Structural health monitoring and assessment using wavelet packet energy spectrum

Abstract A novel methodology about structural health monitoring and assessment using the wavelet packet energy spectrum is developed by analyzing the acceleration response signal, aiming to identify the structural damage in real time and perform early structural damage alarming. The structural damage warning will be made according to baseline thresholds, which are obtained from a convergence analysis based on the mean and variance value of two structural damage indicators, namely energy ratio deviation (ED) and energy ratio variance (EV). To illustrate the effectiveness of the method, the operational stage of the Wangzong tunnel in the Wuhan Metro Line 3 in China is taken as a case study. The results demonstrate that (1) The multi-threshold wavelet packet denoising method is effective in removing noise and reserving information within 0.1 Hz; (2) The damage indicators ED and EV from the wavelet packet energy spectrum are sensitive to structural damage, whose mean and variance value will converge well along with the growth of wavelet packet component number; and (3) By the threshold value based on the one-side 98% upper confidence limit, the potential structural damage can be detected and alarmed dynamically in the underground filling with complexity and uncertainty. In conclusion, this research contributes to developing new metrics for structural health evaluation with consideration of time dimension and vibration properties of the structure, which is helpful in detecting and alarming the possible structural damage prior to structural failures. Its inherent limitations lie in the relatively complicated computing process and strict requirements in sensor location.

[1]  Meng Li,et al.  Methodologies of safety risk control for China’s metro construction based on BIM , 2018, Safety Science.

[2]  Zheng Li,et al.  Damage identification method based on continuous wavelet transform and mode shapes for composite laminates with cutouts , 2018 .

[3]  Fan Wang,et al.  Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine , 2013 .

[4]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Faramarz Khoshnoudian,et al.  Sensitivity-based damage identification method for structures exposed to ground excitation , 2018 .

[6]  Charles R. Farrar,et al.  Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .

[7]  Hani G. Melhem,et al.  Damage detection of structures by wavelet analysis , 2004 .

[8]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[9]  D. Zimmerman,et al.  Structural damage detection using a minimum rank update theory , 1994 .

[10]  Miroslaw J. Skibniewski,et al.  Towards a safety management approach for adjacent buildings in tunneling environments: Case study in China , 2014 .

[11]  Miroslaw J. Skibniewski,et al.  A dynamic Bayesian network based approach to safety decision support in tunnel construction , 2015, Reliab. Eng. Syst. Saf..

[12]  Qingzhou Li,et al.  Structure damage identification under ambient excitation based on wavelet packet analysis , 2017 .

[13]  K. Loparo,et al.  Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling : A method for bearing prognostics , 2007 .

[14]  Qixin Shi,et al.  Motorization Process and Management in Big Cities in China: Take Beijing as an Example , 2007 .

[15]  Xiaodong Lin,et al.  Condition assessment of shield tunnel using a new indicator: The tunnel serviceability index , 2017 .

[16]  Wilson H. Tang,et al.  Probability concepts in engineering planning and design , 1984 .

[17]  Saeid R. Dindarloo,et al.  Maximum surface settlement based classification of shallow tunnels in soft ground , 2015 .

[18]  Jeong‐Tae Kim,et al.  Improved damage identification method based on modal information , 2002 .

[19]  Khaled Daqrouq,et al.  ECG Signal Denoising By Wavelet Transform Thresholding , 2008 .

[20]  Martin Skitmore,et al.  An Incident Database for Improving Metro Safety: The Case of Shanghai , 2016 .

[21]  Wei-Xin Ren,et al.  Wavelet packet based damage identification of beam structures , 2005 .

[22]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[23]  Fernanda Leite,et al.  Exploring approaches to improve the performance of autonomous monitoring with imperfect data in location-aware wireless sensor networks , 2014, Adv. Eng. Informatics.

[24]  Luca Schenato,et al.  Structural Health Monitoring of a Road Tunnel Intersecting a Large and Active Landslide , 2017 .

[25]  Xianguo Wu,et al.  Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach , 2017 .

[26]  Muammer Gökbulut,et al.  Performance comparison of wavelet thresholding techniques on weak ECG signal denoising , 2013 .

[27]  Cheng Lina,et al.  Research on Seismic Signals Denoising Method based on Multi-Threshold Wavelet Packet , 2016 .

