Probabilistic Approach to Modeling Pavement Performance Using IRI Data

Abstract

Accurately predicting pavement performance is an essential element in road infrastructure management. Pavement performance prediction methods can be either deterministic or probabilistic, depending on the method employed to simulate the deterioration or aging process. Deterministic models predict the condition on the basis of mathematical functions of observed or measured deterioration without taking uncertainties associated with the deterioration process into consideration. Probabilistic models, on the other hand, take uncertainties into consideration and predict the condition as the probability of occurrence in a range of possible outcomes. To overcome this shortcoming of deterministic models, probabilistic approaches have been investigated by various researchers. Probabilistic methods can be summarized into three categories: econometric models, Markov Chain Process (PMC) models, and reliability analysis. This paper presents a Markov Chain-based methodological framework to characterize pavement performance in support of pavement management decision makings. The International Roughness Index (IRI) data from the National Department of Transportation in Costa Rica was used for the numerical case study to illustrate the application of the developed methodological framework. The findings from this study show that the proposed methodological framework is a viable approach to modeling pavement deterioration process.