Sensitivity-free damage identification of tall buildings by incomplete modal data and sparse regularization
Identifying the damages in tall buildings is crucial for structural health monitoring.In doing so,(incomplete)modal data is often measured due to its easy availability,e.g.,through ambient tests.One noteworthy issue for the modal data is that its amount is always limited with respect to the amount of the damage parameters,and this shall make the identification problem ill-posed and very sensitive to the measurement noise.As a reasonable way to enhance the robustness of the identification process,the sparse regularization is introduced.Furthermore,to render the additional computational complexity,that is caused by the sparse regularization,as little as possible,a new objective function is established accordingly.It is also noteworthy that the present damage identification approach is sensitivity-free.Numerical examples are conducted to verify the accuracy and robustness of the proposed damage identification approach.
damage identification shear model sparse regularization incomplete modal data alternating minimization approach
Li Wang Zhong-Rong Lu
Department of applied mechanics and engineering,Sun Yat-sen University,Guangzhou,P.R.China
国际会议
The 7th World Conference on Structural Control and Monitoring(7WCSCM)(第七届结构控制与监测世界大会)
青岛
英文
2803-2812
2018-07-22（万方平台首次上网日期，不代表论文的发表时间）