Research on the Settlement Prediction Model of Foundation Pit Based on the Improved PSO-SVM Model

被引：16

作者：

Song, Zhibin ^{[1
]}

Liu, Shurong ^{[2
]}

Jiang, Mingyue ^{[1
]}

Yao, Suling ^{[1
]}

机构：

[1] Hebei Normal Univ Sci & Technol, Coll Urban Construct, Qin Huangdao 066000, Peoples R China

[2] Hebei Normal Univ Sci & Technol, Sch Math & Informat Sci & Technol, Qin Huangdao 066000, Peoples R China

来源：

SCIENTIFIC PROGRAMMING | 2022年 / 2022卷

关键词：

SIMULATION; ALGORITHM;

D O I：

10.1155/2022/1921378

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper presents a settlement prediction method based on PSO optimized SVM for improving the accuracy of foundation pit settlement prediction. Firstly, the method uses the SA algorithm to improve the traditional PSO algorithm, and thus, the overall optimization-seeking ability of the PSO algorithm is improved. Secondly, the improved PSO algorithm is used to train the SVM algorithm. Finally, the optimal SVM model is obtained, and the trained model is used in foundation pit settlement prediction. The results suggest that the settling results obtained from the optimized model are closer to the actual values and also more advantageous in indicators such as RMSE. The fitting value R-2 = 0.9641, which is greater, indicates a better fitting effect. Thus, it is indicated that the improvement method is feasible.

引用

页数：9

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