Research on prediction performance of multiple monitoring points model based on support vector machine. Issue 1 (March 2020)

• Record Type: Journal Article
• Authors: Min, Kaiyi; Li, Yanlong; Yin, Qiaogang; Wen, Lifeng
• Is Part Of: IOP conference series. Volume 794:Issue 1(2020)
• Publication Date: 2020-03
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/794/1/012038
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The multiple monitoring points model is an important means of dam structure health monitoring. Combined with the strong nonlinear mapping capability of support vector machine, the fitting and prediction accuracy of the model is further improved. According to the point selection of the multiple monitoring points SVM model, five kinds of testing schemes are designed. The influence of points selection on the prediction performance of the model is verified by case analysis. The results show that the predictive ability of multiple monitoring points model based on SVM is greatly affected by the correlation degree of the monitoring points. Therefore, it is very important to reasonably select the point data with high similarity as the training samples for the effective prediction of deformation monitoring model.