Abstract
Evaluating flexible pavement performance is mandatory for managing transport infrastructure. This study focuses on modeling the relationships between international roughness index (IRI) and a total of 10 types of pavement distress, including alligator, block, wheel path length, wheel path longitudinal, non-wheel path longitudinal, transverse crackings, patches, bleeding, raveling areas, and pumping. The data recorded under the Long-Term Pavement Performance was used to develop the models. Data sets covering General Pavement Studies from seven states of the United States were used in modeling. The study used modeling approaches, including nonlinear regression analysis, multivariate adaptive regression splines, and artificial neural networks (ANN), in which IRI was the dependent variable and pavement distress was the independent variable. In the developed models, 0.516, 0.623, and 0.684 regression coefficients values were obtained for nonlinear regression analysis, multivariate adaptive regression splines, and artificial neural networks approaches, respectively. The analysis results have determined that the artificial neural networks technique performs more successfully than the other techniques. The statistical error analyses of the root mean square error, Nash-Sutcliffe coefficient of efficiency, mean absolute error, and normalized root mean square error also showed that the same modeling approach performs more successfully. With these data generated from a universally used database, it has been determined once again that ANN is the most efficient mathematical approach in modeling the relationships between surface distresses and IRI.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.