life (RSL) of pavement is essential for the design and construction of roads, mobility planning, transportation modeling as well as road management syst...

http://ira.lib.polyu.edu.hk/bitstream/10397/80499/1/Karballaeezadeh_Prediction_Service_Pavement.pdf

Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)

Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks

Airport pavement management systems (APMS) are computer-based decision support systems that can be used by the agencies running airports to determine cost-effective maintenance and rehabilitation strategies to preserve the various pavement structures (runways, taxiways, etc) which are a critical component of these facilities In this paper, we describe the main elements of APMS and review existing systems

Airport pavement management systems: an appraisal of existing methodologies

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.

Stochastic Modeling of Pavement Performance

Papers presented at this session include: concepts of pavement performance prediction and modeling (lytton,rl); proposed development of pavement performance prediction models from shrp/ltpp data (rauhut,jb and gendell,ds); predictive pavement condition program in the Washington state pavement management system (jackson,nc, kay,rk and peters,aj); a rating system for unsurfaced roads to be used in maintenance management (eaton,ra, gerard,s and dattilo,rs); pavement performance prediction and risk modelling in rehabilitation budget planning : a markovian approach (cook,wd and kazakov,a); development of pavement performance curves for the Iowa Department of Transportation (cable,jk and suh,yc); a Norwegian model for prediction of pavement deterioration (bertelsen,d); pavement performance prediction model (gschwendt,i, poliacek,i and lehovec,f); mn/dot's implementation of a pavement life prediction model (hill,ld); impacts of studded tyres and their role in pavement management (isotalo,j). For the covering abstract of the conference see IRRD 807044.

Pavement performance prediction ---second north american conference on managing pavements, 2-6 november 1987, royal york hotel, toronto, ontario, canada

D S Gendell

Papers

Pavement performance prediction ---second north american conference on managing pavements, 2-6 november 1987, royal york hotel, toronto, ontario, canada