基于动力指纹和Bayes数据融合的桥梁损伤识别

受测量噪声的影响,采用单-指标评价桥梁安全容易产生误判,因此提出-种基于Bayes理论的桥梁损伤识别方法.该方法将识别过程分解为损伤定位识别与损伤定量识别两部分,首先采用Bayes公式融合归-化的动力指纹,进行损伤位置识别,进而提取损伤处的动力指纹构建Bayes网络,计算各节点的条件概率,从而识别损伤程度.通过简支梁数值模拟验证了该方法具有良好的抗噪性,尤其能够对小损伤准确定位,对程度差别小的损伤准确分类....

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Bibliographic Details
Published in东北大学学报（自然科学版） Vol. 38; no. 2; pp. 290 - 294
Main Author 孙爽 梁力 李明 李鑫
Format Journal Article
LanguageChinese
Published 东北大学 资源与土木工程学院,辽宁 沈阳,110819 2017
Subjects
Bayes网络
动力指纹
数据融合
桥梁损伤识别
测量噪声
桥梁损伤识别
动力指纹
data fusion
bridge damage identification
measurement noise
测量噪声
dynamic fingerprint
Bayes网络
数据融合
Bayesian network
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ISSN1005-3026
DOI10.3969/j.issn.1005-3026.2017.02.028

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Summary:受测量噪声的影响,采用单-指标评价桥梁安全容易产生误判,因此提出-种基于Bayes理论的桥梁损伤识别方法.该方法将识别过程分解为损伤定位识别与损伤定量识别两部分,首先采用Bayes公式融合归-化的动力指纹,进行损伤位置识别,进而提取损伤处的动力指纹构建Bayes网络,计算各节点的条件概率,从而识别损伤程度.通过简支梁数值模拟验证了该方法具有良好的抗噪性,尤其能够对小损伤准确定位,对程度差别小的损伤准确分类.
Bibliography:21-1344/T
Using a single index to evaluate bridge safety is likely to draw a false conclusion due to the effect of measurement noise. Thus,an identification method o f bridge damage based on the Bayes theory was proposed, which includes location identification and degree identification. F irst, the Bayes formula was used to fuse the normalized dynamic fingerprints and identify the damage locations. Then,only the fingerprints of damaged locations were extracted to construct the Bayesian network and the conditional probability o f every node was calculated in order to identify the damage degrees. By simulating a simple beam, the result shows that the proposed method has a good anti-noise performance, especially can accurately locate tiny damages and distinguish the damages with slight differences.
SUN Shuang,LIANG Li,LI Ming,LI Xin（School of Resources ＆ Civil Engineering,Northeastern University, Shenyang 110819, China.）
bridge damage identification; dynamic fingerprint; data fusion; Bayesian network; measurement n
ISSN:1005-3026
DOI:10.3969/j.issn.1005-3026.2017.02.028