A rapid identification method for underwater explosion damage of a concrete gravity dam

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (24) : 46-53.

LI Qi1,2,3,WANG Gaohui2，LU Wenbo2，NIU Xinqiang1，GU Chongshi3

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Abstract

The rapid identification of damage location and range of dams subjected to underwater explosion is difficult to realize using traditional damage identification technology.Through the secondary development of ABAQUS based on Python, the batch establishment of a finite element model and the batch calculation of modal analysis under different damage conditions were realized.The frequency databases of gravity dams at different damage locations and ranges were constructed.Particle swarm optimization (PSO) was used to find the optimal training parameters of single and multiple damage identification of dams via a support vector regression (SVR) model, and the model was validated by the test samples of the frequency database.Considering the influence of different noise levels on damage identification, the reliability of SVR models for dam damage identification was evaluated.The results show that the PSO-SVR model can accurately identify the damage location and radius of gravity dams, and the model has good anti-noise performance.

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concrete gravity dam / damage identification / particle swarm optimization(PSO) / support vector regression(SVR) / anti-noise performance

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LI Qi1,2,3,WANG Gaohui2，LU Wenbo2，NIU Xinqiang1，GU Chongshi3. A rapid identification method for underwater explosion damage of a concrete gravity dam[J]. Journal of Vibration and Shock, 2020, 39(24): 46-53

References

[1] 张社荣, 王高辉. 混凝土重力坝抗爆性能及抗爆措施研究[J]. 水利学报, 2012, 43(10): 1202-1213.
    ZHANG Sherong, WANG Gaohui. Study on the antiknock performance and measures of concrete gravity dam [J]. Journal of Hydraulic Engineering, 2012, 43(10): 1202-1213.
[2] 张社荣, 王高辉. 浅水爆炸冲击荷载下高拱坝抗爆性能分析[J]. 天津大学学报 (自然科学与工程技术版), 2013, 46(4): 315-321.
ZHANG Sherong, WANG Gaohui. Antiknock performance of high arch dam subjected to shallow water explosion [J]. Journal of Tianjin University (Science and Technology), 2013, 46(04): 315-321.
[3] LYNCH J P, LOH K J. A summary review of wireless sensors and sensor networks for structural health monitoring [J]. Shock and Vibration Digest, 2006, 38(2): 91-130.
[4] QIN X, XU Q J. Statistical analysis of initial defects between concrete layers of dam using X-ray computed tomography [J]. Construction & Building Materials, 2016, 125: 1101-1113.
[5] 王岩, 吴胜兴, 周继凯. 大坝混凝土动态弯拉声发射特性试验[J]. 无损检测, 2009, 31(2): 115-119.
    WANG Yan, WU Shengxing, ZHOU Jikai. Experiment on acoustic emission characteristics of dam concrete under dynamic bending tension [J]. Nondestructive Testing, 2009, 31(2): 115-119.
[6] LOPERTE A, BAVUSI M, CERVERIZZO G, et al. Ground Penetrating Radar in Dam Monitoring: The Test Case of Acerenza (Southern Italy) [J]. International Journal of Geophysics, 2011, 2011: 1-9.
[7] 滕军, 卢伟. 基于多类型传感器信息的结构损伤识别方法[J]. 东南大学学报(自然科学版), 2010, 40(3):538-542.
    TENG Jun, LU Wei. Structural damage identification method based on multi-type sensors [J]. Journal of Southeast University (Natural Science Edition), 2010, 40(3):538-542.
[8] 任鹏, 周智, 武文华, 等. 基于应变响应统计特征的海洋立管损伤诊断方法 [J]. 振动与冲击, 2017, 36(1):23-30.
    REN Peng, ZHOU Zhi, WU Wenhua, et al. Damage diagnosis strategy for offshore risers based on statistical features of strain responses [J]. Journal of Vibration and Shock, 2017, 36(1):23-30.
[9] 马洒洒, 王彬, 李川, 等. 基于FBG应变传感器的隧道安全实时监测算法研究[J]. 仪器仪表学报, 2017, 38(2):304-310.
    MA Sasa, WANG Bin, LI Chuan, et al. Research on real-time monitoring algorithm for tunnel safety status based on fiber Bragg grating strain sensors [J]. Chinese Journal of Scientific Instrument, 2017, 38(2):304-310.
[10] TIMO S, WALTER L. Embedded strain gauges for condition monitoring of silicone gaskets [J]. Sensors, 2014, 14(7): 12387-12398.
[11] KIRKEGAARD P H, RYTTER A, RAMBOLL. Use of Neural Networks for Damage Assessment in a Steel Mast [J]. Proceedings of Spie - The International Society for Optical Engineering, 1993, 2251.
[12] LI Z X, YANG X M. Damage identification for beams using ANN based on statistical property of structural responses [J]. Computers & Structures, 2008, 86(1-2): 64-71.
[13] 刘春城, 刘佼. 基于支持向量机的大跨度拱桥损伤识别方法研究[J]. 振动与冲击, 2010, 29(7):174-178.
    LIU Chuncheng, LIU Jiao. Damage identification of a long-span arch bridge based on support vector machine [J]. Journal of Vibration and Shock, 2010, 29(7):174-178.
[14] KAVEH A, MANIAT M. Damage detection based on MCSS and PSO using modal data [J]. Smart Structures and Systems, 2015, 15(5): 1253-1270.
[15] 程琳, 杨杰, 郑东健, 等. 基于强震观测和ARMAV模型的混凝土坝模态识别[J]. 振动与冲击, 2017, 36(8):224-230.
    CHENG Lin, YANG Jie, Zheng Dongjian, et al. Modal identification of concrete dams based on strong-motion records and an ARMAV model [J]. Journal of Vibration and Shock, 2017, 36(8):224-230.
[16] LE T P, PAULTRE P. Modal identification based on continuous wavelet transform and ambient excitation tests [J]. Journal of Sound & Vibration, 2012, 331(9): 2023-2037.
[17] DARBRE G R, De SMET C A M, KRAEMER C. Natural frequencies measured from ambient vibration response of the arch dam of Mauvoisin [J]. Earthquake Engineering & Structural Dynamics, 2015, 29(5): 577-586.
[18] YANG J, JIN F, WANG J T, et al. System identification and modal analysis of an arch dam based on earthquake response records [J]. Soil Dynamics & Earthquake Engineering, 2017, 92: 109-121.
[19] CARDEN E P. Vibration Based Condition Monitoring: A Review [J]. Structural Health Monitoring, 2004, 3(4): 355-377.
[20] Gunn S R. Support vector machines for classification and regression [R]. USA: University of Southampton, 1998.
[21] LI Q, WANG G H, LU W B, et al. Failure modes and effect analysis of concrete gravity dams subjected to underwater contact explosion considering the hydrostatic pressure [J]. Engineering Failure Analysis, 2018, 85:62-76.