Modal Identification of a Railway Bridge Under Train Crossings: A Comparative Study

Gönen, Semih; Demirlioglu, Kultigin; Erduran, Emrah

doi:10.1007/978-3-031-05449-5_4

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…BHM systems are usually designed based on the structural response observed on an a-priori FE model (Ye et al, 2020). Although model-based BHM approaches (Gonen and Soyoz, 2021;Gonen et al, 2023) can predict future bridges' structural response under idealized load scenarios, their use results unfeasible for real-time damage detection applications (due to the high computational resources and the relatively long simulation periods required). With the rapid surge and adoption of AI, a new BHM and damage detection paradigm have recently gained importance: the model-free, also known as data-driven, paradigm.…”

Section: Bhm Ai and Adasmentioning

confidence: 99%

Insights: Frontiers in Built Environment

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Bhm Ai and Adasmentioning

confidence: 99%

Insights: Frontiers in Built Environment

2024

Frontiers Research Topics

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“…BHM systems are usually designed based on the structural response observed on an a-priori FE model (Ye et al, 2020). Although model-based BHM approaches (Gonen and Soyoz, 2021;Gonen et al, 2023) can predict future bridges' structural response under idealized load scenarios, their use results unfeasible for real-time damage detection applications (due to the Frontiers in Built Environment frontiersin.org high computational resources and the relatively long simulation periods required). With the rapid surge and adoption of AI, a new BHM and damage detection paradigm have recently gained importance: the model-free, also known as data-driven, paradigm.…”

Section: Bhm Ai and Adasmentioning

confidence: 99%

Bridge management through digital twin-based anomaly detection systems: A systematic review

Rios

Plevris

Nogal

2023

Front. Built Environ.

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Bridge infrastructure has great economic, social, and cultural value. Nevertheless, many of the infrastructural assets are in poor conservation condition as has been recently evidenced by the collapse of several bridges worldwide. The objective of this systematic review is to collect and synthesize state-of-the-art knowledge and information about how bridge information modeling, finite element modeling, and bridge health monitoring are combined and used in the creation of digital twins (DT) of bridges, and how these models could generate damage scenarios to be used by anomaly detection algorithms for damage detection on bridges, especially in bridges with cultural heritage value. A total of 76 relevant studies from 2017 up to 2022 have been taken into account in this review. The synthesis results show a consensus toward the future adoption of DT for bridge design, management, and operation among the scientific community and bridge practitioners. The main gaps identified are related to the lack of software interoperability, the required improvement of the performance of anomaly-detection algorithms, and the approach definition to be adopted for the integration of DT at the macro scale. Other potential developments are related to the implementation of Industry 5.0 concepts and ideas within DT frameworks.

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“…Typically, these methods leverage changes in structural modal characteristics before and after damage occurs, making them capable of detecting not only surface faults but also sensitive to interior damage. However, conventional vibration-based methods usually necessitate multiple sensors and specialized data collection systems tailored to specific applications [2][3][4][5][6][7][8][9]. The custom nature and associated costs of traditional SHM approaches often pose obstacles for large-scale monitoring initiatives.…”

Section: Introductionmentioning

confidence: 99%

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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Modal Identification of a Railway Bridge Under Train Crossings: A Comparative Study

Cited by 10 publications

References 15 publications

Insights: Frontiers in Built Environment

Insights: Frontiers in Built Environment

Bridge management through digital twin-based anomaly detection systems: A systematic review

Drive-by Bridge Damage Detection Using Continuous Wavelet Transform

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