Comparison of Master Sigmoidal Curve and Markov Chain Techniques for Pavement Performance Prediction

Recently, highway agencies have become in need to enhance their pavement management systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this paper focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the international roughness index (IRI) as a pavement performance indicator. The IRI data obtained from the long-term pavement performance (LTPP) program were used to evaluate and compare both methods. In this paper, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.32 to 5.12 m/km were selected. Results showed that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio of about 65 percent between the predicted results of the two modeling techniques.