悬臂梁损伤的支持向量机识别研究

英文篇名：Damage detection of cantilever beam Based on Support Vector Machine
中文刊名：四川建材
英文刊名：Sichuan Building Materials
作者：尹鹏飞 ; 俞晓勋 ; 夏敬婵 ; 刘雪松
英文作者：YIN Pengfei ; YU Xiaoxun ; XIA Jingchan ; LIU Xuesong (1.Henan Xingsheng Construction Engineering ltd zhengzhou 450000 ; China ; 2.Shanghai Kangqiao Construction Engineering ltd Shanghai 201315 ; China ; 3.College of Civil Engineering Henan Polytechnic University Jiaozuo 454003 ; China ; 4.Henan First Construction Engineering Group ltd zhengzhou 450000 ; China)
中文关键词：损伤识别 ; 支持向量机 ; 核函数 ; 模态频率
英文关键词：Damage detection ; Support vector machine ; Kernel function ; Model frequency
出版日期：2011-02-11
机构：河南兴盛建筑工程有限公司;上海康桥建设工程有限公司;河南理工大学土木工程学院;河南第一建筑工程集团有限公司;
年：2011
期：01
出版单位：四川建材

摘要

针对结构损伤识别中,损伤与其影响因素之间的复杂非线性关系,提出了结构损伤识别的支持向量机方法。支持向量机是一种基于统计学理论的机器学习算法,本文以模态频率作为损伤标识量,通过支持向量机建立了损伤程度和频率之间的支持向量机模型,并以悬臂梁的损伤为例进行了计算分析,结果表明提出的方法是科学,可行的。
In allusion to the complex nonlinear relationship between the damage and influencing factors with regard to structural damage detection was proposed damage identification of support vector machines.SVM is a machine learning algorithms based on the statistical theory,we used the modal frequency as the amount of damage in this paper,established the svm-model between the degree of injury and the modal frequency through support vector machine and calculated by an scathing example of cantilever beam.The result shows that the proposed method is scientific and feasible.

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