[28]  Nikolai Bobylev,et al.  Mainstreaming sustainable development into a city's Master plan: A case of Urban Underground Space use , 2009 .

[29]  Ahmet E. Aktan,et al.  ISSUES IN INFRASTRUCTURE HEALTH MONITORING FOR MANAGEMENT , 2000 .

[30]  Wei-Xin Ren,et al.  Structural damage identification by using wavelet entropy , 2008 .

[31]  Miroslaw J. Skibniewski,et al.  An improved Dempster-Shafer approach to construction safety risk perception , 2017, Knowl. Based Syst..

[32]  George F. List,et al.  A new approach to understand metro operation safety by exploring metro operation hazard network (MOHN) , 2017 .

[33]  Pengfei Li,et al.  Protection of buildings against damages as a result of adjacent large-span tunneling in shallowly buried soft ground , 2013 .

[34]  Xinong Zhang,et al.  Damage identification based on wavelet packet analysis method , 2016 .

[35]  Jian-Da Wu,et al.  An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network , 2009, Expert Syst. Appl..

[36]  Samuel T. Ariaratnam,et al.  Development of a sustainability assessment model for underground infrastructure projects , 2009 .

[37]  Yan-Fang Sang,et al.  A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series , 2012, Water Resources Management.

[38]  Shyamala C. Doraisamy,et al.  Multi-level basis selection of wavelet packet decomposition tree for heart sound classification , 2013, Comput. Biol. Medicine.

[39]  Carl Marcus Wallenburg,et al.  Dealing with supply chain risks , 2012 .

[40]  Chih-Chen Chang,et al.  Structural Damage Assessment Based on Wavelet Packet Transform , 2002 .

[41]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[42]  Javad Poshtan,et al.  Bearing fault detection using wavelet packet transform of induction motor stator current , 2007 .

[43]  Daniele Dessi,et al.  Damage identification techniques via modal curvature analysis: Overview and comparison , 2015 .

[44]  Ganggang Sha,et al.  Structural damage identification using damping: a compendium of uses and features , 2017 .

[45]  Sami Ekici,et al.  Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition , 2008, Expert Syst. Appl..

[46]  A. Lui,et al.  Time‐frequency decomposition of signals in a current disruption event , 1997 .

[47]  Chih-Chen Chang,et al.  Statistical Wavelet-Based Method for Structural Health Monitoring , 2004 .

[48]  Tao Liu,et al.  A Study on the WPT-based Structural Damage Alarming of the ASCE Benchmark Experiments , 2008 .

[49]  Hong Hao,et al.  Application of Wavelet Packet Transform in Subsea Pipeline Bedding Condition Assessment , 2012 .

[50]  Mohammad Ali Lotfollahi-Yaghin,et al.  Study of the Functions of Wavelet Packet Transform (WPT) and Continues Wavelet Transform (CWT) in Recognizing the Damage Specification , 2010 .

[51]  Wenjing Zhou,et al.  Online Condition Monitoring of a Rail Fastening System on High-Speed Railways Based on Wavelet Packet Analysis , 2017, Sensors.

[52]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[53]  Grant P. Steven,et al.  VIBRATION-BASED MODEL-DEPENDENT DAMAGE (DELAMINATION) IDENTIFICATION AND HEALTH MONITORING FOR COMPOSITE STRUCTURES — A REVIEW , 2000 .

[54]  Arnab Majumdar,et al.  Metro Railway Safety: Analysis of Accident Precursors , 2012 .

[55]  Li Fang,et al.  Subway Opening, Traffic Accessibility, and Housing Prices: A Quantile Hedonic Analysis in Hangzhou, China , 2018, Sustainability.

[56]  Norris Stubbs,et al.  Damage Localization in Structures Without Baseline Modal Parameters , 1996 .

[57]  Jian Zhao,et al.  Structural health monitoring of underground facilities – technological issues and challenges , 2005 .

[58]  Simaan M. AbouRizk,et al.  Intelligent Approach to Estimation of Tunnel-Induced Ground Settlement Using Wavelet Packet and Support Vector Machines , 2017, J. Comput. Civ. Eng..

[59]  J M W Brownjohn,et al.  Structural health monitoring of civil infrastructure , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.