Structural damage identification based on dual sensitivity analysis from optimal sensor placement

被引：0

作者：

Qi, Tengrun ^{[1
]}

Hou, Zhilong ^{[1
]}

Yu, Ling ^{[1
]}

机构：

[1] School of Mechanics and Construction Engineering, Jinan University, Guangzhou

来源：

Journal of Infrastructure Intelligence and Resilience | 2024年 / 3卷 / 03期

基金：

中国国家自然科学基金;

关键词：

L1; regularization; Optimal sensor placement; Sensitivity analysis; Structural damage identification; Structural health monitoring;

D O I：

10.1016/j.iintel.2024.100110

中图分类号：

学科分类号：

摘要：

Structural damage identification (SDI) methods using incomplete modal information can avoid the extension for unmeasured degrees of freedom, but the absence of essential damage information often leads to the failure of SDI. To address this problem, a novel SDI method based on dual sensitivity analysis and optimal sensors placement technique is proposed in this study. Firstly, in the optimal sensor placement technique, an improved eigenvector sensitivity method combined with weighted modal kinetic energy is proposed, which enables the acquisition of eigenvector information related to damage sensitivity, and incorporates it into the modal strain energy sensitivity matrix to obtain the dual sensitivity analysis matrix. Then, the sparsity of structural damage is considered, and the L1 sparse regularization is selected and introduced into the dual sensitivity analysis damage equation for better SDI results. Finally, to assess the effectiveness of the proposed method, a series of numerical simulations and experimental verifications were carried out under different structural damage scenarios. The results indicate that the proposed method can efficiently localize and quantify the structural damage with minimal modal information in one single step. © 2024 The Authors

引用

共 37 条

[1]

Cai Q., Zhu S., The nexus between vibration-based energy harvesting and structural vibration control: a comprehensive review, Renew. Sustain. Energy Rev., 155, (2022)

[2]

Chen C.B., Yu L., A hybrid ant lion optimizer with improved Nelder Mead algorithm for structural damage detection by improving weighted trace lasso regularization, Adv. Struct. Eng., 23, 3, pp. 468-484, (2020)

[3]

Chen Z.P., Yu L., A new structural damage detection strategy of hybrid PSO with Monte Carlo simulations and experimental verifications, Measurement, 122, pp. 658-669, (2018)

[4]

Chen Z., Yu L., Structural damage detection based on crack damage function and equivalent damage factor vector, Proc., 7th World Conf. On Structural Control and Monitoring. Qingdao, China: International Association for Structural Control and Monitoring, (2018)

[5]

Delgado R.A., Middleton R.H., Sparse representation using stepwise Tikhonov regularization with offline computations, IEEE Signal Process. Lett., 26, 6, pp. 873-877, (2019)

[6]

Entezami A., Shariatmadar H., Sarmadi H., Structural damage detection by a new iterative regularization method and an improved sensitivity function, J. Sound Vib., 399, (2017)

[7]

Friderikos O., Baranger E., Guillon D., Regularized least squares for the building of a sizing criterion based on damage mechanics, Compos. Struct., 234, (2020)

[8]

Ghiasi A., Moghaddam M.K., Ng C.T., Sheikh A.H., Shi J.Q., Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network, Eng. Struct., 264, (2022)

[9]

Gomes G.F., de Almeida F.A., da Silva Lopes Alexandrino P., da Cunha S.S., de Sousa B.S., Ancelotti A.C., A multiobjective sensor placement optimization for SHM systems considering Fisher information matrix and mode shape interpolation, Eng. Comput., 35, 2, pp. 519-535, (2018)

[10]

Heo G., Wang M., Satpathi D., Optimal transducer placement for health monitoring of long span bridge, Soil Dynam. Earthq. Eng., 16, 7-8, pp. 495-502, (1997)

← 1 2 3 4 →