Extraction of contact-point response in indirect bridge health monitoring using an input estimation approach

Nayek, Rajdip; Narasimhan, Sriram

doi:10.1007/s13349-020-00418-z

Cited by 38 publications

(11 citation statements)

References 36 publications

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“…To assess the performance of the proposed approach with noise contaminated signals, two scenarios with 5% and 10% noise are considered. The noisy measurement is simulated as 25 F G U R E 2 Response residuals of CP and their spectra for different speeds…”

Section: Effect Of Measurement Noisementioning

confidence: 99%

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

Zhu

Guo

2022

Structural Contr & Hlth

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Summary The drive‐by bridge health monitoring is to assess the bridge condition using the acceleration responses measured on the body or axle of instrumented vehicles. The vehicle responses are greatly affected by the road surface roughness that makes the bridge dynamic information blurred. Instead of direct using vehicle responses for the bridge monitoring, the dynamic response of contact point (CP) between the vehicle and bridge is further explored to enhance the drive‐by bridge modal identification. A novel three‐step framework is proposed to extract the components related to the bridge vibration from vehicle responses. The first step is to identify the input forces of two successive vehicles by solving the combined state‐input estimation problem using dual Kalman filter. The CP responses of two contact points are calculated using the input forces and vehicle parameters. In the second step, the subtraction technique is applied to the identified CP responses of the two instrumented vehicles and the effect of the road surface roughness can be significantly reduced. Finally, an automatic singular spectrum analysis technique (auto SSA) is incorporated to decompose the response residual. Then the mono‐component modes related to the bridge response are extracted from the response residual for drive‐by bridge modal identification and/or the nonstationary characteristic identification of vehicle–bridge interaction (VBI) system. Results of numerical and experimental study demonstrate that the method can significantly suppress the vehicle response component and reduce the effect of road surface roughness to enhance the bridge modal identification.

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Section: Effect Of Measurement Noisementioning

confidence: 99%

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

Zhu

Guo

2022

Structural Contr & Hlth

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“…The work by Yang et al is the only known field-test of this approach, however, to extend the approach to more commonly used vehicles, the vehicle suspension and vibration of the vehicle body need to be considered. Since the use of the CP-response was initially proposed by Yang et al (2018) additional numerical studies have been carried out in this area with only one study, by Nayek and Narasimhan (2020), extending the formulation for the CP-response beyond the single degree of freedom model initially proposed by Yang et al (2018). Nayek and Narasimhan (2020) use the CPresponse for a quarter-car model, however they use an inputstate estimation procedure employing a Gaussian process latent force model to estimate the CP-response.…”

Section: Introductionmentioning

confidence: 99%

“…Since the use of the CP-response was initially proposed by Yang et al (2018) additional numerical studies have been carried out in this area with only one study, by Nayek and Narasimhan (2020), extending the formulation for the CP-response beyond the single degree of freedom model initially proposed by Yang et al (2018). Nayek and Narasimhan (2020) use the CPresponse for a quarter-car model, however they use an inputstate estimation procedure employing a Gaussian process latent force model to estimate the CP-response. This approach employs a complex optimisation procedure to estimate the CP-response and requires a number of assumptions to be made, while also requiring information on the vehicle properties.…”

Section: Introductionmentioning

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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“…Recent studies have shown that by solving an inverse problem -deconvolving the measured cabin accelerations to recover the tire-level input -the detrimental suspension effects could be mitigated [10,11]. To incorporate error in modeling and measurements, [12] proposed a Gaussian process latent force model that successfully estimated the road input for a linear, damped, two degrees of freedom (DOF) quarter car suspension model. However, all these models require complete knowledge about the system which is less practical for real-world applications.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Input Estimation of Vehicle Systems

Cronin

Eshkevari

Sen

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This study proposes a learning-based method with domain adaptability for input estimation of vehicle suspension systems. In a crowdsensing setting for bridge health monitoring, vehicles carry sensors to collect samples of the bridge's dynamic response. The primary challenge is in preprocessing; signals are highly contaminated from road profile roughness and vehicle suspension dynamics. Additionally, signals are collected from a diverse set of vehicles vitiating model-based approaches. In our data-driven approach, two autoencoders for the cabin signal and the tire-level signal are constrained to force the separation of the tire-level input from the suspension system in the latent state representation. From the extracted features, we estimate the tire-level signal and determine the vehicle class with high accuracy (98% classification accuracy). Compared to existing solutions for the vehicle suspension deconvolution problem, we show that the proposed methodology is robust to vehicle dynamic variations and suspension system nonlinearity.

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Extraction of contact-point response in indirect bridge health monitoring using an input estimation approach

Cited by 38 publications

References 36 publications

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

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

Transfer Learning for Input Estimation of Vehicle Systems

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