A Probabilistic and Adaptive Approach to Modeling Performance of Pavement Infrastructure
Zheng Li and Zhanmin Zhang, University of Texas at Austin, August 2007, 120 pp. (167267-1)
Accurate prediction of pavement performance is critical to pavement management agencies. Reliable and accurate predictions of pavement infrastructure performance can save significant amounts of money for pavement infrastructure management agencies through better planning, maintenance, and rehabilitation activities. Pavement infrastructure deterioration is a dynamic, complicated, and stochastic process with its outcome as the aggregated impact from various factors such as traffic loading, environmental condition, structural capacities, and some unobserved factors. However, existing performance prediction models are still constrained by inadequate consideration of the dynamic and stochastic characteristics of pavement infrastructure deterioration.
The goal of this research is to develop a probabilistic and adaptive methodological framework that is capable of capturing the dynamic and stochastic nature of pavement deterioration processes. The ordered probit model and the sequential logit model as probabilistic models are proposed to directly predict the performance of pavements in terms of their condition states by relating the performance to the structural, traffic, and environmental variables. The proposed probabilistic models were pilot-tested with pavement performance data collected during the AASHO Road Test, yielding good prediction results. In addition, these models were further enhanced as mechanistic-empirical models and compared with existing performance models. The comparison results show that the proposed models yield better predictions than the previously developed models in terms of prediction accuracy. Then, a structural state space model is proposed to characterize the dynamic nature of pavement deterioration. The proposed structural model has the capability of adaptively updating the performance model with new inspection data by taking advantage of a polynomial trend filter and the Kalman filter algorithm. The results from a simulation case study indicate that the adaptive algorithm is robust and responsive to structural deviations of the pavement deterioration process. Therefore, it is concluded that the proposed probabilistic and adaptive methodological framework is reliable and robust to accurately predict pavement performance.
Keywords: Pavement Performance, Pavement Deterioration, Pavement Infrastructure
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