Structural Damage Identification Based on Structural Sensitivity Analysis and Sparse Regularized Optimization

PDF(3022 KB)

Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (9) : 135-140.

ZHOU Shu-mei, BAO Yue-quan, LI Hui

Author information +

History +

Abstract

Sparsity constraints are now very popular to regularize inverse problems in the field of applied mathematics. Structural damage identification is a typical inverse problem of structural dynamics and structural damage is a spatial sparse phenomenon, i.e., when structural damage occurs, only part of elements or substructures are damaged. In this paper, a structural damage identification method based on the structural sensitivity analysis and the sparse constraints regularization is proposed. Based on structural sensitivity analysis, the relation between structural damage stiffness parameter variation and change of modal parameters of linear equations is established. Considering the structural damage sparsity conditions, the sparse regularized optimization method is employed to obtain solution. The numerical example of a truss structure with considering measurement noise, incomplete of measurements and multi-damage cases are carried out. The effects of number sensor and layout to the identification results are also investigated. The results indicate that the damage locations and extents can be effectively identified by the proposed method. With considering sparsity constraint, the accuracy of structural damage identification is obviously increased.

Key words

structural health monitoring / compressive sampling / structural damage identification / structural sensitivity analysis / sparse regularized optimization

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHOU Shu-mei, BAO Yue-quan, LI Hui. Structural Damage Identification Based on Structural Sensitivity Analysis and Sparse Regularized Optimization[J]. Journal of Vibration and Shock, 2016, 35(9): 135-140

References

[1] 欧进萍. 重大工程结构智能传感网络与健康监测系统的研究与应用[J]. 中国科学基金, 2005, (1): 8-12.
OU Jin-ping. Research and practice of smart sensor network and health monitoring systems for civil infrastructures in mainland China[J]. Science Foundation in China, 2005, (1): 8-12.
[2] 李宏男，高东伟，伊廷华. 土木工程结构健康监测系统的研究状况与进展[J]. 力学进展, 2008; 38(2): 151-166.
LI Hong-nan, Gao Dong-wei, Yi Ting-hua. Advances in structural health monitoring systems in civil engineering[J]. Advances in Mechanics, 2008, 38 (2): 151-166.
[3] 李爱群，丁幼亮，王浩，等. 桥梁健康监测海量数据分析与评估——“结构健康监测”研究进展[J]. 中国科学, 2012; 42(42): 972-984.
LI Ai-qun, DING You-liang, Wang Hao, et al. Analysis and assessment of bridge health monitoring mass data—progress in research/development of “Structural Health Monitoring”[J]. Science China Press, 2012, 42(42): 972-984.
[4] 焦美菊，孙利民，李清富. 基于监测数据的桥梁结构可靠性评估. 同济大学学报(自然科学版), 2011, 39(10): 1452-1457.
JIAO Mei-ju, SUN Li-ming, LI Qing-fu. Bridge structural reliability assessment based on health monitoring data[J]. Journal of Tongji University (Natural Science), 2011, 39(10): 1452-1457.
[5] Sohn H, Farrar C R, Hemez F M, et al. A Review of Structural Health Monitoring Literature: 1996-2001[R], LANL Report, LA-13976-MS, 2003.
[6] 宗周红，任伟新，阮毅. 土木工程结构损伤诊断研究进展[J]. 土木工程学报, 2003, 36(5): 105-110.
ZONG Zhou-hong, REN Wei-xin, RUAN Yi. Recent advances in research on damage diagnosis for civil engineering structures[J]. China Civil Engineering Journal, 2003, 36(5): 105-110.
[7] 郑栋梁，李中付，华宏星. 结构早期损伤识别技术和发展趋势[J]. 振动与冲击, 2002, 21(2): 1-8.
ZHENG Dong-liang, LI Zhong-fu, HUA Hong-xing. A summary review of structural initial damage identification methods[J]. Journal of Vibration and Shock, 2002, 21(2): 1-8.
[8] Farrar C R, Baker W E, Bell T M, et al. Dynamic Characterization and Damage Detection in the I-40 Bridge Over the Rio Grande [R]. Los Alamos National Laboratory Report, LA-1275. MS. 1994.
[9] Hera A, Hou Z. Application of wavelet approach for ASCE structural health monitoring benchmark studies[J]. ASCE Journal of Engineering Mechanics, 2004, 126(7): 677-683.
[10] Yang J N, Lei Y, Lin S, et al. Hilbert-Huang based approach for structural damage detection[J]. ASCE Journal of Engineering Mechanics, 2004: 85-95.
[11] Chen S S, Kim S. Neural network based signal monitoring in a smart structural system[J]. Smart Structures and Materials, 1994, 2191: 175-186.
[12] Balmès E, Basseville M, Mevel L et al. Statistical model-based damage localization: a combined subspace-based and substructuring approach[J]. Structural Control and Health Monitoring, 2008, 15(6): 857-875.
[13] Candès E J. Compressive sampling[C]. Proceedings of the International Congress of Mathematicians, Madrid, Spain, 2006: 1433-1452.
[14] Candès E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transaction Information Theory, 2006, 52(2): 489-509.
[15] Donoho D. Compressed sensing[J]. IEEE Transaction Information Theory, 2006, 52(4): 1289-306.
[16] 马坚伟，徐杰，鲍跃全，等. 压缩感知及其应用：从稀疏约束到低秩约束优化[J]. 信号处理, 2012, 28(5): 609-624.
MA Jian-wei, XU Jie, BAO Yue-quan, et al. Compressive sensing and its application: from sparse to low-rank regularized optimization[J]. Signal Processing, 2012, 28(5): 609-623.
[17] Zhao J, DeWolf J T. Sensitivity study for vibrational parameters used in damage detection[J]. Journal of Structure Engineering, 1999, 125(4): 410-416.
[18] Boyd S P, Vandenberghe L. Convex optimization[M]. Cambridge University Press, Cambridge, U K, 2004