基于支持向量机的桥梁群体震害预测方法
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摘要
基于支持向量机(SVM)原理,详细介绍了其用于桥梁群体震害预测的步骤和方法 ;通过选取唐山、海城、汶川等地震中的103座桥梁的震害资料作为学习样本,建立了桥梁的震害评估SVM模型;通过比较SVM模型与BP神经网络的震害预测能力,发现SVM模型具有更高的准确率。研究表明SVM模型具有良好的推广能力,可以应用于实际的桥梁震害预测工作。
In order to evaluate the seismic fragility of bridges more scientifically and effectively,the paper introduces in detail the procedure and method of the seismic damage prediction of bridges group, based on support vector machine. The paper built a SVM model, by selecting learning samples from earthquake disaster data of 103 bridges in Tangshan earthquake, Haicheng earthaquake and Wenchuan earthquake etc. By comparing the earthquake prediction ability of the SVM model and the BP neural network, we can find that the accuracy of SVM model is higher.The study shows that the SVM model has good generalization capability and can be operated in practice.
In order to evaluate the seismic fragility of bridges more scientifically and effectively,the paper introduces in detail the procedure and method of the seismic damage prediction of bridges group, based on support vector machine. The paper built a SVM model, by selecting learning samples from earthquake disaster data of 103 bridges in Tangshan earthquake, Haicheng earthaquake and Wenchuan earthquake etc. By comparing the earthquake prediction ability of the SVM model and the BP neural network, we can find that the accuracy of SVM model is higher.The study shows that the SVM model has good generalization capability and can be operated in practice.
引文
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[4]赵钊,郭恩栋,王再荣,等.基于人工神经网络的城市桥梁震害评估方法[J].世界地震工程,2011,27(4):7-12.
[5]Cortes C,Vapnik V.Support vector networds[J].Machine Learning,1995,20:273-295.
[6]Vapnik V N.The Nature of Statistical Learning Theory[M].New York:Springer Verlag,1995.
[7]白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008.
[8]J C Platt.Using Analytic QP and Sparseness to Speed Training of Support Vector Machines[M].Cambrige:MIT Press.1999.
[9]吴昊.城市轨道交通桥梁震害预测方法研究[D].北京:北京交通大学,2009.
[10]国家质量监督检验检疫总局.GB/T 19428-2003,地震灾害预测及其信息管理系统技术规范宣贯教材[M].北京:中国标准出版社,2004.
[2]王东升,冯启民.桥梁震害预测[J].自然灾害学报,2001,10(3):113-118.
[3]朱美珍.公路桥梁震害预测的实用方法[J].同济大学学报,1994,22(3):279-283.
[4]赵钊,郭恩栋,王再荣,等.基于人工神经网络的城市桥梁震害评估方法[J].世界地震工程,2011,27(4):7-12.
[5]Cortes C,Vapnik V.Support vector networds[J].Machine Learning,1995,20:273-295.
[6]Vapnik V N.The Nature of Statistical Learning Theory[M].New York:Springer Verlag,1995.
[7]白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008.
[8]J C Platt.Using Analytic QP and Sparseness to Speed Training of Support Vector Machines[M].Cambrige:MIT Press.1999.
[9]吴昊.城市轨道交通桥梁震害预测方法研究[D].北京:北京交通大学,2009.
[10]国家质量监督检验检疫总局.GB/T 19428-2003,地震灾害预测及其信息管理系统技术规范宣贯教材[M].北京:中国标准出版社,2004.
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