摘要
利用结构损伤空间稀疏性进行结构损伤识别是结构健康监测领域的研究热点之一,基于结构灵敏度分析与稀疏正则化的损伤识别方法能有效识别结构损伤位置和程度,但在噪声等因素影响下,其识别结果容易出现误判和刚度强化等问题。针对此问题,基于范数归一化与稀疏正则化约束,提出了一种结构损伤检测方法;该方法在迭代求解过程中,通过增加范数归一化、稀疏正则化约束以及对模型增加牛顿迭代法约束和总损伤折减系数约束,达到减少误判、增加结果合理化和提高识别精度的目的。三种不同结构损伤识别仿真算例研究表明:增加范数归一化和模型约束后,结构损伤识别精度得到明显提高;在不同噪声水平下,所提新方法既能有效定位结构损伤又能准确识别结构损伤程度,且具有较强鲁棒性。
Using the space sparsity of structural damage is prevalent to identify structural damages in the field of structural health monitoring.Based on the sparse regularization,first-order sensitivity analysis can detect damage locations and quantify damage extents effectively.However,the misjudgments and stiffness hardening would occur under the influence of noises.A new structural damage detection(SDD)algorithm,based on the norm normalization and sparse regularization constraints,was proposed to solve these problems.It can reduce misjudgments,make the damage detection results more rational and improve identification accuracy by adding the norm normalization and sparse regularization constraints to the process of iteration as well as adding,the constraint of Newton iteration method and the total damage reduction factor to the model.The numerical simulation results from three different structures indicate that the damage detection identification are obviously improved after adding the norm normalization and model constraints.The new SDD method can effectively identify damage locations and extents under different level noises and get high robustness to noises.
作者
骆紫薇
余岭
刘焕林
潘楚东
LUO Ziwei;YU Ling;LIU Huanlin;PAN Chudong(School of Mechanics and Construction Engineering,Jinan University,Guangzhou 510632,China;MOE Key Lab of Disaster Forecast and Control in Engineering,Jinan University,Guangzhou 510632,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2018年第18期30-35,58,共7页
Journal of Vibration and Shock
基金
国家自然科学基金(51678278
51278226)
关键词
结构损伤识别
一阶灵敏度分析
稀疏正则化
模型约束
范数归一化
structural damage identification
first-order sensitivity analysis
sparse regularization
model constraint
norm normalization
