基于支持向量机的大跨度拱桥损伤识别方法研究
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摘要
作为一种新兴的机器学习算法,支持向量机在损伤识别中已显示出其回归能力的优越性。将模态曲率改变率作为损伤识别特征参数,提出了基于支持向量机的大跨度拱桥损伤识别方法。首先应用模态曲率改变率进行损伤定位识别,然后重新构造训练样本,利用最小二乘支持向量机方法进行大跨度拱桥的损伤程度识别,该方法在较少的样本条件下,取得了非常接近目标值的识别效果。通过与RBF神经网络的训练结果进行对比,验证了该方法的精确性。
As a new machine learning algorithm,the method of support vector machine (SVM) shows its superiority of the ability of regression in the fields of damage identification.Considering variation ratio of curvature mode as the feature parameters of damage identification,the method of the damage identification of a long-span arch bridge based on SVM was presented.At first,the variation ratio of curvature mode was used to carry on damage location identification.Then,the training samples were reconstructed,the method of least square support vector machine was used to identify the long-span arch bridge damage level,and the identification results were very close to the target values under the condition of small samples.Compared with the results from the RBF neural network,the precision of the proposed method was verified.
As a new machine learning algorithm,the method of support vector machine (SVM) shows its superiority of the ability of regression in the fields of damage identification.Considering variation ratio of curvature mode as the feature parameters of damage identification,the method of the damage identification of a long-span arch bridge based on SVM was presented.At first,the variation ratio of curvature mode was used to carry on damage location identification.Then,the training samples were reconstructed,the method of least square support vector machine was used to identify the long-span arch bridge damage level,and the identification results were very close to the target values under the condition of small samples.Compared with the results from the RBF neural network,the precision of the proposed method was verified.
引文
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[2]何浩祥,闫维明,彭凌云.基于支持向量机的钢筋混凝土桥梁损伤识别[J].公路交通科技,2008,25(3):65-69.
[3]李冬生.拱桥吊杆损伤监测与健康诊断[D].哈尔滨:哈尔滨工业大学,2007,4.
[4]Vapnik V.张学工译.统计学习理论的本质[M].北京:清华大学出版社,2004,6.
[5]李忠献,齐怀展,朱劲松.基于模态曲率法的大跨度斜拉桥损伤识别[J].地震工程与工程振动,2007,27(4):122-126.
[6]冯立芳.小波和神经网络相结合的结构损伤识别方法[D].杭州:浙江大学,2006.
[7]Nello Cristianini,John Shawe-Taylor.An Introduction to Sup-port Vector Machines and Other Kernel-based Learning Meth-ods[M].2000,6.
[8]金伟良,袁雪霞.基于LS-SVM的结构可靠度响应面分析方法[J].浙江大学学报(工学版),2007,1:44-47.
[9]Fletcher R.Practical Method of Optimization,John Wiley and Sons:Chichester and New York,1987.
[10]徐建伟,李飞泉,彭卫.中承式拱桥的吊杆损伤识别[J].公路,2007,12:71-77.
[11]刘龙,孟光.基于曲率模态和支持向量机的结构损伤位置两步识别方法[J].工程力学(增刊I),2006,23:35-39.
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