scholarly journals Application of Hilbert-Huang Transform in Structural Health Monitoring: A State-of-the-Art Review

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Bo Chen ◽  
Sheng-lin Zhao ◽  
Peng-yun Li
Keyword(s):  
System Identification ◽  
Health Monitoring ◽  
Structural Damage ◽  
State Of The Art ◽  
Theoretical Method ◽  
Future Trends ◽  
Structural Damage Detection ◽  
Engineering Structures ◽  
Hilbert Huang Transform ◽  
Mode Function

This paper reviews the development and application of HHT in the field of SHM in the last two decades. The challenges and future trends in the development of HHT based techniques for the SHM of civil engineering structures are also put forward. It also reviews the basic principle of the HHT method, which contains the extraction of the intrinsic mode function (IMF), mechanism of the EMD, and the features of HT; shows the application of HHT in the system identification, which contains the introduction of theoretical method, the identification of modal parameters, and the system identification on real structures; and discusses the structural damage detection using HHT based approaches, which includes the detection of common damage events, sudden damage events, and cracks and flaws.

scholarly journals Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Composite Structures ◽  
Structural Damage ◽  
Structural Integrity ◽  
State Of The Art ◽  
Future Trends ◽  
Learning Models ◽  
Damage Diagnosis ◽  
Comprehensive Review ◽  
Initiation And Propagation

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

scholarly journals Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview

2020 ◽  
Vol 10 (8) ◽  
pp. 2786 ◽  
Author(s):  
Hoofar Shokravi ◽  
Hooman Shokravi ◽  
Norhisham Bakhary ◽  
Seyed Saeid Rahimian Koloor ◽  
Michal Petrů
Keyword(s):  
System Identification ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Damage Identification ◽  
Engineering Structures ◽  
Practical Applications ◽  
Damage Scenarios ◽  
Advantages And Disadvantages ◽  
Civil Engineering Structures ◽  
Subspace System Identification

Structural health monitoring (SHM) is the main contributor of the future’s smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author’s knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated.

Piezoelectric sensor–based damage progression in concrete through serial/parallel multi-sensing technique

2019 ◽  
Vol 19 (2) ◽  
pp. 339-356 ◽  
Author(s):  
Balamonica K ◽  
Jothi Saravanan T ◽  
Bharathi Priya C ◽  
Gopalakrishnan N
Keyword(s):  
Damage Detection ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Structural Damage ◽  
Data Retrieval ◽  
Lead Zirconate ◽  
Structural Damage Detection ◽  
Retrieval Process ◽  
Zirconate Titanate ◽  
In Series

Structural damage detection using unmanned Structural Health Monitoring techniques is becoming the need of the day with the technologies available presently. Sensors made of Lead Zirconate Titanate materials, due to their simplicity and robustness, are increasingly used as an effective monitoring sensor in Structural Health Monitoring. Continuous monitoring of the structures using Lead Zirconate Titanate sensors often results in a laborious data retrieval process due to the large amount of signal generated. To speed up the data retrieval process, a multi-sensing technique in which the Lead Zirconate Titanate patches are connected in series and parallel is proposed for structural damage detection. The proposed method is validated using an experimental investigation carried out on a reinforced concrete beam embedded with smart Lead Zirconate Titanate sensor units. The beam is subjected to damage, and the location of damage is identified using conductance signatures obtained from patches sensed individually and through multiplexing. This article proposes an effective methodology for selection of patches to be connected in series/parallel to maximise the efficiency of damage detection. Damage quantification using conventional statistical metrics such as root mean square deviation, mean absolute percentage deviation and cross correlations are found to be ineffective in identifying the location of damage from the multiplexed signatures. In turn, dynamic metrics such as moving root mean square deviation, moving mean absolute percentage deviation and moving cross correlation with overlapped moving blocks of data are proposed in the present work and their ability to detect the damage location from multiplexed signatures is discussed.

