基于响应复合能量因子的波浪激励下高桩码头桩基损伤识别

PDF(4347 KB)

振动与冲击 ›› 2024, Vol. 43 ›› Issue (5) : 31-40.

论文

李成明1，王启明1,3，朱瑞虎2,3，胡艳1，王泊淳1

作者信息 +

Damage identification for pile foundation of high-piled wharf under wave excitation based on response composite energy factor

LI Chengming1,WANG Qiming1,3,ZHU Ruihu2,3,HU Yan1,WANG Bochun1

Author information +

文章历史 +

摘要

波浪是高桩码头桩基长期、重要外部激励，探索波浪激励下桩基损伤识别对构建码头健康监测体系具有重要意义。由于波浪激励下桩基动力响应呈现多类型信号混叠、窄带、非平稳的特性，使得现有损伤识别方法难以实现准确判定。为解决上述问题，首先构建基于自适应噪声的完备集成经验模态分解与K-means++的信号自动重构方法，以提取损伤特征子信号；然后，通过融合能量、相位等信息，构建新型复合能量损伤识别指标，以提高微小波浪激励下损伤识别的敏感性和鲁棒性。进一步,在高桩码头桩基多种损伤工况下开展波浪激励试验，验证新方法在损伤识别方面的有效性。结果表明，新构建的复合能量因子兼顾能量、相位的优点，能准确识别出损伤存在、损伤位置和损伤程度。

Abstract

Waves are long-term and significant external excitations for the pile foundation in high-piled wharves. Identifying damage in pile foundations under wave excitation is crucial for constructing health monitoring systems. The dynamic response under wave excitation exhibits multi-type aliasing, narrow-band and non-stationarity, which pose challenges for accurate damage identification using existing methods. To address the above issues, an automatic reconstruction method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and K-means++ was proposed to extract damage sub-signals. Then, a novel composite energy damage index was constructed to enhance the sensitivity and robustness of damage identification under small wave excitation. Furthermore, wave excitation experiments were conducted on a high-piled wharf model under various damage conditions to verify the effectiveness of the new method in damage identification. The results show that the composite energy factor combines both energy and phase advantages, which can precisely identify the damage’s presence, location and severity.

导出引用

李成明1，王启明1,3，朱瑞虎2,3，胡艳1，王泊淳1. 基于响应复合能量因子的波浪激励下高桩码头桩基损伤识别[J]. 振动与冲击, 2024, 43(5): 31-40

LI Chengming1,WANG Qiming1,3,ZHU Ruihu2,3,HU Yan1,WANG Bochun1. Damage identification for pile foundation of high-piled wharf under wave excitation based on response composite energy factor[J]. Journal of Vibration and Shock, 2024, 43(5): 31-40

