摘要
损伤识别是结构健康监测的重要环节之一.为了进一步提高损伤识别效率和精度,提出了基于改进的模态应变能指标和DBO-BP(Dung Beetle Optimization-Back Propagation)神经网络的两阶段结构损伤识别方法.首先采用改进的归一化模态应变能损伤指标进行结构的损伤定位分析,然后以结构的平均单元模态应变能变化率为输入参数,损伤单元刚度折减系数为输出参数,利用最优拉丁超立方方法改进后的蜣螂优化算法(Dung Beetle Optimization,DBO)优化BP(Back Propagation)神经网络权值和阈值,进行结构的损伤定量分析.以混凝土板结构和平面刚架结构作为算例进行损伤识别验证.结果表明,该方法损伤位置定位准确,损伤程度计算效率高,并且识别误差减小到了0.4%,损伤识别效果好.
Damage identification is one of the most important aspects of structural health monitoring.In order to further improve the efficiency and accuracy of damage identification,a two-stage structural damage identification method based on the improved modal strain energy index and DBO-BP(Dung Beetle Optimization-Back Propagation)neural network is proposed.Firstly,the improved normalized damage index of modal strain energy is used for the damage localization analysis of the structure.Then,taking the average change rate of the structure unit modal strain energy as the input parameter,the stiffness reduction coefficient of the damaged unit as the output parameter,and utilizing the DBO algorithm which is an improved version of the optimal latin hypercubic method,the weights and thresholds of the BP neural network are optimized to perform the structural damage quantitative analysis.The concrete slab structure and flat rigid frame structure are used as exemplary models for damage identification verification.The results show that the proposed method is accurate in damage location identification,with high calculation efficiency for the degree of damage and small identification error as low as 0.4%,exhibiting excellent damage identification performance.
作者
杨海军
马磊
徐永智
于聪
YANG Haijun;MA Lei;XU Yongzhi;YU Cong(Hebei Institute of Civil Engineering and Architecture,Zhangjiakou 075000,Hebei,China;Zhangjiakou Key Laboratory of Engineering Mechanical Analysis,Zhangjiakou 075000,Hebei,China)
出处
《力学季刊》
北大核心
2025年第1期118-129,共12页
Chinese Quarterly of Mechanics
基金
河北建筑工程学院研究生创新基金(XY2024016)。
关键词
损伤识别
归一化模态应变能
最优拉丁超立方
蜣螂优化算法
BP神经网络
damage identification
normalized modal strain energy
optimal latin hypercube
dung beetle optimization algorithm
BP neural network
