Many researchers have developed different forms of the IRI model to measure the smoothness. Regardless of which forms, the major factors to contribute the IRI model can be roughly divided into two categories: variables related to distress, and variables related to non-distress. This paper investigates how the variables impact the pavement smoothness by using Long-Term Pavement Performance (LTPP) program data. The main contributions of this paper are (1) comprehensively analyzes the smoothness models in order to identify the independent factors of IRI model, (2) present the application of data snooping method to detect and remove the outlier involved in the LTPP database, (3) evaluate the three models that describe the relationship between the pavement age and the IRI values using the 10 test sections of the LTPP program database, and then recommend best one, (4) evaluate the effect of the different elements of site factors and climate condition to IRI model, and suggest the choice of appropriate site factor and climate variables, (5) suggest the data combination and data interpolation on the basis of the LTPP data set, and (6) establish the prediction of IRI model using linear regression model. The results of the regression analyses have demonstrated that those distresses identified in this paper have directly related to incremental changes in the IRI and a significant effect on incremental changes in IRI with time. The results from this study have also shown that the regressed model has a slight enhancement relative to the JPCP model. Hopefully, these investigation and studies can be used for the management, design, and/or evaluation for flexible pavement.
Abstract