TY - JOUR
T1 - An Adaptive Sparse Regularization Method for Response Covariance-Based Structural Damage Detection
AU - Lin, Jian Fu
AU - Wu, Wei Lin
AU - Huang, Jian Liang
AU - Wang, Jun Fang
AU - Ren, Wen Xin
AU - Ni, Yi Qing
AU - Wang, Li Xin
N1 - Publisher Copyright:
© 2023 Jian-Fu Lin et al.
PY - 2023
Y1 - 2023
N2 - Structural damage detection is usually an ill-posed inverse problem due to the contamination of measurement noise and model error in structural health monitoring. To deal with the ill-posed damage detection problem, l2-regularization is widely used. However, l2-regularization tends to provide nonsparse solutions and distribute identified damage to many undamaged elements, potentially leading to false alarms. Therefore, an adaptive sparse regularization method is proposed, which considers spatially sparse damage as a prior constraint since structural damage often occurs in some locations with stiffness reduction at the sparse elements out of the large total number of elements in an entire structure. First, a response covariance-based convex cost function is established by incorporating an l1-regularized term and an adaptive regularization factor to formulate the sparse regularization-based damage detection problem. Then, optimal sensor placement is conducted to determine the optimal measurement locations where the acceleration responses are adopted for computing the response covariance-based damage index and cost function. Further, the predictor-corrector primal-dual path-following approach, an efficient and robust convex optimization algorithm, is applied to search for solutions to the damage detection problem. Finally, a comparison study with the Tikhonov regularization-based damage detection method is conducted to examine the performance of the proposed adaptive sparse regularization-based method by using an overhanging beam model subjected to different damage scenarios and noise levels. The numerical study demonstrates that the proposed method can effectively and accurately identify damage under multiple damage scenarios with various noise levels, and it outperforms the Tikhonov regularization-based method in terms of high accuracy and few false alarms. The analyses on time consumption, adaptiveness of the sparse regularization factor, model-error resistance, and sensor number influence are conducted for further discussions of the proposed method.
AB - Structural damage detection is usually an ill-posed inverse problem due to the contamination of measurement noise and model error in structural health monitoring. To deal with the ill-posed damage detection problem, l2-regularization is widely used. However, l2-regularization tends to provide nonsparse solutions and distribute identified damage to many undamaged elements, potentially leading to false alarms. Therefore, an adaptive sparse regularization method is proposed, which considers spatially sparse damage as a prior constraint since structural damage often occurs in some locations with stiffness reduction at the sparse elements out of the large total number of elements in an entire structure. First, a response covariance-based convex cost function is established by incorporating an l1-regularized term and an adaptive regularization factor to formulate the sparse regularization-based damage detection problem. Then, optimal sensor placement is conducted to determine the optimal measurement locations where the acceleration responses are adopted for computing the response covariance-based damage index and cost function. Further, the predictor-corrector primal-dual path-following approach, an efficient and robust convex optimization algorithm, is applied to search for solutions to the damage detection problem. Finally, a comparison study with the Tikhonov regularization-based damage detection method is conducted to examine the performance of the proposed adaptive sparse regularization-based method by using an overhanging beam model subjected to different damage scenarios and noise levels. The numerical study demonstrates that the proposed method can effectively and accurately identify damage under multiple damage scenarios with various noise levels, and it outperforms the Tikhonov regularization-based method in terms of high accuracy and few false alarms. The analyses on time consumption, adaptiveness of the sparse regularization factor, model-error resistance, and sensor number influence are conducted for further discussions of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85176375952&partnerID=8YFLogxK
U2 - 10.1155/2023/3496666
DO - 10.1155/2023/3496666
M3 - Journal article
AN - SCOPUS:85176375952
SN - 1545-2255
VL - 2023
JO - Structural Control and Health Monitoring
JF - Structural Control and Health Monitoring
M1 - 3496666
ER -