Abstract
Damage identification methods based on structural modal parameters are influenced by the structure form, number of measuring sensors and noise, resulting in insufficient modal data and low damage identification accuracy. The additional virtual mass method introduced in this study is based on the virtual deformation method for deriving the frequency-domain response equation of the virtual structure and identify its mode to expand the modal information of the original structure. Based on the initial condition assumption that the structural damage was sparse, the damage identification method based on sparsity with l1 and l2 norm of the damage-factor variation and the orthogonal matching pursuit (OMP) method based on the l0 norm were introduced. According to the characteristics of the additional virtual mass method, an improved OMP method (IOMP) was developed to improve the localization of optimal solution determined using the OMP method and the damage substructure selection process, analyze the damage in the entire structure globally, and improve damage identification accuracy. The accuracy and robustness of each damage identification method for multi-damage scenario were analyzed and verified through simulation and experiment.
Highlights
With the rapid development of modern science and technology, there has been an increasing number of large and complex engineering structures [1,2]
The damage identification objective function translates into over-determined form when structural modal information is expanded by the additional virtual mass method, which will lead to non-sparse result because of noise
These findings show that the improved OMP (IOMP) damage identification method based on additional virtual mass can precisely identify the damage to this frame model
Summary
With the rapid development of modern science and technology, there has been an increasing number of large and complex engineering structures [1,2]. Assessed structural damage by comparing the dynamic response parameters of the finite element model in damaged and undamaged states based on the experimental natural frequency and vibration mode of the structure and verified the model using the cantilever beam model. The damage identification method based on the dynamic response and modal parameters of the structure has some limitations. The damage identification objective function translates into over-determined form when structural modal information is expanded by the additional virtual mass method, which will lead to non-sparse result because of noise. Modified a structural model and identified its damage using the l1 regularization method of sparse recovery theory based on the structural frequency and mode shape. The additional virtual mass and the sparse damage identification methods were combined for higher identification precision and consistency with actual damage distribution. Damage Identification Method Based on Additional Virtual Mass and Damage Sparsity
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