基于应变模态和稀疏贝叶斯学习的网架结构损伤识别  

Damage identification of truss structure based on strain mode and sparse Bayesian learning

作　　者：仇树茂 杨海峰[1] 吴子燕[1] 李梦莹 悦峰 QIU Shu-mao;YANG Hai-feng;WU Zi-yan;LI Meng-ying;YUE Feng(School of Mechanics,Civil Engineering and Architecture,Northwestern Polytechnical University,Xi'an 710129,China)

机构地区：[1]西北工业大学力学与土木建筑学院,西安710129

出　　处：《计算力学学报》2022年第1期63-69,共7页Chinese Journal of Computational Mechanics

基　　金：国家自然科学基金(11902253,51278420);西北工业大学研究生创意创新种子基金(ZZ2019121)资助项目.

摘　　要：传统稀疏贝叶斯学习算法进行损伤识别时需要对每个单元进行刚度损伤系数的迭代更新,当结构单元众多时,存在计算效率低和对振型的完备性要求高等问题。本文提出了损伤识别两步法,首先利用应变模态差指标进行疑似损伤单元的判断;接着以单元刚度损伤系数为目标参数,建立结构损伤识别的多层次稀疏贝叶斯学习模型,利用稀疏贝叶斯学习算法进一步识别疑似损伤单元的损伤位置以及程度。以一个空间网架结构为对象,针对单位置损伤和多位置损伤情况验证了该方法的有效性。When the traditional sparse Bayesian learning algorithm is adopted for structure damage identification,the stiffness damage parameters of each element need to be updated iteratively.The problems such as low computational efficiency and high requirements for completeness of mode shapes will be very serious when there are many structural elements.In order to solve the above problems,a two-step method for damage identification is proposed.Firstly,the modal strain difference index is used to judge the suspected damage elements.Secondly,the element stiffness damage parameters are taken as the target to establish a multi-level sparse Bayesian learning model for structural damage identification.Then the damage location and severity of suspected damage elements are identified using the sparse Bayesian learning algorithm.A space truss structure is used to verify object,the effectiveness of the proposed method for identification of single damage and multi-damage.

关 键 词：结构损伤识别 应变模态 稀疏贝叶斯学习 空间网架 

分 类 号：O327[理学—一般力学与力学基础] TU356[理学—力学]