Graduation Year
2004
Document Type
Dissertation
Degree
Ph.D.
Degree Granting Department
Civil Engineering
Major Professor
Jian John Lu, Ph.D.
Co-Major Professor
Manjriker Gunaratne, Ph.D.
Committee Member
Ram Pendyala, Ph.D.
Committee Member
Edward Mierzejewski, Ph.D.
Committee Member
Lihua Li, Ph.D.
Keywords
transition probability, logistic model, deterioration, deterministic, stochastic process
Abstract
Timely identification of undesirable pavement crack conditions has been a major task in pavement management. Up to date, myriads of pavement performance models have been developed for forecasting pavement crack condition with the traditional preferred techniques being the use of regression relationships developed from laboratory and/or field statistical data. However, it becomes difficult for regression techniques to predict the crack performance accurately and robustly in the presence of a variety of tributary factors, high nonlinearity, and uncertainty. With the advancement of modeling techniques, two innovative breeds of models, Artificial Neural Networks and Markov Chains, have drawn increasing attention from researchers for modeling complex phenomena like the pavement crack performance. In this study, two distinct models, a recurrent Markov chain, and an Artificial Neural Network (ANN), were developed for modeling the performance of pavement crack condition with time. A logistic model was used to establish a dynamic relationship between transition probabilities associated with the pavement crack condition and the applicable tributary variables. The logistic model was then used conveniently to construct a recurrent Markov chain for use in predicting the crack performance of asphalt pavements in Florida. Florida pavement condition survey database were utilized to perform a case study of the proposed methodologies. For comparison purpose, a currently popular static Markov chain was also developed based on a homogeneous transition probability matrix that was derived from the crack index statistics of Florida pavement survey database. To evaluate the model performance, two comparisons were made; (1) between the recurrent Markov chain and the static Markov chain; and (2) between the recurrent Markov chain and the ANN. It is shown that the recurrent Markov chain outperforms both the static Markov chain and the ANN in terms of one-year forecasting accuracy. Therefore, with high uncertainty typically experienced in the pavement condition deterioration process, the probabilistic dynamic modeling approach as embodied in the recurrent Markov chain provides a more appropriate and applicable methodology for modeling the pavement deterioration process with respect to cracks.
Scholar Commons Citation
Yang, Jidong, "Road Crack Condition Performance Modeling Using Recurrent Markov Chains And Artificial Neural Networks" (2004). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/1310
