Jiandong Huang1, Jia Zhang1, Yuan Gao2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 805-821, 2022, DOI:10.32604/cmes.2022.017792
Abstract
Pervious concrete (PC) is at risk of clogging due to the continuous blockage of sand into it during its service time. This study aims to evaluate and predict such clogging behavior of PC using hybrid machine learning techniques. Based on the 84 groups of the dataset developed in the earlier study, the clogging behavior of the PC was determined by the algorithm combing the SVM (support vector machines) and particle swarm optimization (PSO) methods. The PSO algorithm was employed to adjust the hyperparameters of the SVM and verify the performance using 10-fold cross-validation. The predicting results of the developed model… More >
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