Damage detection using data-driven methods applied to moving-load responses

Cavadas, Filipe; Smith, Ian F. C.; Figueiras, Joaquim

doi:10.1016/j.ymssp.2013.02.019

Cited by 84 publications

(55 citation statements)

References 18 publications

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“…Zaurin and Cabas (2011) proposed a method in which video images and sensor data are correlated and used to create a series of unit influence lines that were used as input features to detect and localize damage in the structure. Cavadas et al (2013) presented a data-driven method for bridge health monitoring using moving-load responses and only quasi-static structural response was used. The moving principal component analysis is used to extract the principal components inside a moving window of constant size and damage was identified when the principal component coordinate value exceeds the threshold bounds.…”

Section: Pattern Recognition Algorithmsmentioning

confidence: 99%

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Zhu

Law

2015

Advances in Structural Engineering

118

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The use of the vehicle-bridge interaction (VBI) data for structural health monitoring has received considerable interest in the last decade. Compared with the traditional bridge health monitoring, the VBI based approach allows the target bridges to be monitored or assessed under operating conditions. The VBI system has time-variant features and the vehicle can serve as a moving exciter and a mobile sensor in the system. Many bridge damage identification techniques based on VBI have been developed, and they could be divided into three categories, namely, technique based on the bridge responses, technique based on the vehicle responses and technique based on both the vehicle and bridge responses. This paper presents a review on the structural health monitoring based on VBI and the challenges for its general implementation in practice.

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Section: Pattern Recognition Algorithmsmentioning

confidence: 99%

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Zhu

Law

2015

Advances in Structural Engineering

118

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“…Direct use of vibration data for feature extraction is believed to be one of the efficient methods for structural damage detection [7]. One of the common approaches for data-driven damage detection is to use a regressive model to train the data and extract damage sensitive features.…”

Section: Time Series Analysis For Damage Detectionmentioning

confidence: 99%

Data-Driven Structural Damage Identification Using DIT

Shahidi

Yao

Chamberlain

et al. 2015

Conference Proceedings of the Society for Experimental Mechanics Series

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Vibration-based damage detection research aims to develop efficient algorithms to identify structural damage from monitoring data. One of the main categories of such algorithms is data-driven techniques which extract features from measured signals, and identify the damage by evaluating the significance of potential changes in these features. This paper presents application of several data-driven damage identification methodologies on a multivariate simulated data set. First, general regression models are applied to data collected through clusters of sensors and damage sensitive features are extracted. For systems with linear topology, it is shown that substructural regression modeling can also be performed on time-and frequency-domain transforms of the measured signals to estimate local stiffness of the structure as damage features. Subsequently, change detection techniques are utilized to statistically determine the significance of changes in the extracted features in order to distinguish between assignable changes as a result of damage and common changes due to environmental factors. Finally, a toolsuite is developed to facilitate application of the developed algorithms and improve the damage identification performance through incorporation of various combinations of regression models, damage features and statistical tests. IntroductionThe ultimate goal of vibration-based structural health monitoring (SHM) research is to develop efficient algorithms capable of detecting the time, location, and severity of any damage induced changes in structural components of monitored systems. Toward this goal, multitudes of methods have been proposed over recent decades that commonly establish a baseline for selected damage features and monitor their change from the baseline in the following unknown health conditions. Various features have been used for the purpose of damage identification; uncertain parameters of a finite element (FE) simulation of the structure that is calibrated with reference to modal quantities extracted using system identification (SID) algorithms [1,2], or features that are directly extracted from measured signals without using FE or SID procedures to train the data [3][4][5][6]. Each group of these damage identification methods has their own advantages and drawbacks. The first group is usually more laborious to implement and require certain a priori knowledge of structural properties, and location of damage; however, the calibrated model could be beneficial in design of repair scenarios or estimating the remaining life of the structure. The main advantage of the second group is their efficiency, and that they can be readily applied to measured signals without any prior information. Therefore, their application for a general automated damage detection platform is more promising. However, these data-driven methods are ineffective without statistical analyses to determine the change threshold for the extracted features. This paper presents application of multiple data-driven damage detection pr...

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“…The technique uses a single-point measurement and is able to identify the presence and the location of the damage along the beam. Cavadas et al 23 discussed the application of data-driven methods on moving load responses to detect the occurrence and the location of damage. However, these dynamic detection algorithms are faced with some difficulties in applications.…”

Section: Introductionmentioning

confidence: 99%

Damage localization for beam structure by moving load

Yang

Liu

Sun

et al. 2017

Advances in Mechanical Engineering

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A static algorithm is presented in this article for damage localization in beam structures by moving load. Central to the damage localization approach is the combination of the moving load technique and the pure bending theory. The first advantage of the presented algorithm is that only a few sensors are needed in the static test. The second advantage of the presented algorithm is that only the deflection parameters of the current structure are needed in damage localization process. The proposed method is applied to a simple supported beam and its feasibility is verified using damage simulations. It was found that the defect location can be identified by inspecting the sudden increase in the curvature of the deflection curve for the beam. The results showed that the presented algorithm is effective on the localization of single damage or multiple damages in the beam structure without baseline data.

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Damage detection using data-driven methods applied to moving-load responses

Cited by 84 publications

References 18 publications

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Data-Driven Structural Damage Identification Using DIT

Damage localization for beam structure by moving load

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