基于支持向量机的张弦梁损伤识别试验
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摘要
对单榀张弦梁的索力损失和腹杆损伤进行了试验研究。对拉索加载不同程度的预应力来模拟索的预应力损失,利用环境脉动和冲击激励,通过采用Fourier变换或小波变换求得索的频率来计算施加在索上的预应力值。试验结果表明,该方法可以有效地监测索预应力。对张弦梁的上部腹杆进行了环境脉动下的损伤试验,对不同的杆件沿径向进行相应程度的截面切割用以模拟不同程度的损伤状态。对加速度样本进行小波包分解得到特征向量,利用支持向量机对特征向量进行损伤分类,验证了支持向量机方法用于损失识别的有效性。当支持向量机和主成分分析结合后,试验的损伤识别效果有明显的提高。
The cable prestressing loss and web members damage experiment of a beam string structure was carried out.Different prestressing forces were exerted on the cable to simulate cable prestressing loss,the prestressing forces were calculated by the frequencies which were solved by Fourier transform or wavelet transform under impulse excitation.Test results show this method is accurate and convenient.The damage cases of web members on the beam were tested to validate the efficiency of wavelet support vector machine method.Wavelet packet decomposition was applied to the structural response signals under ambient vibration,feature vectors were obtained by feature extraction method with "energy-damage state".The feature vectors were used for training and classification as the inputs of the compact support vector machine.The structural damage position and degree can be identified and classified,and the test result is highly accurate especially combined with principle component analysis.
The cable prestressing loss and web members damage experiment of a beam string structure was carried out.Different prestressing forces were exerted on the cable to simulate cable prestressing loss,the prestressing forces were calculated by the frequencies which were solved by Fourier transform or wavelet transform under impulse excitation.Test results show this method is accurate and convenient.The damage cases of web members on the beam were tested to validate the efficiency of wavelet support vector machine method.Wavelet packet decomposition was applied to the structural response signals under ambient vibration,feature vectors were obtained by feature extraction method with "energy-damage state".The feature vectors were used for training and classification as the inputs of the compact support vector machine.The structural damage position and degree can be identified and classified,and the test result is highly accurate especially combined with principle component analysis.
引文
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[10]任伟新,陈刚.由基频计算拉索拉力的实用公式[J].土木工程学报,2005,38(11):26-31.
[2]李洪泉,吕西林.钢筋混凝土框架地震损伤识别与采用耗能装置修复的试验研究[J].建筑结构学报,2001,22(3):9-14.
[3]Yen G G,Lin K C.Wavelet packet feature extractionfor vibration monitoring[J].IEEE TransactionsIndustrial Electronics,2000,47(3):650-667.
[4]Vapnik V N.Statistical learning theory[M].NewYork:Wiley,1998.
[5]三田彰.结构健康监测动力学[M].西安:西安交通大学出版社,2004.
[6]何浩祥,闫维明,周锡元.小波支持向量机在结构损伤识别中的应用研究[J].振动、测试与诊断,2007,27(1):53-57.
[7]Jutten C,Herault J.Independent component analysisversus PCA[C]∥Proceeding of 4thEuropean SignalProcessing Conference.France,Grenoble:[s.n.],1988:287-314.
[8]刘开国.大跨度张弦梁式结构的分析[J].空间结构,2001,7(2):39-43.
[9]丁阳,岳增国,刘锡良.大跨度张弦梁结构的地震响应分析[J].地震工程与工程振动,2003,23(5):163-168.
[10]任伟新,陈刚.由基频计算拉索拉力的实用公式[J].土木工程学报,2005,38(11):26-31.
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