scholarly journals Experimental investigations on the cross-correlation function amplitude vector of the dynamic strain under varying environmental temperature for structural damage detection

2018 ◽  
Vol 39 (3) ◽  
pp. 631-649
Author(s):  
Miao Li ◽  
Wei-Xin Ren ◽  
Tian-Li Huang ◽  
Ning-Bo Wang
Keyword(s):  
Correlation Function ◽  
Damage Detection ◽  
Temperature Effect ◽  
Structural Damage ◽  
Cross Correlation ◽  
Environmental Temperature ◽  
Cross Correlation Function ◽  
Dynamic Strain ◽  
Experimental Investigations ◽  
Structural Damage Detection

This article focuses on the experimental investigations on the cross-correlation function amplitude vector of the dynamic strain (CorV_S) under varying environmental temperature for structural damage detection. It is verified that under white noise excitation, CorV_S is only related to the natural frequencies, mode shapes, and damping ratios of structures. The normalized CorV_S of the undamaged structure maintains a uniform shape. A laboratory experimental investigation based on an end-fixed steel beam shows that CorV_S can be used for structural damage detection. However, CorV_S constructed by the dynamic strain of in-situ test varies with time, and the CorV_S curves do not have the same shape. When the environmental temperature fluctuates significantly, high correlation exists between the dynamic strain and environmental temperature. By analyzing the power spectral density of the signals measured from active and inactive strain gauges, it is found that the signals induced by temperature stress, which do not reflect the dynamic performance of the bridge, exist in the very low-frequency band. To avoid the interference to CorV_S, the temperature effect component is separated from the dynamic strain by analytical mode decomposition method. Then, each CorV_S curve maintains a uniform shape. The results demonstrate that it is prone to get a misjudgment for the condition of a structure if temperature effect on CorV_S is ignored. It is necessary to eliminate the environmental temperature effect on CorV_S for the damage detection of a structure in service.

Damage Detection Based on the Cross Correlation Function Amplitude Vector and its Application to the ASCE Benchmark Structure

2007 ◽  
Vol 353-358 ◽  
pp. 2317-2320 ◽  
Author(s):  
Zhe Feng Yu ◽  
Zhi Chun Yang
Keyword(s):  
Correlation Function ◽  
Damage Detection ◽  
Structural Damage ◽  
Cross Correlation ◽  
Relative Difference ◽  
Random Excitation ◽  
Cross Correlation Function ◽  
Vibration Responses ◽  
Specific Frequency ◽  
The Cross

A new method for structural damage detection based on the Cross Correlation Function Amplitude Vector (CorV) of the measured vibration responses is presented. Under a stationary random excitation with a specific frequency spectrum, the CorV of the structure only depends on the frequency response function matrix of the structure, so the normalized CorV has a specific shape. Thus the damage can be detected and located with the correlativity and the relative difference between CorVs of the intact and damaged structures. With the benchmark problem sponsored by ASCE Task Group on Structural Health Monitoring, the CorV is proved an effective approach to detecting the damage in structures subject to random excitations.

Damage Detection of Historic Timber Structure Based on Cross Correlation Function Amplitude Vectors

2011 ◽  
Vol 368-373 ◽  
pp. 2442-2446
Author(s):  
Yan Fang Hou ◽  
Wei Bing Hu
Keyword(s):  
Correlation Function ◽  
Damage Detection ◽  
Structural Damage ◽  
Random Vibration ◽  
Cross Correlation ◽  
Timber Structure ◽  
Cross Correlation Function ◽  
Vector Criterion ◽  
Before And After ◽  
Relative Change

Cross Correlation Function Amplitude Vector(CorV) is a method of damage detection which is based on random vibration .In this paper, CorV is introduced in the damage detection of historic timber structure according to the characteristics of structure and damage.Meanwhile,the research has been done. CorV of structural damage before and after the change has been expressed that is based on Cross Correlation function amplitude Vector Criterion(CVAC) .Results show that there is a remarkable decrease of CVAC among the CorVs between damaged and intact structures.Damage locations can be determined through the relative change of CorVs which is before or after the damage of the structure . A basis can be provided for the damage of buildings and the ancient structure protection through this paper.

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

2020 ◽  
pp. 147592172094283 ◽  
Author(s):  
Zhiqiang Shang ◽  
Limin Sun ◽  
Ye Xia ◽  
Wei Zhang
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Deep Neural Networks ◽  
Beam Model ◽  
Structural Damage Detection ◽  
Denoising Autoencoder ◽  
Vibration Data

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

Structural Damage Detection with Cross Correlation Function of Vibration Observations

2010 ◽  
Vol 163-167 ◽  
pp. 2776-2779
Author(s):  
Jia Yan Lei ◽  
Qian Feng Yao ◽  
Ying Lei
Keyword(s):  
Correlation Function ◽  
Structural Damage ◽  
Cross Correlation ◽  
Damage Identification ◽  
Function Analysis ◽  
Cross Correlation Function ◽  
Building Model ◽  
Identification Technique ◽  
Test Points ◽  
Shear Building

A structural damage identification technique based on cross correlation function analysis of vibration measurements is proposed. An 8-storey steel shear building model has been chosen as the case verification. Structural acceleration responses from neighbouring test points are used to establish damage identification parameters. Experimental analysis shows that the method can achieve quite precise results.

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