Structural damage diagnosis of steel truss bridges by outlier detection

Kim, C.W.; Kitauchi, S.; Chang, Kai-Chun; McGetrick, Patrick; Sugiura, Kunitomo; Kawatani, Mitsuo

doi:10.1201/b16387-670

Cited by 9 publications

(10 citation statements)

References 11 publications

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“…A linear dynamic system can be modelled by using AR model that describes a time-varying process where current output series depends linearly on the previous value of the series. AR coefficients are extensively used as damage sensitive feature in many SHM applications [6], [10], [11]. The AR model of order d can be described as [12]…”

Section: Damage Sensitive Feature Extractionmentioning

confidence: 99%

“…Another most adopted damage detection approach is based on Mahalanobis square distance. In MSD based data normalization approach [10], [18], Damage index of any test feature vector i z is defined as:…”

Section: Data Descriptionmentioning

confidence: 99%

“…However, these features need to be extracted from vibration signal measured in a very controlled environment that is very difficult in real-time applications [4]. On the other hand, Auto-Regressive (AR) coefficients are extensively used as damage sensitive feature that are related to the natural frequencies and damping of the structure [9], [10]. Also, it is very easy to estimate the AR coefficients from the vibration signals.…”

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confidence: 99%

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Statistical Damage Detection Approach in SHM Based on Error Prediction Model

Kumar¹,

Biswas²,

Dhang³

2016

IJSCER

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Vibration-based structure health monitoring technique detects the damage by observing the change in dynamic characteristics of the structure. Change in dynamic characteristics due to operational and environmental variation may confuse with the change due to damage in the structure resulting false alarm of the damage. In SHM, data normalization technique can be used to suppress the adverse effect due to different operational and environmental variability. In this paper, a data normalization approach based on error prediction model is presented that estimates the residuals of the vibration feature due to damage. Damage is detected by processing the residual errors after applying Principal Component Analysis (PCA) on vibration features. The residual errors due to operational and environmental variabilities are optimally minimized through the best reconstruction of vibration features using an optimal number of principal components. The Variance of Reconstruction Error (VRE) is applied to obtain the optimum number of principal components for best reconstruction of vibration features. Relative standard deviation of the residual errors is used as damage index that quantifies the level of the damage in the structure. The proposed approach is validated on a benchmark problem of detecting damage in a three-story building under different operational and environmental variabilities. A comparative analysis is performed with previously reported work for damage detection to test the efficacy of the proposed algorithm. 

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Section: Damage Sensitive Feature Extractionmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

mentioning

confidence: 99%

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Statistical Damage Detection Approach in SHM Based on Error Prediction Model

Kumar¹,

Biswas²,

Dhang³

2016

IJSCER

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“…These methods can be time consuming and prone to human error and thereby are unreliable and inconsistent in their findings. As such, the SHM of railway bridges is increasingly becoming an area of interest . Most of the researchers working on the SHM of railway bridges have focused on the numerical and experimental simulation of truss bridges (examples include research conducted by Kopsaftopoulos and Fassois, Wang et al, Nuno, and Siriwardena) .…”

Section: Introductionmentioning

confidence: 99%

Damage detection of steel girder railway bridges utilizing operational vibration response

Azim

Gül

2019

Struct Control Health Monit

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In this paper, we develop a damage identification framework based on acceleration responses for railroad bridges. The methodology uses sensor-clusteringbased time series analysis of bridge acceleration responses to the motion of the train. The results are expressed in terms of damage features, and damage to the bridge is investigated by observing the magnitude of these damage features. The investigation demonstrates the damage features by comparing the fit ratios of locations of interest so that damage can be identified and located and the relative severity of the damage assessed. The damage cases considered are stiffness loss, moment capacity reduction, and change in boundary conditions. In this study, a finite element analysis of a railway bridge model is used to verify our methodology. Our findings show that the proposed damage detection framework is very promising for continuously assessing the condition of railway bridges and thus will facilitate early detection of potential structural damage. This will be valuable for infrastructure owners seeking to develop more economical and effective maintenance strategies.

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“…7,8 In SHM, many researchers have addressed the damage detection problem as outlier detection/novelty detection in varying operational and environmental conditions. [9][10][11][12][13] Meruane and Heylen proposed a model-based damage detection approach to detect, locate and quantify the damage on a three span bridge under varying temperatures. 14 In these methods, operational and environmental variabilities are considered as implicitly embedded in structural responses.…”

Section: Introductionmentioning

confidence: 99%

Damage Diagnosis of Steel Truss Bridges under Varying Environmental And Loading Conditions

Kumar¹,

Biswas²,

Dhang³

2019

The International Journal of Acoustics and Vibration

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In this paper, we propose a damage detection and localization algorithm for steel truss bridges using a data-driven approach under varying environmental and loading conditions. A typical steel truss bridge is simulated in ANSYS for data generation. Damage is introduced by reducing the stiffness of one or more members of the truss bridge. The simulated acceleration time-history signals are used for the purpose of damage diagnosis purpose. Vibration data collected from healthy bridges are processed through principal component analysis (PCA) to find the reduced size weighted feature vectors in model space. Unknown test vibration data (healthy or damaged) finds the closest match of its reduced size model from the training database containing only healthy vibration data. The residual error between the spread of closest healthy vibration data and unknown test vibration data is processed to determine damage location and severity of the damage to the structure. A comparative study between a proper orthogonal decomposition (POD) based damage detection algorithm and proposed algorithm is presented. The results show that the proposed algorithm is efficient to identify the damage location and assess the severity of damage, called as the Damage Index (DI), under varying environmental and moving load conditions.

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Structural damage diagnosis of steel truss bridges by outlier detection

Cited by 9 publications

References 11 publications

Statistical Damage Detection Approach in SHM Based on Error Prediction Model

Statistical Damage Detection Approach in SHM Based on Error Prediction Model

Damage detection of steel girder railway bridges utilizing operational vibration response

Damage Diagnosis of Steel Truss Bridges under Varying Environmental And Loading Conditions

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