Content area
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.
Details
Accuracy;
Comparative analysis;
Investigations;
Algorithms;
Optimization;
Signal processing;
Environmental conditions;
Damage detection;
Diagnosis;
Measurement techniques;
Structural damage;
Parameter uncertainty;
Comparative studies;
Regularization;
Seismic activity;
Seismic response;
Inverse problems;
Ill posed problems;
Sensors;
Damage patterns;
Earthquake damage;
Three dimensional models;
Regularization methods;
False alarms
; Lin, Jianfu 3
; Liu Xuanyu 1 1 Department of Applied Mechanics and Engineering, School of Aeronautics and Astronautics, Sun Yat-Sen University, Shenzhen 518107, China; [email protected] (W.W.);, Center of Safety Monitoring of Engineering Structures, Shenzhen Academy of Disaster Prevention and Reduction, China Earthquake Administration, Shenzhen 518003, China
2 National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
3 Center of Safety Monitoring of Engineering Structures, Shenzhen Academy of Disaster Prevention and Reduction, China Earthquake Administration, Shenzhen 518003, China, Department of Applied Mechanics and Engineering, School of Aeronautics and Astronautics, Sun Yat-Sen University, Shenzhen 518107, China; [email protected] (W.W.);