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.

A significant issue that researchers have been addressing recently is the recycling of various waste materials from different industries. Annually, millions of tons of waste are released. Pollution and safe, cost-effective disposal are critical issues with these waste materials. This study aims to investigate usability of concrete waste (COW) and ceramic waste (CEW) as filler material in Hot Mix Asphalt (HMA). Firstly, in this direction, a series of tests were conducted to determine the optimum bitumen content. Five different bitumen contents (i.e., 4.5%, 5%, 5.5%, 6%, and 6.5%) were used to prepare asphalt concrete samples. The Marshall Stability (MS) test was then performed on the prepared samples using various filler ratios, including limestone (LS), concrete waste (COW), and/or ceramic waste (CEW). Selecting the series of asphalt with 4% filler that has provided the maximum stability, COW or CEW was replaced with LS filler at rates of 25%, 50%, 75%, and 100%. Consequently, the use of COW or CEW as a mineral filler in HMA was found to be suitable.