Vibration-Based Damage Detection of Composite Wingbox Structures Using Improved Hilbert-Huang Transform

2006 ◽  
Vol 324-325 ◽  
pp. 539-542 ◽  
Author(s):  
Huan Guo Chen ◽  
Yun Ju Yan ◽  
Jie Sheng Jiang
Keyword(s):  
Structural Damage ◽  
Wavelet Packet ◽  
Dynamic Responses ◽  
Structural Dynamic ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Index Vector ◽  
Selection For ◽  
Instantaneous Energy ◽  
Composite Wing

A vibration-based approach to detect crack damage in a cantilever composite wingbox is studied using the improved Hilbert-Huang Transform (HHT). The improved HHT is composed of HHT with Wavelet Packet Transform (WPT) and a simple but effective method for intrinsic mode function (IMF) selection. For different damage status, in order to obtain structural dynamic responses, which imply plentiful damage information, the composite wing boxes were excited by a contrived square wave signal. Then, the dynamic responses of intact wingbox and damaged wingbox are disposed using improved HHT. Finally, a feature index vector of structural damage, i.e. the ariation quantity of instantaneous energy, is constructed. The obtained results show that the proposed damage feature index vector is more sensitive to small damage than those in traditional signal processing.

STRUCTURAL DAMAGE DETECTION BASED ON CHANGES IN DYNAMIC PARAMETERS

2017 ◽  
Vol 166 (46) ◽  
pp. 75-84
Author(s):  
Dominika Ziaja ◽  
Bartosz Miller
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Single Layer ◽  
Dynamic Parameters ◽  
Structural Damage Detection ◽  
Dynamic Excitation ◽  
Portal Frame

The article presents the idea of structural health monitoring as a supporting action to protect the environment. The preliminary proposal of a procedure enabling the damage detection of the joints is shown on the example of a two-storey portal frame. Conclusions, presented in the paper, based on the measurement of accelerations in selected points of structure, subjected to dynamic excitation. Single-layer, feed-forward artificial neural networks were used as a tool for the analysis of changes in the dynamic parameters.

scholarly journals Structural damage detection based on improved frequency change ratio

2019 ◽  
Vol 272 ◽  
pp. 01010
Author(s):  
Jian WANG ◽  
Huan JIN ◽  
Xiao MA ◽  
Bin ZHAO ◽  
Zhi YANG ◽  
...  
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Detection Method ◽  
Mode Shapes ◽  
Frequency Change ◽  
Damage Localization ◽  
Structural Damage Detection ◽  
Practical Application ◽  
Slight Damage

Frequency Change Ratio (FCR) based damage detection methodology for structural health monitoring (SHM) is analyzed in detail. The effectiveness of damage localization using FCR for some slight damage cases and worse ones are studied on an asymmetric planar truss numerically. Disadvantages of damage detection using FCR in practical application are found and the reasons for the cases are discussed. To conquer the disadvantages of FCR, an Improved Frequency Change Ratio (IFCR) based damage detection method which takes the changes of mode shapes into account is proposed. Verification is done in some damage cases and the results reveal that IFCR can identify the damage more efficiently. Noisy cases are considered to assess the robustness of IFCR and results indicate that the proposed method can work well when the noise is not severe.

Development of Civil Engineering Structural Damage Diagnosis

2013 ◽  
Vol 671-674 ◽  
pp. 2029-2031
Author(s):  
De Yu Huang
Keyword(s):  
Damage Detection ◽  
Hot Spots ◽  
International Research ◽  
Structural Damage ◽  
Civil Engineering ◽  
Structural Damage Detection ◽  
Traditional Methods ◽  
Engineering Structures ◽  
Damage Diagnosis ◽  
Dynamic Methods

Damage diagnosis of civil engineering structures has become one of the hot spots of the current international research in the field of Civil Engineering.This article describes the tasks and objectives of structural damage detection in civil engineering,systematically expounded the civil engineering structural damage diagnosis describes the traditional methods of structural damage diagnosis, static methods and dynamic methods, and evaluated their respective advantages and disadvantages.Finally, the study made several suggestions and Prospects for structural damage detection.

Structural Damage Detection Based on Fuzzy LS-SVM Integrated Quantum Genetic Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1365-1371 ◽  
Author(s):  
Jian Hong Xie
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Least Square ◽  
Support Vector ◽  
Structural Damage Detection ◽  
Quantum Genetic Algorithm

Structural damage detection and health monitoring is very important in many applications, and a key related issue is the method of damage detection. In this paper, Fuzzy Least Square Support Vector Machine (FLS-SVM) is constructed by combining Fuzzy Logic with LS-SVM, and a real-coded Quantum Genetic Algorithm (QGA) is applied to optimize parameters of FLS-SVM. Then, the method of FLS-SVM integrated QGA is used to detect damages for fiber smart structures. The testing results show FLS-SVM possesses the higher detecting accuracy and the bitter dissemination ability than LS-SVM under the same conditions, and the parameters of FLS-SVM can be effectively optimized by the real-coded QGA. The proposed method of FLS-SVM integrated QGA is effective and efficient for structural damage detection.

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