Bayes Linear Regression Performance Model Depending on Experts’ Knowledge and Current Road Condition

Abstract

Abstract Strategic and long-term planning in pavement management systems relies primarily on performance prediction models to ensure efficient and forward-looking management and to set present and future budget requirements. In many developing countries, roads face increasing damage because of the lack of regular maintenance. This reinforces the need to develop a system to predict the deterioration of roads in order to determine the optimal intervention strategies for the road network. This article suggests a Bayesian regression method to develop a performance model for cases when archived pavement data are not available, and this would use expert knowledge as a prior distribution. As such, experts who have worked for a long time with the road and transportation agencies have been interviewed to develop a portion of the input data. Posterior distribution was calculated using the likelihood estimation function based on road condition inspections according to the predefined protocol. The results were prediction models of pavement deterioration based on a mixture of a few onsite inspections interacting with expert knowledge.