摘要
斜拉桥主梁是其运营中最为主要的受力构件,主梁的损伤识别方法研究一直是热点问题。针对结构健康监测数据,提出了一种基于马氏距离累积量的时域损伤识别方法。首先,构造基于马氏距离累积量的结构损伤识别向量,给出了利用监测数据进行损伤识别的操作流程。其次,建立了金塘大桥有限元模型,以正弦力激励的方式获得结构单元在健康及损伤状况下的加速度数据;并利用无损伤状态下的加速度数据作为参考样本,损伤状态下的加速度数据作为待测样本,通过对比损伤识别向量的变化情况进行斜拉桥主梁的损伤识别。最后,针对金塘大桥的实桥监测数据,进一步验证了该方法的有效性。结果表明,基于马氏距离累积量的损伤识别方法可以较为准确的识别斜拉桥主梁的损伤状况。
The main girder of cable-stayed bridge is the most important bearing component during the whole operation life.The study on damage detection method of the main girder is always a hot issue. A time-domain damag edetection metiiod based on Mahalanobis distance cumulants was proposed by structural health monitoring data.Firstly the structural damage detection vector based on the cumulative of Mahalanobis the operation process of damage detection from monitoring data was given.In addition,the finite element model of Jintang Bridge is established, and the acceleration data of tiie structural under the condition of health and damage are obtained by means of sinusoidal excitation. The acceleration data of damage state were takples while the without damage acceleration data were taken as reference samples. Then,the damage detection of the main girder of the cable-stayed bridge was carried out by comparing the changes of damage detection vector.Finally,the validity of tiie method was further proved by the field monitoring data of that the damage detection method based on the Mahalanobis distance cumulants can accurately identify the damage of the main girder of the cable-stayed bridge.
作者
俞鹏
彭卫
王银辉
汤焕
陈闯
YU Peng;PENG Wei;WANG Yin-hui;TANG Huan;CHEN Chuang(School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074, China;School of Civil Engineering and Architecture,Ningbo Institute of Technology,Zhejiang University;Zhejiang Fanhai Traffic Engineering Ltd,Ningbo 315100,China)
出处
《科学技术与工程》
北大核心
2018年第12期299-304,共6页
Science Technology and Engineering
基金
浙江省教育厅科研项目(Y201636902)资助
关键词
斜拉桥主梁
马氏距离累积量
监测数据
时域损伤识别
the main girder of the cable-stayed bridge
Mahalanobis distance cumulants
monitoring data
time-damage detction
