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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
Structural Sparse Damage Identification Considering Ambient Temperature Variations Based on Support Vector Machine and Enhanced Moth-Flame Optimization
投稿时间:2021-03-02  修订日期:2021-05-19
DOI:
中文关键词:  结构损伤识别  温度影响  稀疏正则化  支持向量机  稀疏损伤  优化算法  I-40桥
英文关键词:structural damage identification  temperature effect  sparse regularization  support vector machine  sparse damage  optimization algorithm  I-40 bridge
基金项目:广西玉林市科学研究与技术开发计划项目(玉市科20202927); 南宁市优秀青年科技创新创业人才培育计划项目(RC20190108); 武汉市城建委科技项目(201804); 湖北省教育厅科学研究计划指导性项目(B2018051); 湖北省高等学校优秀中青年科技创新团队计划(T2020010)
作者单位邮编
雷勇志 武汉工程大学土木工程与建筑学院 430073
顾箭峰* 武汉工程大学土木工程与建筑学院 430074
黄民水 武汉工程大学土木工程与建筑学院 
杨雨厚 广西交科集团有限公司 
舒国明 河北交通职业技术学院 
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中文摘要:
      结构处于自然环境中常会受到外界环境因素如温度变化的影响。温度效应这一难以量化分析的非线性因素会干扰结构模态测试,引起实测的结构动力响应信息出现较大误差,从而影响对结构健康状况判定。另外,基于优化算法的损伤识别方法在反演输出结构损伤位置及量化损伤程度时,易出现局部最优解与计算效率低下等问题。针对以上难题,在本文中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别。该方法首先采取支持向量机对结构的环境温度变化进行量化分析,得到环境温度变化准确范围;随后引入稀疏正则化技术确定结构稀疏损伤工况;接着将获得的环境温度变化情况及损伤工况信息作为强化飞蛾扑火优化算法的初始种群生成依据,从而得到对实际损伤工况有针对性的初始种群用于缩小优化算法搜索空间,提高计算效率,强化损伤识别效率与准确程度。最后采用基于频率的结构多损伤定位保证准则及模态应变能基本因子构建的目标函数,通过考虑环境温度及随机噪声双重影响的简支梁数值算例以及I-40钢-混组合体系桥梁工程实例验证了本文所提出的损伤识别方法的可行性。
英文摘要:
      Civil engineering is always surrounded by the natural environment, which is affected by various factors, such as temperature variations. Temperature effects, as a nonlinear factor, is difficult to be analyzed quantitatively. Meanwhile, it will influence the results of modal testing and cause the errors in the measured dynamic response data, which set up obstacles to the evaluation of real structural damage situation. Furthermore, damage identification method based on optimization algorithm is easy to be trapped in local optimal and lower computing efficiency when the method has been used to identify damage location and extent. 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 structural sparse damage identification problem considering temperature variations. Firstly, SVM is used to quantify structural temperature variations and eliminate the temperature effects. Then, sparse regularization method is introduced to determine structural sparse damage condition. Thirdly, the temperature variations and damage situation obtained in the previous step are adopted to perform the initialization of EMFO, which can narrow search space, improve efficiency and enhance the accuracy of damage identification. Finally, two examples, a numerical simply supported beam considering temperature variations and random noise effects, and a practical engineering of I-40 Bridge, a large steel-concrete composite bridge, are utilized to verify the effectiveness of the proposed method.
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