环境温度影响下基于支持向量机与强化飞蛾扑火优化算法的结构稀疏损伤识别

结构处于自然环境中常会受到环境温度变化的影响,引起实测动力响应出现较大误差,进一步影响对结构健康状况的判定.另外,基于优化算法的损伤识别在反演损伤位置及量化损伤程度时,易出现局部最优解,且计算效率低下.针对以上难题,本文提出一种结合支持向量机与强化飞蛾扑火优化算法的损伤识别方法,用于对环境温度影响下的结构稀疏损伤进行识...

Structural sparse damage identification considering ambient temperature variations based on support vector machine and enhanced moth-flame optimization

Civil engineering structures are always affected by ambient temperature variations,which will influence the results of modal testing and set up obstacles to the evaluation of real structural damage.Furthermore,a damage identification method based on an optimization algorithm is easy to be trapped in a local optimum and lower computing efficiency when the method is used to locate and quantify damage.Aiming to the above problems,in this paper,a damage identification method,which is based on support vector machine (SVM) and enhanced moth-flame optimization (EMFO),is proposed to solve a structural sparse damage identification problem considering temperature variations.Firstly,SVM is used to quantify structural temperature variations.Then,a sparse regularization method is introduced to determine structural sparse damage conditions.Thirdly,the temperature variations and damage situation obtained in the previous step are adopted to perform the initialization of EMFO,which can narrow the search space,and enhance damage identification efficiency.Finally,two examples,a simulated simply supported beam considering temperature variations and random noise effects,and a real engineering structure of I-40 Bridge,a large steel-concrete composite bridge,are utilized to verify the effectiveness of the proposed method.