Structural damage identification based on hybrid whale annealing algorithm and sparse regularization

PDF(1972 KB)

Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (17) : 85-91.

L Hao1, FENG Zhongren1， WANG Xiongjiang1， ZHOU Wei1， CHEN Baiben2

Author information +

History +

Abstract

In practical application, the whale optimization algorithm(WOA) has disadvantages of low accuracy and slow convergence speed.Here, a hybrid whale simulated-annealing algorithm(HWSA) based on nonlinear convergence factor, adaptive weight and simulated annealing strategy was proposed to balance convergence speed and optimization ability of the algorithm, and enhance the performance of the algorithm.Identification factors based on structural modal parameters were constructed, and sparse constraints were introduced.The effectiveness of the improved method was illustrated through damage identification numerical simulation for ASCE Benchmark structural models under different damage conditions.The results showed that misjudged elements decrease with increase in sparse constraints to be beneficial to structural damage identification, and the L0.5 sparse constraint has the most significant improvement on identification accuracy; the hybrid whale annealing algorithm can effectively identify damage location and damage level, its convergence accuracy and optimization performance are significantly improved, its performance is stable in noise environment, and it has a certain anti-noise robustness; this method can be further applied in damage identification of practical engineering.

Key words

structural damage identification / hybrid whale simulated-annealing algorithm(HWSA) / nonlinear convergence factor / adaptive weight / simulated annealing / sparse regularization

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

L Hao, FENG Zhongren， WANG Xiongjiang， ZHOU Wei， CHEN Baiben. Structural damage identification based on hybrid whale annealing algorithm and sparse regularization[J]. Journal of Vibration and Shock, 2021, 40(17): 85-91

References

［1］黄方林, 王学敏, 陈政清, 等.大型桥梁健康监测研究进展［J］.中国铁道科学, 2005,26(2): 1-7.
HUANG Fanglin, WANG Xuemin, CHEN Zhengqing, et al.Research progress made on the health monitoring for large-type bridges ［J］.China Railway Science, 2005,26(2): 1-7.
［2］王延伟, 宋旭坤, 温瑞智, 等.智能优化算法在结构损伤识别中的应用［J］.世界地震工程, 2011,27(4): 44-51.
WANG Yanwei, SONG Xukun, WEN Ruizhi, et al.Application of intelligent optimization algorithm to damage detection of structure ［J］.World Earthquake Engineering, 2011,27(4): 44-51.
［3］MIRJALILI S, LEWIS A.The whale optimization algorithm［J］.Advances in Engineering Software, 2016,95: 51-67.
［4］龙文, 蔡绍洪, 焦建军, 等.求解大规模优化问题的改进鲸鱼优化算法［J］.系统工程理论与实践, 2017,37(11): 2983-2994.
LONG Wen, CAI Shaohong, JIAO Jianjun, et al.Improved whale optimization algorithm for large scale optimization problems ［J］.System Engineering—Theory & Practice, 2017,37(11): 2983-2994.
［5］赵春华, 胡恒星, 陈保家, 等.基于深度学习特征提取和WOA-SVM状态识别的轴承故障诊断［J］.振动与冲击, 2019,38(10): 31-37.
ZHAO Chunhua, HU Hengxing, CHEN Baojia, et al.Bearing fault diagnosis based on the deep learning feature extraction and WOA-SVM state recognition ［J］.Journal of Vibration and Shock, 2019,38(10): 31-37.
［6］白丽丽, 韩振南, 任家骏, 等.基于拉普拉斯分值和鲸鱼寻优SVM的滚动轴承故障诊断［J］.太原理工大学学报, 2019,50(6): 829-834.
BAI Lili, HAN Zhennan, REN Jiajun, et al.Fault diagnosis of rolling bearing based on Laplacia score and whale optimization algorithms optimized SVM ［J］.Journal of Taiyuan University of Technology, 2019,50(6): 829-834.
［7］牛培峰, 吴志良, 马云鹏, 等.基于鲸鱼优化算法的汽轮机热耗率模型预测［J］.化工学报, 2017,68(3): 1049-1057.
NIU Peifeng, WU Zhiliang, MA Yunpeng, et al.Prediction of steam turbine heat consumption rate based on whale optimization algorithm ［J］.CIESC Journal, 2017,68(3): 1049-1057.
［8］ELAZIZ M A, MIRJALILI S.A hyper-heuristic for improving the initial population of whale optimization algorithm［J］.Knowledge-Based Systems, 2019,172: 42-63.
［9］KIRKPATRICK S, VECCHI M P.Optimization by simulated annealing［M］.Washington, DC: American Association for the Advancement of Science, 1983.
［10］FRISWELL M I.Finite element model updating in structural dynamics［M］.Berlin: Springer, 1995.
［11］TIBSHIRANI R.Regression shrinkage and selection via the Lasso［J］.Journal of the Royal Statistical Society, 1996,58(1): 267-288.
［12］JOHNSON E A, LAM H F, KATAFYGIOTIS L S, et al.Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data［J］.Journal of Engineering Mechanics, 2004,130(1): 3-15.