基于贝叶斯网络的Fuzzy-SVM路基震害预测模型*

doi:10.16265/j.cnki.issn 1003-3033.2021.11.024

中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (11): 171-178.doi: 10.16265/j.cnki.issn 1003-3033.2021.11.024

基于贝叶斯网络的Fuzzy-SVM路基震害预测模型^*

刘阳^1,2 讲师, 张建经³ 教授, 罗宏森^1,2 副教授, 于海莹¹ 副教授, 向波⁴ 教授级高级工程师

1 四川师范大学公共安全与应急研究院,四川成都 610066;
2 四川师范大学工学院,四川成都 610101;
3 西南交通大学土木工程学院,四川成都 610031;
4 四川省公路规划勘察设计研究院有限公司,四川成都 610041

收稿日期:2021-08-19 修回日期:2021-10-13 出版日期:2021-11-28 发布日期:2022-05-28
作者简介:刘阳 (1993—),男,四川成都人,博士,讲师,主要从事岩土工程防灾减灾、地质灾害风险分析及预警等方面的研究。E-mail:ly@sicnu.edu.cn。
基金资助:
国家重点研发计划项目(2017YFC0504901);四川省教育厅重点项目(18ZA0406);四川交通建设科技项目(2016B2-2)。

Fuzzy-SVM prediction model of subgrade seismic damage based on Bayesian network

LIU Yang^1,2, ZHANG Jianjing³, LUO Hongsen^1,2, YU Haiying¹, XIANG Bo⁴

1 Institute of Public Safety and Emergency Response, Sichuan Normal University, Chengdu Sichuan 610066, China;
2 Institute of Technology, Sichuan Normal University, Chengdu Sichuan 610101, China;
3 School of Civil Engineering, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
4 Sichuan Highway Planning, Survey, Design and Research Institute Ltd., Chengdu Sichuan 610041, China

Received:2021-08-19 Revised:2021-10-13 Online:2021-11-28 Published:2022-05-28

摘要/Abstract

摘要： 为解决现有路基震害预测方法主观性强且无法考虑非线性特征的问题,以贝叶斯网络(BN)为框架,将工程经验与历史路基震害样本融合,改进网络参数求解方法,建立一种基于BN的模糊(Fuzzy)-支持向量机(SVM)路基震害预测模型。利用Fuzzy理论求解BN参数的先验概率,同时利用SVM求解BN参数的实际样本潜在概率;基于贝叶斯原理,将先验概率与实际样本潜在概率融合,得到既满足震害工程经验又体现历史震害样本中非线性特性的预测模型。结果表明:将提出的预测模型应用于汶川地震影响区的42个路基隐患点,预测准确率为80.95%。该模型在小样本情况下较传统机器学习方法(以SVM为代表)精度更高;并且,该模型在路基属性不完整的情况下也能有效预测震害等级。

关键词: 贝叶斯网络(BN), 路基震害, 预测模型, 模糊(Fuzzy)-支持向量机(SVM), 先验知识

Abstract: In order to address the problem of strong subjectivity and lack of consideration of nonlinear characteristics for existing subgrade earthquake damage prediction methods, Fuzzy-SVM prediction model based on BN was established by integrating engineering experience with historical seismic damage samples and by improving network parameter solving method. Fuzzy theory was used to solve prior probability of BN parameters, and SVM was used to solve their actual sample potential probability. Then, based on Bayesian principle, prior probability and actual sample potential probability were fused to obtain a prediction model which not only were consistent with engineering experience of earthquake damage, but also reflected nonlinear characteristics of historical samples. The results show that the prediction model features a accuracy rate of 80.95% when being applied to 42 subgrade hidden danger points in Wenchuan earthquake affected area. It has a higher accuracy than traditional machine learning method (represented by SVM) in the case of small samples. Moreover, it can effectively predict earthquake damage level when subgrade attributes are incomplete.

Key words: Bayesian network (BN), subgrade seismic damage, prediction model, fuzzy-support vector machine (SVM), prior knowledge

中图分类号:

X915.5

刘阳, 张建经, 罗宏森, 于海莹, 向波. 基于贝叶斯网络的Fuzzy-SVM路基震害预测模型^*[J]. 中国安全科学学报, 2021, 31(11): 171-178.

LIU Yang, ZHANG Jianjing, LUO Hongsen, YU Haiying, XIANG Bo. Fuzzy-SVM prediction model of subgrade seismic damage based on Bayesian network[J]. China Safety Science Journal, 2021, 31(11): 171-178.

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基于贝叶斯网络的Fuzzy-SVM路基震害预测模型^*

Fuzzy-SVM prediction model of subgrade seismic damage based on Bayesian network

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