Using drive-by health monitoring to detect bridge damage considering environmental and operational effects

Locke, William R.; Sybrandt, Justin; Redmond, Laura; Safro, Ilya; Atamturktur, Sez

doi:10.1016/j.jsv.2019.115088

Cited by 83 publications

(41 citation statements)

References 31 publications

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“…with FRF and model updating for better identification capability as demonstrated in this paper; (2) The CSAC index in the frequency range between the first two frequencies is found to be sensitive to damage. Thus, this frequency range can be used to identify the damage location and severity in simply supported beams; (3) The objective function can be constructed using CSAC indices of the FRFs between adjacent measurement points.…”

Section: Data Availability Statementmentioning

confidence: 67%

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A Step-by-Step Damage Identification Method Based on Frequency Response Function and Cross Signature Assurance Criterion

Zhan

Zhang

Siahkouhi

2021

Sensors

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This paper aims to present a method for quantitative damage identification of a simply supported beam, which integrates the frequency response function (FRF) and model updating. The objective function is established using the cross-signature assurance criterion (CSAC) indices of the FRFs between the measurement points and the natural frequency. The CSAC index in the frequency range between the first two frequencies is found to be sensitive to damage. The proposed identification procedure is tried to identify the single and multiple damages. To verify the effectiveness of the method, numerical simulation and laboratory testing were conducted on some model steel beams with simulated damage by cross-cut sections, and the identification results were compared with the real ones. The analysis results show that the proposed damage evaluation method is insensitive to the systematic test errors and is able to locate and quantify the damage within the beam structures step by step.

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Section: Data Availability Statementmentioning

confidence: 67%

“…Thus, developing new damage identification methods is necessary. Over the years, many researchers have developed various bridge structural health-monitoring methods [ 1 , 2 , 3 ]. Bridge health assessment is usually conducted using static and/or dynamic methods.…”

Section: Introductionmentioning

confidence: 99%

A Step-by-Step Damage Identification Method Based on Frequency Response Function and Cross Signature Assurance Criterion

Zhan

Zhang

Siahkouhi

2021

Sensors

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“…While the results of the simulation shown in Figure 4 demonstrate the ability of the CP-response to capture the natural frequencies of the bridge, the assumption of a perfectly smooth road surface with no bridge damping is not a realistic one. The influence of the surface roughness of the pavement has been shown to be problematic in drive-by bridge inspection, with the pavement causing the vehicle to vibrate at its own frequencies, covering up the bridge frequencies which are of interest (Hester & González, 2017;Locke et al, 2020).…”

Section: Contact-point Response In the Presence Of Pavement Roughnessmentioning

confidence: 99%

Examining changes in bridge frequency due to damage using the contact-point response of a passing vehicle

Corbally

Malekjafarian

2021

Journal of Structural Integrity and Maintenance

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Ongoing inspection and maintenance of bridges poses a challenging task for infrastructure owners who must manage large bridge stocks with limited budgets. Drive-by monitoring approaches, using sensors in a vehicle, provide a promising solution to this challenge. This paper investigates the use of the response at the point-of-contact between the tyre and the bridge as a means of monitoring bridge frequency. An expression is derived to allow the contact-point (CP) response to be inferred directly from in-vehicle measurements, expanding on previous studies by allowing the vehicle suspension characteristics to be considered. The sensitivity of the CP-response to the pavement characteristics is investigated in detail and a rigid-disk model is used to overcome issues with how existing vehicle-bridge interaction models consider the interaction between the wheel and the pavement. The feasibility of the CP-response as a measure of bridge condition is investigated and results show that the CP-response significantly outperforms the response measured directly on the vehicle. The CP-response is successful in identifying the bridge frequency and changes caused by damage, without being influenced by the vehicle frequencies. Incorporating the CP-response into drive-by bridge monitoring will improve accuracy over existing methods which use the vehicle response alone.

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“…As vehicle speed increases, the road profile effects are amplified, the vehicle bridge interaction (VBI) time reduces, and the resolution of the acceleration signal is significantly depleted. Temperature and other environmental effects have been shown to cause a shift in frequency amplitudes due to the associated change in the stress and strain levels in the bridge that accompany climatic changes [9]. A few studies have suggested that the use of multiple runs is a promising approach to tackle these issues [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning

Malekjafarian

Moloney²,

Golpayegani

2021

Lecture Notes in Civil Engineering

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This paper studies a machine learning algorithm for bridge damage detection using the responses measured on a passing vehicle. A finite element (FE) model of vehicle bridge interaction (VBI) is employed for simulating the vehicle responses. Several vehicle passes are simulated over a healthy bridge using random vehicle speeds. An artificial neural network (ANN) is trained using the frequency spectrum of the responses measured on multiple vehicle passes over a healthy bridge where the vehicle speed is available. The ANN can predict the frequency spectrum of any passes using the vehicle speed. The prediction error is then calculated using the differences between the predicated and measured spectrums for each passage. Finally, a damage indicator is defined using the changes in the distribution of the prediction errors versus vehicle speeds. It is shown that the distribution of the prediction errors is low when the bridge condition is healthy. However, in presence of a damage on the bridge, a recognisable change in the distribution will be observed. Several data sets are generated using the healthy and damaged bridges to evaluate the performance of the algorithm in presence of road roughness profile and measurement noise. In addition, the impacts of the training set size and frequency range to the performance of the algorithm are investigated.

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Using drive-by health monitoring to detect bridge damage considering environmental and operational effects

Cited by 83 publications

References 31 publications

A Step-by-Step Damage Identification Method Based on Frequency Response Function and Cross Signature Assurance Criterion

A Step-by-Step Damage Identification Method Based on Frequency Response Function and Cross Signature Assurance Criterion

Examining changes in bridge frequency due to damage using the contact-point response of a passing vehicle

Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning

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