Structural damage detection based on norm normalization and sparse regularization constraints
LUO Ziwei 1，YU Ling 1,2，LIU Huanlin 1 PAN Chudong 1
1.School of Mechanics and Construction Engineering, Jinan University, Guangzhou 510632, China；
2.MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou 510632, China
Abstract：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.
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