Structural Damage Detection Using Auto/Cross-Correlation Functions Under Multiple Unknown Excitations

2014 ◽  
Vol 14 (05) ◽  
pp. 1440006 ◽  
Author(s):  
Pinghe Ni ◽  
Yong Xia ◽  
Siu-Seong Law ◽  
Songye Zhu
Keyword(s):  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Detection Methods ◽  
Structural System ◽  
Vibration Responses ◽  
Structural System Identification

Traditional structural system identification and damage detection methods use vibration responses under single excitation. This paper presents an auto/cross-correlation function-based method using acceleration responses under multiple ambient white noise or impact excitations. The auto/cross-correlation functions are divided into two parts. One is associated with the structural parameters and the other with the energy of the excitation. These two parts are updated sequentially using a two-stage method. Numerical and experimental studies are conducted to demonstrate the accuracy and robustness of the proposed method. The effects of measurement noise and number of measurement points on the identification results are also studied.

A Multi-State Strategy for Structural Damage Detection Using Sensitivity of Weighted Transmissibility Function

2021 ◽  
pp. 2150144
Author(s):  
Ziwei Luo ◽  
Huanlin Liu ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Fe Model ◽  
Limited Information ◽  
Structural Damage Detection ◽  
State Strategy ◽  
Output Only

In practice, a model-based structural damage detection (SDD) method is helpful for locating and quantifying damages with the aid of reasonable finite element (FE) model. However, only limited information in single or two structural states is often used for model updating in existing studies, which is not reasonable enough to represent real structures. Meanwhile, as an output-only damage indicator, transmissibility function (TF) is proven to be effective for SDD, but it is not sensitive enough to change in structural parameters. Therefore, a multi-state strategy based on weighted TF (WTF) is proposed to improve sensitivity of TF to change in parameters and in order to further obtain a more reasonable FE model for SDD in this study. First, WTF is defined by TF weighted with element stiffness matrix, and relationships between WTFs and change in structural parameters are established based on sensitivity analysis. Then, a multi-state strategy is proposed to obtain multiple structural states, which is used to reasonably update the FE model and detect structural damages. Meanwhile, due to fabrication errors, a two-stage scheme is adopted to reduce the global and local discrepancy between the real structure and the FE model. Further, the [Formula: see text]-norm and the [Formula: see text]-norm regularization techniques are, respectively, introduced for both model updating and SDD problems by considering the characteristics of problems. Finally, the effectiveness of the proposed method is verified by a simply supported beam in numerical simulations and a six-storey frame in laboratory. From the simulation results, it can be seen that the sensitivity to structural damages can be improved by the definition of WTF. For the experimental studies, compared with the FE model updated from the single structural state, the FE model obtained by the multi-state strategy has an ability to more reasonably describe the change of states in the frame. Moreover, for the given structural damages, the proposed method can detect damage locations and degrees accurately, which shows the validity of the proposed method and the reliability of the updated FE model.

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.

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.

scholarly journals Decision Fusion for Structural Damage Detection: Numerical and Experimental Studies

2010 ◽  
Vol 2010 ◽  
pp. 1-12
Author(s):  
Yong Chen ◽  
Senyuan Tian ◽  
Bingnan Sun
Keyword(s):  
Damage Detection ◽  
System Performance ◽  
Structural Damage ◽  
Model Updating ◽  
Experimental Studies ◽  
Decision Fusion ◽  
Experimental Results ◽  
Detection Methods ◽  
Correct Selection ◽  
Fusion Strategy

This paper describes a decision fusion strategy that can integrate multiple individual damage detection measures to form a new measure, and the new measure has higher probability of correct detection than any individual measure. The method to compute the probability of correct selection is presented to measure the system performance of the fusion system that includes the presented fusion strategy. And parametric sensitive studies on system performance are also conducted. The superiority of the fusion strategy herein is that it can be extended to deal with the multiresolution subdecision or blind adaptive detection, and corresponding methodologies are also provided. Finally, an experimental setup was fabricated, whereby the vibration properties of damaged and undamaged structures were measured. The experimental results with the undamaged structural model provide information for producing an improved theoretical and numerical model via model updating techniques. Three existing vibration-based damage detection methods with varied resolutions were utilized to identify the damage that occurred in the structure, based on the experimental results. Then the decision fusion strategy was implemented to join the subdecisions from these three methods. The fused results are shown to be superior to those from single method.

