Prediction of Long-Term Seawall Settlement Based on Least Squares Support Vector Machine

Abstract

In this study, the basic principle of the least squares support vector machine (LS- SVM) method was introduced systematically. The method was applied in a seawall engineering project to establish a prediction model of long-term seawall settlement based on the LS-SVM method, and the model parameters were optimized based on the genetic algorithm. Based on the monitoring data of seawall settlement during the construction of a soft foundation seawall project in Wenzhou, China, a sample was selected to train the model, and the predicted value of long-term settlement calculated by the model was compared with the measured sample value. The results show that, with highly accurate and reliable prediction results, the prediction model of long-term seawall settlement based on the LS-SVM method possesses great application value.