基于频域系统辨识和支持向量机的桥梁状态监测方法  

BRIDGE CONDITION MONITORING APPROACH BASED ON FREQUENCY DOMAIN SYSTEM IDENTIFICATION AND SUPPORT VECTOR MACHINE

作　　者：樊可清[1] 倪一清[2] 高赞明[2] 

机构地区：[1]五邑大学信息学院,广东江门529020 [2]香港理工大学土木及结构工程学系

出　　处：《工程力学》2004年第5期25-30,共6页Engineering Mechanics

摘　　要：随着大跨度悬索、斜拉桥的增加,保障桥梁安全、降低维护费用成为交通管理以及政府部门关注的问题。针对损伤样本难以获得的实际情况,将桥梁状态监测问题作为模式识别中的“一类学习”问题处理。桥梁模式特征获取过程是“只有输出响应”的系统辨识问题,考虑到监测系统需要在线工作的特点,提出运用概念直观、结果可靠且便于自动实现的CMIF系统辨识方法作为获取模式特征的工具。为了获得足够敏感的异常报警判别函数,采用了基于支持向量机的一类学习算法,这种方法在得到很高灵敏性的同时,可以方便地权衡敏感性和泛化性能之间的矛盾。用香港汀九桥794小时实测数据对所采用的算法进行验证,证明了算法的有效性和实用性,其结果可供设计类似监测系统时参考。As the long span suspension and cable-stayed bridges are widely used in the world, the highway department has become increasingly interested in developing the bridge condition monitoring system to enhance the bridge security and reduce the cost for maintenance. Due to the difficulty in acquiring damage case, the bridge condition monitoring is regarded as a one class learning problem in pattern recognition. A bridge condition monitoring method based on frequency domain system identification and Support Vector Machine (SVM), which is a novel kernel-based machine learning algorithm, is developed in this paper. The method can not only gain precise decision function for alarming, but also solve the problem between the sensitivity and generalization. The feature extraction procedure for bridge condition monitoring is a typical output only system identification problem. In order to use the method online, a frequency domain system identification method, CMIF algorithm which is much easy to ran automatically, is used to extract modal parameters. The algorithm in this paper is verified by the field data from Ting Kau Bridge in Hong Kong. The results show that the method is reliable and useful in the bridge monitoring system design.

关 键 词：系统辨识 桥梁状态监测 支持向量机 一类学习算法 模态参数辨识 

分 类 号：U446[建筑科学—桥梁与隧道工程] TP181[交通运输工程—道路与铁道工程]