Development of pavement roughness master curves using Markov Chain
Saeid Alimoradi, Amir Golroo, Seyed Mohammad Asgharzadeh
Abstract
Nowadays, probabilistic prediction models commonly represented by Markov Chain Process (MCP) attract more attention in pavement management systems. Additionally, roughness condition indices provide a broad judgment. One element that contributes the most to the roughness progression is the initial roughness of pavements which has not been considered in the prediction models developed by MCP. On the other hand, the prediction results of MCP, which address the whole pavement network as an average, are inconvenient to be deployed in decision-making programmes. This paper utilised MCP to forecast pavement roughness regarding its initial value. International Roughness Index (IRI), extracted from the long-term pavement performance (LTPP) database, was selected to be analyzed. Based on M&R history, four major families, including 1770 pavement sections in total, were introduced. The prediction process of MCP was modified led to the direct prediction of IRI values instead of the deteriorated portion of the pavement sections. A framework, which had derived from the concept of Master Curves, is proposed to pave the way for the optimisation programmes to be applied to the MCP results. The Root Mean Squared Errors (RMSE) of the composed master curves were significantly low; the average RMSE was 0.00675.