Online Tracking of Structural Parameters Based on Recursive Subspace Identification

2010 ◽  
Author(s):  
Jian-Huang Weng ◽  
Chin-Hsiung Loh
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Shaking Table Test ◽  
Structural Parameters ◽  
Shaking Table ◽  
External Loading ◽  
Subspace Identification ◽  
Structural System ◽  
Real Time System ◽  
On Line

An important objective of structural health monitoring (SHM) for civil infrastructure is to identify the state of the structure and to detect these changes when it occurred. The changes of features in a structural system may due to the nonlinear inelastic response of structure or due to the structural damage subjected to severe external loading. Therefore, damage detection in large structural system, such as buildings and bridges, can improve safety and reduce maintenance costs and the design of damage detection system is one of the goals of SHM. The objective of this paper is to develop an on-line and almost real-time system parameter estimation and damage detection technique from the response measurements through using the Recursive Subspace identification (RSI) or Recursive Stochastic Subspace Identification (RSSI) approaches. Verification of the proposed method by using data from both numerical simulation and the shaking table test of a steel structure with abrupt change of inter-story stiffness are discussed. With the implementation of forgetting factor in RSSI/RSI methods the ability of on-line damage detection of structural system can be enhanced.

Synthesis of Cross-Correlation Functions of Partial Responses and the Extended Kalman Filter Approach for Structural Damage Detection Under Ambient Excitations

2018 ◽  
Vol 18 (08) ◽  
pp. 1840003 ◽  
Author(s):  
Y. Lei ◽  
D. D. Xia ◽  
F. Chen ◽  
Y. M. Deng
Keyword(s):  
Kalman Filter ◽  
Damage Detection ◽  
White Noise ◽  
Extended Kalman Filter ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Product Models ◽  
Structural Responses ◽  
Measurement Noises

It is still necessary to investigate the detection of structural damage under ambient excitations since the excitations are random and unmeasured while measurement noises are inevitable. In this paper, a method based on the synthesis of cross-correlation functions of partial structural responses and the extended Kalman filter (EKF) approach is proposed for the identification and damage detection of structures under ambient excitations, in which both independent stationary and non-stationary white noise excitations in the product models are discussed. First, the equations of cross-correlation functions of structural responses are established when the ambient excitations are independent stationary white noise processes. Then, the EKF approach is utilized to identify structural parameters and cross-correlation functions using partial measurements of structural acceleration responses. Structural damage is detected based on the degradations of the identified structural element stiffness parameters. Finally, the proposed method is extended to deal with independent non-stationary white noise excitations in the product models. The numerical simulation examples of the ASCE structural health monitoring benchmark building subject to ambient excitation, a moment resisting frame model under white noise excitation, and a cantilever beam model under multiple independent non-stationary excitations are used to validate the feasibility of the proposed method. It is shown that the method is not sensitive to measurement noises. Also, a lab experimental study of the identification of a multi-story shear structure is investigated to further illustrate the applicability of the proposed method.

Non-baseline method for damage detection in truss structures using displacement and strain measurements

2018 ◽  
Vol 22 (3) ◽  
pp. 818-830 ◽  
Author(s):  
Peng Ren ◽  
Zhi Zhou ◽  
Jinping Ou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Experimental Studies ◽  
Measurement Data ◽  
Element Model ◽  
Truss Structures ◽  
Detection Methods ◽  
Detection Approach ◽  
Potential Development ◽  
System States

Realistic problems restrict the application of many existing structural damage detection methods. Due to the requirement of a comparison between two system states, lack of appropriate baseline data may become one of the limitations to undertake structural health monitoring strategy. This article suggests a non-baseline damage detection approach based on the mixed measurements and the transmissibility concept and demonstrates it in truss structures. The algorithm uses the measurement data from the strains of the truss elements and the displacements of the truss joints, in which the displacements are utilized to estimate the baseline strains based on the transmissibility matrix from an initial finite element model. Wavelet-based damage-sensitive features are extracted from both estimated and measured strains to detect damages of the target elements. Numerical and experimental studies are performed to investigate the feasibility and effectiveness of the proposed approach. It is concluded from the instances that the robustness of the algorithm is realized when handling the measurement noise, modeling errors and the operational condition variability. These permit the potential development of the damage detection method for real structures in site.

A Study on Structural System Identification and Damage Detection for Free Vibration Response Using the Wavelet Transform

2015 ◽  
Vol 752-753 ◽  
pp. 1029-1034
Author(s):  
Asnizah Sahekhaini ◽  
Pauziah Muhamad ◽  
Masayuki Kohiyama ◽  
Aminuddin Abu ◽  
Lee Kee Quen ◽  
...  
Keyword(s):  
Free Vibration ◽  
Damage Detection ◽  
Vibration Response ◽  
Structural System ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Complex Morlet Wavelet ◽  
Structural System Identification ◽  
Free Vibration Response ◽  
Energy Components

This paper presents a wavelet-based method of identification modal parameter and damage detection in a free vibration response. An algorithm for modal parameter identification and damage detection is purposed and complex Morlet wavelet is chosen as an analysis wavelet function. This paper only focuses on identification of natural frequencies of the structural system. The method utilizes both undamaged and damage experiment data of free vibration response of the truss structure system. Wavelet scalogram is utilizes for damage detection. The change of energy components for undamaged and damage structure is investigated from the plot of wavelet scalogram which corresponded to the detection of damage.

Sign in / Sign up

Export Citation Format

Share Document