Abstract In view of the disadvantages of the SVM model, such as difficulty in parameter selection and single-point data modeling in the field of settlement prediction of foundation pit, we establish a neighbor-point PSO-SVM model. Selecting the PSO-SVM model for the optimal training sample quantity research, the results show that short-term samples have the best prediction effect. We introduce the settlement deformation value of neighbor points as a factor affecting the settlement of foundation pit into the improved PSO-SVM model. The example shows that the fitting accuracy of the PSO-SVM model considering the neighbor points under short-term sample data is better than that of the PSO-SVM model. The prediction accuracy is poor under medium and long-term sample conditions. Aiming at this shortcoming, we propose a combination of multi-scale one-dimensional wavelet decomposition function and Cauchy distribution function to improve the PSO-SVM model that takes into account the neighbor points. The experimental results show that the improved PSO-SVM model effectively solves the difficulty in parameter selection and single-point data modeling. The model is suitable for the prediction of settlement deformation under different sample sizes, and has high prediction accuracy.
YUAN Zhiming,LI Peihong,LIU Xiaosheng. Study on the Application of Improved PSO-SVM Model Considering Neighbor-Point in the Settlement Prediction of Foundation Pit[J]. jgg, 2021, 41(3): 313-318.
YUAN Zhiming,LI Peihong,LIU Xiaosheng. Study on the Application of Improved PSO-SVM Model Considering Neighbor-Point in the Settlement Prediction of Foundation Pit[J]. jgg, 2021, 41(3): 313-318.
