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Comparative Analysis of Wavelet Transform for Time-Frequency Analysis and Transient Localization in Structural Health Monitoring

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Structural Durability & Health Monitoring 2021年第1期15卷 1-22页

作者： ahmed silik Mohammad Noori Wael A.Altabey Ramin Ghiasi Zhishen Wu International Institute for Urban Systems Engineering(IIUSE) Southeast UniversityNanjing210096China Department of Civil Engineering Nyala UniversityNyalaSudan Department of Mechanical Engineering California Polytechnic State UniversitySan Luis ObispoCA 93405USA Department of Mechanical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt

A critical problem facing data collection in structural health monitoring,for instance via sensor networks,is how to extract the main components and useful features for damage detection.A structural dynamic measurement is more often a complex time-varying process and therefore,is prone to dynamic changes in time-frequency *** extract the signal components and capture the useful features associated with damage from such nonstationary signals,a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is *** analyses have proven to be a viable and effective tool in this *** transform(WT)can analyze different signal components and then comparing the characteristics of each signal with a resolution matched to its ***,the challenge is the selection of a proper wavelet since various wavelets with varied properties that are to analyze the same data may result in different *** article presents a study on how to carry out a comparative analysis based on analytic wavelet scalograms,using structural dynamic acceleration responses,to evaluate the effectiveness of various wavelets for damage detection in civil *** scalogram’s informative time-frequency regions are examined to analyze the variation of wavelet coefficients and show how the frequency content of a signal changes over time to detect transient events due to ***,damage-induced changes are tracked with time-frequency *** this aim,energy distribution and sharing information are *** undamaged and damaged simulated comparative results of a structure reveal that the damaged structure were shifted from the undamaged ***,the Bump wavelet shows the best results than the others.

关键词： Dynamic measurement wavelet selection continuous wavelet

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Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

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Structural Durability & Health Monitoring 2024年第6期18卷 763-783页

作者： Yadong Xu Weixing Hong Mohammad Noori Wael A.Altabey ahmed silik Nabeel S.D.Farhan Nanjing Highway Development Center Changzhou211106China Nanjing Zhixing Information Technology Co. Ltd.Nanjing210000China Department of Mechanical Engineering California Polytechnic State UniversitySan Luis ObispoCA 93405USA Department of Mechanical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt Department of Civil Engineering Nyala UniversityP.O.Box 155NyalaSudan School of Civil Engineering University of LeedsLeedsLS29JTUK

This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element *** ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge *** CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency *** integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge *** diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter *** approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.

关键词： Structural Health Monitoring(SHM) bridges big model Convolutional Neural Network(CNN) Finite Element Method(FEM)

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Development of Features for Early Detection of Defects and Assessment of Bridge Decks

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Structural Durability & Health Monitoring 2023年第4期17卷 257-281页

作者： ahmed silik Xiaodong Wang Chenyue Mei Xiaolei Jin Xudong Zhou Wei Zhou Congning Chen Weixing Hong Jiawei Li Mingjie Mao Yuhan Liu Mohammad Noori Wael A.Altabey Nanjing Zhixing Information Technology Co. Ltd.NanjingChina Jiangsu Advance Transportation Institute NanjingChina Suzhou Highway Development Center SuzhouChina Guangdong Communications Group Co. Ltd.GuangzhouChina Department of Mechanical Engineering California Polytechnic State UniversitySan Luis ObispoCA93405USA School of Civil Engineering University of LeedsLeedsLS29JTUK Department of Civil Engineering Faculty of Engineering SciencesNyala UniversityNyala092Sudan Department of Mechanical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt

Damage detection is an important area with growing interest in mechanical and structural *** of the critical issues in damage detection is how to determine indices sensitive to the structural damage and insensitive to the surrounding environmental *** damage identification indices commonly focus on structural dynamic characteristics such as natural frequencies,mode shapes,and frequency *** study aimed at developing a technique based on energy Curvature Difference,power spectrum density,correlation-based index,load distribution factor,and neutral axis shift to assess the bridge deck *** addition to tracking energy and frequency over time using wavelet packet transform,in order to further demonstrate the feasibility and validity of the proposed technique for bridge condition assessment,experimental strain data measured from two stages of a bridge in the different intervals were *** comparative analysis results of the bridge in first and second stage show changes in the proposed feature *** is concluded,these changes in the values of the proposed features can be used to assess the bridge deck performance.

关键词： Structural health monitoring strain monitoring distribution factor wavelet packet transform

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