The adoption of a support vector machine optimized by GWO to the prediction of soil liquefaction

Establishing a prediction model of soil liquefaction is an effective way to evaluate the site's quality and prevent the relevant loss caused by the earthquake. Considering the complexity of the liquefaction mechanism and the disadvantage of shear wave not being able to test the type of soil, the standard penetration test (SPT) data and the grey wolf optimization (GWO) algorithm were applied to try to improve the prediction accuracy of the SVM model in this paper. First, the optimal value of C and g of SVM was calculated and selected by iterating the GWO; then, the selected parameters were submitted into the SVM to train the prediction model with the training set; finally, the initial parameter of GWO was judged and updated by testing the test set and evaluating whether the performance of trained model until the goal of accuracy was meet. Besides, the GWO-SVM based on the dataset without the parameter of the shear wave velocity was also trained and tested to prove the advantage of combining the SPT data and shear wave data. It was indicated that the GWO algorithm could not only improve the accuracy of SVM fitting and optimize the performance of the prediction but also can fasten the operation; combining the SPT data and shear wave data was able to improve the prediction accuracy.

Automated summary

Establishing a prediction model of soil liquefaction is crucial for evaluating site quality and preventing earthquake-related losses. This study utilized SPT data and the GWO algorithm to improve the accuracy of the SVM model. By training the model with the training set and updating parameters with the test set, the GWO algorithm was able to enhance accuracy and optimize performance, ultimately showing the advantage of combining SPT and shear wave data for improved prediction accuracy.