Estimating Pavement Performance Models Using Advanced Technologies and Time Series Analysis

Abstract

This paper proposes state-space specifications of autoregressive moving average models and structural time series models as a framework to develop and estimate performance models for transportation infrastructure facilities. Time series models in state-space form fit the maintenance optimization model of Durango-Cohen and are consistent with the latent performance modeling approach of Ben-Akiva and Ramaswamy. To illustrate the proposed framework, the paper developed and estimated performance models for an asphalt pavement using pressure and deflection measurements generated by sensors and falling weight deflectometers, respectively. Analysis of the results shows that the ensuing models are consistent with physical properties of flexible pavements. The results also indicate that state-dependence may be statistically significant and further reinforce the computational and statistical advantages of the proposed framework over Markovian transition probabilities.