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Damage Detection of 2D Frame Structures using Incomplete Measurements by Optimization Procedure and Model Reduction



Source title: 
Journal of Advanced Engineering and Computation, 2(3): 164-173, 2018 (Scopus)
Academic year of acceptance: 

The article presents an effective method for damage assessment of 2D frame structures using incomplete modal data by optimization procedure and model reduction technique. In this proposed method, the structural damage detection problem is defined as an optimization problem, in which a hybrid objective function and the damage severity of all elements are considered as the objective function and the continuous design variables, respectively. The teaching-learning-based optimization (TLBO) algorithm is applied as a powerful optimization tool to solve the problem. In addition, owing to the use of incomplete measurements, an improved reduction system (IRS) technique is adopted to reduce the mass and stiffness matrices of structural finite element model. The efficiency and robustness of the proposed method are validated with a 4-storey (3 bay) steel plane frame involving several damage scenarios without and with measurement noise. The obtained results clearly demonstrate that even the incompleteness and noisy environment of measured modal data, the present method can work properly in locating and estimating damage of the frame structure by utilizing only the first five incomplete modes' data.