The PSO optimisation SVM prediction model for the asphalt pavement environment and service fatigue life
by Yu Sun; Dongpo He; Jun Li
International Journal of Information and Communication Technology (IJICT), Vol. 20, No. 4, 2022

Abstract: In order to improve the accuracy of prediction by support vector machine (SVM), parameter optimisation of SVM is an important part of asphalt pavement life prediction. In this paper, a particle swarm optimisation support vector machine (PSO_SVM) method was proposed to predict the fatigue life of SBS modified asphalt mixture. This method combines SVM with particle swarm optimisation (PSO), makes full use of SVM's unique advantages in dealing with small sample regression problems and PSO global search optimisation, improves convergence speed, and achieves depth and breadth optimisation. Experimental results show that this method improves the parameter selection efficiency of SVM, and the prediction results are more accurate than those of ANN and SVM.

Online publication date: Wed, 01-Jun-2022

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