摘要
针对曲率模态对振型节点较不敏感且无法定量估计损伤的问题,在广义局部信息熵的基础上引入曲率模态,推导出广义局部曲率模态信息熵的公式,并建立相应的损伤指标.利用有限元软件Midas civil建立一简支梁桥损伤模型,提取并处理该简支梁的动力参数,将一阶曲率模态和广义局部曲率模态信息熵分别作为神经网络的输入参数,对损伤进行识别并对比两种参数的识别结果,以此来研究测点数量对指标精确度的影响.研究结果表明:广义局部曲率模态信息熵作为神经网络的输入参数能较好地定位并定量损伤,在靠近振型节点处指标的识别精度高于曲率模态,当测点数量为33时,识别精度最高.
In view of the fact that curvature mode is less sensitive to vibration mode nodes and can not estimate damage quantitatively,a formula of mode information entropy for generalized local curvature is deduced,based on generalized local information entropy and by introducing conception of the curvature model.The corresponding damage index is established therefore.A simple supported beam is analyzed by using the finite element software Midas civil.The dynamic parameters of the simply supported beam are extracted and processed.The first-order curvature mode and the local generalized curvature modal information entropy are used respectively as two input parameters for the neural network to identify the damage.The conclusion of two parameters having been identified are compared with each other so as to study the influence of the number of measure points on the identification accuracy of each index.The results show that the generalized local curvature mode information entropy as the input parameter of the neural network can locate and quantify the damage much better.The accuracy of indicators near the vibration mode node is higher than the result by the method of the curvature mode.When the number of measuring points takes 33,the identification accuracy becomes the highest.
作者
项长生
原子
周宇
XIANG Chang-sheng;YUAN Zi;ZHOU Yu(College of Civil Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China;Western Center of Disaster Mitigation in Civil Engineering of Ministry of Education, Lanzhou Univ. of Tech., Lanzhou 730050, China;School of Civil Engineering, Anhui University of Architecture, Hefei 230601, China)
出处
《兰州理工大学学报》
CAS
北大核心
2021年第1期129-135,共7页
Journal of Lanzhou University of Technology
基金
国家自然科学基金(51868045)。
关键词
广义局部曲率模态信息熵
曲率模态
BP神经网络
损伤定位
损伤定量
generalized local curvature modal information entropy
curvature modal
BP neural network
damage location
damage quantification
