A self-tuning least squares support vector machine for estimating the pavement rutting behavior of asphalt mixtures

The present study proposes a new self-tuning least squares support vector machine, called MSOS-SVM, for modeling the pavement rutting behavior of asphalt mixtures. MSOS-SVM combines the least squares support vector machine (LS-SVM), the symbiotic organisms search (SOS), and chaotic maps. In this system, the LS-SVM is used to establish the relationship model between the flow number obtained from laboratory tests and the parameters specified in the asphalt mix design. SOS is used to find the best LS-SVM tuning parameters. Meanwhile, chaotic system is used to enhance the exploration and exploitation process of SOS. A total of 118 historical cases were used to establish the intelligence-prediction model. The results validate the ability of MSOS-SVM to model the pavement rutting behavior of asphalt mixtures to a relatively high level of accuracy as measured using four error indicators. The present study demonstrates that the proposed computational intelligence system is a highly beneficial decision-making tool for road designers and engineers.