Discrimination Model and Application of Sand Liquefaction
The liquefaction of sand soil causes the bearing capacity of the foundation to decrease.Reasonable judgment of the degree of sand liquefaction is of great significance to prevent and control disasters such as foundation subsidence.Drawing on machine learning methods,30 sets of sand liquefaction data samples were selected to establish a sand liquefaction discrimination model improved by particle swarm optimization algorithm,and compared with SVM and BP sand liquefaction discrimination model.The results show that the LSSVM model is optimized by the PSO algorithm and determines the regularization parameter to be 323.125247535 and the kernel parameter to be 1.0150532465.For 15 sets of training samples,the PSO-LSSVM sand liquefaction discrimination model and SVM sand liquefaction discrimination model have a return accuracy of 100%,and the BP sand liquefaction discrimination model has a return accuracy of 93.3%.For the five groups of test samples,the prediction accuracy of the PSO-LSSVM sand liquefaction discrimination model was 100%,while the prediction accuracy of the SVM sand liquefaction discrimination model and the BP sand liquefaction discrimination model was 80%.In the prediction of sand liquefaction in the Yellow River Basin,the PSO-LSSVM sand liquefaction discrimination model has higher prediction accuracy and can guide engineering and technical personnel to predict the state of sand and formulate prevention and control measures.
NETL
NSTL
万方数据
