Effects of maintenance, traffic and climate condition on International Roughness Index of flexible pavement

ABSTRACT

This paper focuses on developing the predictive models for International Roughness Index (IRI) of flexible pavement using several soft computing systems. About 564 sections with 4229 observations of the flexible pavement from the database of the long-term pavement performance (LTPP) program were used for developing and validating the prediction models. Equivalent single axle load (ESAL), structural number (SN), thickness, resilient modulus (M_R) of the base layer, age, annual truck volume trend, total annual precipitation, freezing index, and maintenance treatments were the predictive attributes used in models. Various statistical criteria were used for evaluating the performance of the developed models, comprehensively. The results showed that the models developed in the current study are fairly promising tools for the prediction of IRI and capable of representing the complex relationship between the IRI and the predictive attributes selected. The models created through the Gradient Boosting Method (GBM) and Random Forest (RF), which were markedly outperformed the results of other methods, produced a correlation coefficient (R) greater than 0.90 and mean absolute error lower than 0.2. Furthermore, the SN was found to be the most important parameter for the IRI according to the variable importance score of the best performing prediction model.

KEYWORDS:

• Gradient Boosting Method (GBM)
• International Roughness Index (IRI)
• K-Nearest Neighbours Method (K-NN)
• M5P
• Random Forest (RF)
• Sequential Minimal Optimization for Regression (SMOreg)

Disclosure statement

No potential conflict of interest was reported by the author(s).