参考文献

[1] Wang Q M, Zhu R H, Zheng J H, et al. A method for the damage detection of pile foundation in high-pile wharf based on a curvature mode deletion model [J]. China Ocean Engineering, 2020, 34(6): 871-880. [2] Nejad N M, Aval S, Maldar M, et al. A damage detection procedure using two major signal processing techniques with the artificial neural network on a scaled jacket offshore platform [J]. Advances in Structural Engineering, 2021, 24(8): 1655-1667. [3] 王怀元，李德新. 高应变动力测桩法在桩基检测中的技术探讨[J]. 地质与勘探, 1999(06): 92-94. WANG Huai-yuan, LI De-xin. Application of high－strain dynamic pegging to the detecting of pile foundation [J]. Geology And Prospecting, 1999(06): 92-94. [4] 赵振东，杉本三千雄，铃木善雄. 桩基低应变完整性检测的分析研究[J]. 地震工程与工程振动, 1995(04): 104-112. ZHAO Zhen-dong, MICHIO Sugimoto, YOSHIO Suzuki. Analytical study on low strain integrity testing for piles [J]. Earthquake Engineering and Engineering Vibration, 1995(04): 104-112. [5] 曹宇春，吴世明，高广远. 桩基动力检测技术的现状及存在的问题[J]. 上海地质, 2002(01): 43-45. CAO Yu-chun, WU Shi-ming, GAO Guang-yuan. Application situation of piles dynamic detection and existed problems [J]. Shanghai Geology, 2002(01): 43-45. [6] 李沛，李东军. 在役码头结构检测评估现状与展望[J]. 中国港湾建设. 2019, 39(08): 71-75. LI Pei, LI Dong-jun. Present situation and prospect of detection and evaluation for structure of in-service wharfs [J]. China Harbour Engineering, 2019, 39(08): 71-75. [7] 孙熙平，王元战，赵炳皓. 环境激励下高桩码头模态参数识别及损伤诊断[J]. 海洋工程, 2013, 31(05): 62-68. SUN Xi-ping, WANG Yuan-zhan, ZHAO Bing-hao. Modal identification and damage diagnosis of the high-piled wharf under ambient excitation [J]. The Ocean Engineering, 2013, 31(05): 62-68. [8] 李肖，苏静波，吉同元，等. 高桩码头桩基动力损伤识别方法[J]. 水运工程, 2015(10): 57-62. LI Xiao, SU Jing-bo, JI Tong-yuan, et al. Identification method for pile foundations dynamic damage of piled wharf [J]. Port & Waterway Engineering, 2015(10): 57-62. [9] 王启明，朱瑞虎，王宁，等. 基于模态柔度的高桩码头桩基损伤识别[J]. 水运工程, 2020(10): 46-51. WANG Qi-ming, ZHU Rui-hu, WANG Ning, et al. Damage detection of pile foundation in high-pile wharf based on modal flexibility [J]. Port & Waterway Engineering, 2020(10): 46-51. [10] 朱瑞虎，王宁，王启明，等. 基于模态应变能的高桩码头桩基损伤识别研究[J]. 水道港口, 2021, 42(04): 504-509. ZHU Rui-hu, WANG Ning, WANG Qi-ming, et al. Rsearch on damage identification of pile foundation of high pile wharf based on modal strain energy [J]. Journal of Waterway and Harbor, 2021, 42(04): 504-509. [11] Wang N, Zhu R H, Wang Q M, et al. A method for quantitative damage identification in a high-piled wharf based on modal strain energy residual variability [J]. Ocean Engineering, 2022, 254. [12] Zhu R H, Wang Q M, Zheng J H, et al. Damage detection of pile foundations in marine engineering based on a multidimensional dynamic signature [J]. Journal of Coastal Research, 2021, 37(3): 576-588. [13] Sun L, Lu Y, Zhang X. A review on damage identification and structural health monitoring for offshore platform [C]//International Conference on Offshore Mechanics and Arctic Engineering. American Society of Mechanical Engineers, 2016, 49941: V003T02A052. [14] Ko J M, Ni Y Q. Technology developments in structural health monitoring of large-scale bridges [J]. Engineering Structures, 2005, 27(12): 1715-1725. [15] Teng Z Q, Teng S, Zhang J Q, et al. Structural damage detection based on real-time vibration signal and convolutional neural network [J]. Applied Sciences-Basel, 2020, 10(14). [16] Gillich G R, Furdui H, Wahab M A, et al. A robust damage detection method based on multi-modal analysis in variable temperature conditions [J]. Mechanical Systems and Signal Processing, 2019, 115: 361-379. [17] Temarel P, Bai W, Bruns A, et al. Prediction of wave-induced loads on ships: Progress and challenges [J]. Ocean Engineering, 2016, 119: 274-308. [18] Li W, Huang Y. A method for damage detection of a jacket platform under random wave excitations using cross correlation analysis and PCA-based method [J]. Ocean Engineering, 2020, 214. [19] Ji C, Chen M, Yuan Z, et al. Model test studies on the new method of offshore platform damage identification and localization [J]. The Ocean Engineering, 2010, 28(4): 9-17. [20] Liu J, Zhou G, Han G, et al. Modal parameters identification and bearing capacity evaluation for ocean oil-derrick under wave excitation [J]. The Ocean Engineering, 2009, 27(1): 22-27. [21] Staszewski W J, Robertson A N. Time-frequency and time-scale analyses for structural health monitoring [J]. Philosophical Transactions of the Royal Society a-Mathematical Physical and Engineering Sciences, 2007, 365(1851): 449-477. [22] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995. [23] Yu Y, Li W, Sheng D, et al. A novel sensor fault diagnosis method based on modified ensemble empirical mode decomposition and probabilistic neural network [J]. Measurement, 2015, 68: 328-336. [24] Wu Z H, Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method [J]. Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences, 2004, 460(2046): 1597-1611. [25] Torres M E, Colominas M A, Schlotthauer G, et al. A complete ensemble empirical mode decomposition with adaptive noise [C]//2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2011: 4144-4147. [26] Zhu Y, Ni Y, Jesus A, et al. Thermal strain extraction methodologies for bridge structural condition assessment [J]. Smart Materials and Structures, 2018, 27(10): 105051. [27] Xiao F, Chen G S, Zatar W, et al. Signature extraction from the dynamic responses of a bridge subjected to a moving vehicle using complete ensemble empirical mode decomposition [J]. Journal of Low Frequency Noise Vibration and Active Control, 2021, 40(1): 278-294. [28] Mousavi A A, Zhang C W, Masri S F, et al. Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach [J]. Structural Health Monitoring-an International Journal, 2022, 21(4): 1833-1848. [29] Hu Y, Wang Q, Zhu R, et al. Online robustness damage identification of dynamic response of high pile wharf under wave excitation [J]. Ocean Engineering, 2023, 273: 113950. [30] Yang H, Li L L, Li G H, et al. A novel feature extraction method for ship-radiated noise [J]. Defence Technology, 2022, 18(4): 604-617. [31] Li Y X, Li Y, Chen X, et al. A new underwater acoustic signal denoising technique based on ceemdan, mutual information, permutation entropy, and wavelet threshold denoising [J]. Entropy, 2018, 20(8). [32] Babouri M K, Ouelaa N, Kebabsa T, et al. Diagnosis of mechanical defects using a hybrid method based on complete ensemble empirical mode decomposition with adaptive noise and optimized wavelet multi-resolution analysis: experimental study [J]. International journal of advanced manufacturing technology, 2021,112(9-10):2657-2681. [33] Alsalah A, Holloway D, Mousavi M, et al. Identification of wave impacts and separation of responses using EMD [J]. Mechanical Systems and Signal Processing, 2021, 151: 107385. [34] Jain A K, Murty M N, Flynn P J. Data clustering: A review [J]. Acm Computing Surveys, 1999, 31(3): 264-323. [35] Li H Z, Wang J. Collaborative annealing power k-means plus plus clustering [J]. Knowledge-Based Systems, 2022, 255. [36] Mörchen F, Ultsch A, Hoos O. Extracting interpretable muscle activation patterns with time series knowledge mining [J]. International Journal of Knowledge-Based and Intelligent Engineering Systems, 2005, 9(3): 197-208. [37] Shahapure K R, Nicholas C. Cluster quality analysis using silhouette score [C]//2020 IEEE 7th international conference on data science and advanced analytics (DSAA). IEEE, 2020: 747-748. [38] Gholipour G, Zhang C W, Mousavi A A. Nonlinear numerical analysis and progressive damage assessment of a cable-stayed bridge pier subjected to ship collision [J]. Marine Structures, 2020, 69. [39] Tributsch A, Adam C. An enhanced energy vibration-based approach for damage detection and localization [J]. Structural Control & Health Monitoring, 2018, 25(1). [40] Mousavi A A, Zhang C, Masri S F, et al. Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study [J]. Structural Health Monitoring, 2022, 21(3): 887-912. [41] Cheraghi N, Riley M J, Taheri F. A novel approach for detection of damage in adhesively bonded joints in plastic pipes based on vibration method using piezoelectric sensors [C]//2005 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2005, 4: 3472-3478. [42] Kunwar A, Jha R, Whelan M, et al. Damage detection in an experimental bridge model using Hilbert-Huang transform of transient vibrations [J]. Structural Control & Health Monitoring, 2013, 20(1): 1-15. [43] Liu W, Lü Q, Chen R, et al. A modified empirical mode decomposition method for multiperiod time-series detrending and the application in full-waveform induced polarization data [J]. Geophysical Journal International, 2019, 217(2): 1058-1079.