Drive-by bridge damage monitoring using Bridge Displacement Profile Difference

Elhattab, Ahmed; Uddin, Nasim; O’Brien, Eugene J.

doi:10.1007/s13349-016-0203-6

Cited by 56 publications

(25 citation statements)

References 30 publications

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“…The vehicle is modeled as a quarter-car shown in Figure 3. This model has been extensively used in the literature [25,26,27] as it illustrates many of the important characteristics of VBI [28]. The quarter-car has two independent degrees of freedom corresponding to body mass and axle mass translations.…”

Section: Numerical Modelingmentioning

confidence: 99%

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

Malekjafarian

Golpayegani

Moloney

et al. 2019

Sensors

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This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels.

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Section: Numerical Modelingmentioning

confidence: 99%

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

Malekjafarian

Golpayegani

Moloney

et al. 2019

Sensors

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show abstract

“…To numerically examine the proposed method, a Vehicle Bridge Interaction model [53] has been adopted to simulate the vehicle crossing over the bridge. The VBI model has been built using the fully-integrated Euler-Bernoulli Beam element, which has two degrees of freedom per node (vertical displacement and out of plane rotation).…”

Section: Extract Bridge Frequency From a Fast Passing Vehicle Signmentioning

confidence: 99%

Drive-By Bridge Frequency Identification under Operational Roadway Speeds Employing Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR)

Elhattab

Uddin

O’Brien

2018

Sensors

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Recently, drive-by bridge inspection has attracted increasing attention in the bridge monitoring field. A number of studies have given confidence in the feasibility of the approach to detect, quantify, and localize damages. However, the speed of the inspection truck represents a major obstacle to the success of this method. High speeds are essential to induce a significant amount of kinetic energy to stimulate the bridge modes of vibration. On the other hand, low speeds are necessary to collect more data and to attenuate the vibration of the vehicle due to the roughness of the road and, hence, magnify the bridge influence on the vehicle responses. This article introduces Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR) as a new technique, which possesses the ability to extract bridge dynamic properties from the responses of a vehicle that passes over the bridge at high speed. Stochastic Resonance (SR) is a phenomenon where feeble information such as weak signals can be amplified through the assistance of background noise. In this study, bridge vibrations that are present in the vehicle responses when it passes over the bridge are the feeble information while the noise counts for the effect of the road roughness on the vehicle vibration. UPSR is one of the SR models that has been chosen in this study for its suitability to extract the bridge vibration. The main contributions of this article are: (1) introducing a Frequency Independent-Stochastic Resonance model known as the FI-UPSR and (2) implementing this model to extract the bridge vibration from the responses of a fast passing vehicle.

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“…The road surface profile is not considered in this simulation. The dynamic interaction between the vehicle and the bridge is implemented in MATLAB [10,26] based on the contact force concept adopted by Yang et al [27] . and Gonzalez [28] .…”

Section: Numerical Simulationmentioning

confidence: 99%

“…The novel technique of using the bridge as an instrument to infer the dynamic wheel forces was first investigated by [4] . Since then the field of moving force identification (MFI) has progressed rapidly [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] . The MFI problem is an inverse dynamics process and like many, is ill-conditioned which means the solution is very sensitive to small perturbations in measurements, which requires the regularization approach to overcome this problem.…”

Section: Introductionmentioning

confidence: 99%

B-Wim System Using Fewer Sensors

Mohammed

Uddin²

2018

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Bridge Weigh-in-Motion (B-WIM) is the concept of using measured response on a bridge to calculate the static weights of passing traffic loads as they pass overhead at full highway speed. This paper describes an enhancement to the Moving Force Identification (MFI) algorithm by estimating the response of some DOFs using limited number of measurements in order to increase measurements number (Input). The pseudoinverse of the mode shape matrix has been utilized to approximately calculate the modal response using limited measured response. Then the calculated modal response has been used to estimate more DOFs that are different from the measured one. The proper orthogonal decomposition (POD) technique is employed to determine the governing modes that increase the modal response accuracy. Numerical example for quarter car model passing over simply supported bridge has been established to demonstrate the idea.

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Drive-by bridge damage monitoring using Bridge Displacement Profile Difference

Cited by 56 publications

References 30 publications

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

Drive-By Bridge Frequency Identification under Operational Roadway Speeds Employing Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR)

B-Wim System Using Fewer Sensors

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