Comparative Study of Adaptive l1-Regularization for the Application of Structural Damage Diagnosis Under Seismic Excitation - Weilin Wu; Junfang Wang; Jianfu Lin; Xuanyu Liu (Buildings)
Damage identification plays a crucial role in the post-earthquake assessment and safety control of civil structures, which is usually an ill-posed inverse problem due to the presence of uncertainties and lack of measurement information. Regularization is a cutting-edge technique used to address ill-posed problems and has been developed for decades. A comprehensive review and comparison have first been conducted to identify the limitations and research gaps in the existing regularization methods for structural damage detection. Thereafter, we identified the development of the adaptive sparse regularization (ASR) method, capable of dynamically adjusting regularization parameters and sparsity according to specific damage patterns or environmental conditions, as one of the emerging research directions. Therefore, this paper systematically formulates and summarizes the theoretical framework of the ASR-based damage detection method for engineering applications to facilitate an in-depth comparative analysis. To validate the performance of the ASR method for post-earthquake structural damage diagnosis, numerical experiments are carried out on 2D and 3D models under diverse damage detection scenarios subjected to typical natural seismic excitations. These experimental investigations consider the influences of different parameter settings and uncertainties. Subsequently, the effects of damage patterns, available modal information, and solution algorithms are systematically analyzed and discussed. The results of the numerical investigation indicate that the ASR-based method is effective for damage detection, showing satisfactory accuracy and stability under complex damage scenarios and extreme conditions with a limited number of sensors and insufficient modal information. Furthermore, integrating the ASR-based damage detection method with appropriate optimization algorithms can enhance its capability to precisely identify isolated or hybrid-distributed damage.