Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles

2011 ◽  
Vol 18 (2) ◽  
pp. 207-226 ◽  
Author(s):  
Jianchun Li ◽  
Ulrike Dackermann ◽  
You-Lin Xu ◽  
Bijan Samali
Keyword(s):  
Neural Network ◽  
Frequency Response ◽  
Civil Engineering ◽  
Damage Identification ◽  
Response Functions ◽  
Engineering Structures ◽  
Frequency Response Functions ◽  
Civil Engineering Structures ◽  
Neural Network Ensembles ◽  
Network Ensembles

scholarly journals Use of Time- and Frequency-Domain Approaches for Damage Detection in Civil Engineering Structures

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
V. H. Nguyen ◽  
J. Mahowald ◽  
S. Maas ◽  
J.-C. Golinval
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Prestressed Concrete ◽  
Civil Engineering ◽  
Damage Identification ◽  
Real Life ◽  
Hankel Matrix ◽  
Principal Component ◽  
Engineering Structures ◽  
Civil Engineering Structures

The aim of this paper is to apply both time- and frequency-domain-based approaches on real-life civil engineering structures and to assess their capability for damage detection. The methodology is based on Principal Component Analysis of the Hankel matrix built from output-only measurements and of Frequency Response Functions. Damage detection is performed using the concept of subspace angles between a current (possibly damaged state) and a reference (undamaged) state. The first structure is the Champangshiehl Bridge located in Luxembourg. Several damage levels were intentionally created by cutting a growing number of prestressed tendons and vibration data were acquired by the University of Luxembourg for each damaged state. The second example consists in reinforced and prestressed concrete panels. Successive damages were introduced in the panels by loading heavy weights and by cutting steel wires. The illustrations show different consequences in damage identification by the considered techniques.

Review on Damage Identification and Diagnosis Research of Civil Engineering Structure

2014 ◽  
Vol 1006-1007 ◽  
pp. 34-37 ◽  
Author(s):  
Hong Ni ◽  
Ming Hui Li ◽  
Xi Zuo
Keyword(s):  
Structural Damage ◽  
Civil Engineering ◽  
Damage Identification ◽  
Engineering Structures ◽  
Engineering Structure ◽  
Civil Engineering Structure ◽  
Structure Damage ◽  
Development Direction ◽  
Structural Damage Identification ◽  
Civil Engineering Structures

This paper first describes the importance of structural damage identification and diagnosis in civil engineering, and introduces domestic and foreign status of damage identification and diagnosis methods, and on the basis of this, it also introduces all kinds of methods for damage identification and diagnosis of civil engineering structures, and finally puts forward the development direction of civil engineering structure damage identification and diagnosis.

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.

Damage Identification Using Wavelet Packet Transform and Neural Network Ensembles

2018 ◽  
Vol 18 (12) ◽  
pp. 1850148 ◽  
Author(s):  
Xiang Zhang ◽  
Renwen Chen ◽  
Qinbang Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Identification ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Rotor Blades ◽  
Identification Accuracy ◽  
Vibration Responses ◽  
Neural Network Ensembles ◽  
Network Ensembles

This study presents a damage identification method that combines wavelet packet transforms (WPTs) with neural network ensembles (NNEs). The WPT is used to extract damage features, which are taken as the input vectors in the NNEs used for damage identification. An experiment was performed on a helicopter rotor blades structure to verify the proposed method. First, the vibration responses collected by different sensors are decomposed using the WPT. Second, the relative band energy of each decomposed frequency band is calculated and fused as the damage feature vectors. Third, two types of the NNEs are designed. One is based on the backward propagation neural networks (BPNNs) for detecting the damage locations and severities and the other one is based on the probabilistic neural network (PNN) to detect the damage types. Finally, the trained NNEs are employed in damage identification. From the identification outcomes, it is concluded that damage information can be extracted effectively by the WPT and the identification accuracy of the NNEs is better than that of individual neural networks (INNs).

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