Contact point accelerations, instantaneous curvature, and physics-based damage detection and location using vehicle-mounted sensors

Erduran, Emrah; Gonen, Semih

doi:10.1016/j.engstruct.2024.117608

Cited by 5 publications

(2 citation statements)

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“…Numerical simulations demonstrated the successful detection and localization of damage using the IAS at the driving frequency. Erduran and Gonen [49] introduced a novel damage detection method that establishes a physical link between contact point accelerations and the instantaneous curvature of a bridge. This approach facilitates effective and precise damage detection and localization in bridges through drive-by monitoring methods.…”

Section: Introductionmentioning

confidence: 99%

“…The main reason for omitting this parameter lies in the underlying principle of the proposed framework, which aims to capture the static response of the structure at low frequencies. Thus, the proposed framework is relatively immune to the effects of measurement noise, which is typically proportional to the square root of the bridge frequency, as indicated by most accelerometer manufacturers [49]. Additionally, Yang et al [64] demonstrated that noise tends to affect bridge frequencies more prominently in the high frequency region, above 22 Hz, while having minimal impact on the first few frequencies.…”

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confidence: 97%

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Drive-by Bridge Damage Detection Using Continuous Wavelet Transform

Demirlioglu,

Erduran

2024

Applied Sciences

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Bridges serve as vital engineering structures crafted to facilitate secure and effective transportation networks. Throughout their life-cycle, they withstand various factors, including diverse environmental conditions, natural hazards, and substantial loads. Recent bridge failures underscore the significant risks posed to the structural integrity of bridges. Damage detection techniques, being core components of structural health monitoring, play a crucial role in objectively assessing bridge conditions. This article introduces a novel framework for identifying damage in bridges utilizing continuous wavelet analysis of accelerations recorded using two sensors mounted on a vehicle traversing the bridge. The proposed method leverages changes in the static response of the bridge, which has proven to be more sensitive to damage than its dynamic counterpart. By doing so, the method eliminates the reliance on modal parameters for damage identification, addressing a significant challenge in the field. The proposed framework also addresses key challenges encountered by drive-by monitoring methods. It mitigates the adverse effects of road roughness by utilizing residual accelerations and efficiently detects and locates damage even in the absence of corresponding data from an undamaged bridge. Numerical investigations demonstrate the robustness of the proposed method against various parameters, including damage location and extent, vehicle speeds, road roughness levels, different boundary conditions, and multi-damage scenarios.

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Section: Introductionmentioning

confidence: 99%

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confidence: 97%

Drive-by Bridge Damage Detection Using Continuous Wavelet Transform

Demirlioglu,

Erduran

2024

Applied Sciences

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Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders

de Souza,

Bragança,

Ribeiro

et al. 2024

Railw. Eng. Sci.

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High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety. In this context, drive-by methodologies have emerged as a feasible and cost-effective monitoring solution for detecting damage on railway bridges while minimizing train operation interruptions. Moreover, integrating advanced sensor technologies and machine learning algorithms has significantly enhanced structural health monitoring (SHM) for bridges. Despite being increasingly used in traditional SHM applications, studies using autoencoders within drive-by methodologies are rare, especially in the railway field. This study presents a novel approach for drive-by damage detection in HSR bridges. The methodology relies on acceleration records collected from multiple bridge crossings by an operational train equipped with onboard sensors. Log-Mel spectrogram features derived from the acceleration records are used together with sparse autoencoders for computing statistical distribution-based damage indexes. Numerical simulations were performed on a 3D vehicle–track–bridge interaction system model implemented in Matlab to evaluate the robustness and effectiveness of the proposed approach, considering several damage scenarios, vehicle speeds, and environmental and operational variations, such as multiple track irregularities and varying measurement noise. The results show that the proposed approach can successfully detect damages, as well as characterize their severity, especially for very early-stage damages. This demonstrates the high potential of applying Mel-frequency damage-sensitive features associated with machine learning algorithms in the drive-by condition assessment of high-speed railway bridges.

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