Vol16 No.8: 1975-1985
【Title】Fuzzy-support vector machine geotechnical risk analysis method based on Bayesian network
【Author】LIU Yang1; ZHANG Jian-jing1*; ZHU Chong-hao1; XIANG Bo2; WANG Dong2
【Addresses】1 School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China; 2 Sichuan Provincial Transport Department Highway Planning, Survey, Design and Research institute, Chengdu 610041, China
【Corresponding author】ZHANG Jian-jing
【Citation】Liu Y, Zhang JJ, Zhu CH, et al. (2019) Fuzzy-support vector machine geotechnical risk analysis method based on Bayesian network. Journal of Mountain Science 16(8). https://doi.org/10.1007/s11629-018-5358-7
【Abstract】Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the Fuzzy-SVM (support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover, the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors.
【Keywords】Geotechnical evaluation; Overfitting problem; Bayesian network; Prior knowledge; Fuzzy set theory; Support vector machine