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Structure and Infrastructure Engineering
Maintenance, Management, Life-Cycle Design and Performance
Volume 19, 2023 - Issue 4
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Article

Risk-based methodology to assess bridge condition based on visual inspection

Numa J. BertolaLaboratory for Maintenance and Safety of Structures (MCS), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, SwitzerlandCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-4151-3123
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Eugen BrühwilerLaboratory for Maintenance and Safety of Structures (MCS), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, SwitzerlandORCID Iconhttps://orcid.org/0000-0003-2321-010X
ORCID Icon
Pages 575-588 | Received 25 Feb 2021, Accepted 25 May 2021, Published online: 03 Aug 2021

Citations (6)

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Read on this site (1)

Francesca Poli, Mattia Francesco Bado, Andrea Verzobio & Daniele Zonta. (2023) Bridge structural safety assessment: a novel solution to uncertainty in the inspection practice. Structure and Infrastructure Engineering 0:0, pages 1-15.
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Articles from other publishers (5)

Angelo Cardellicchio, Sergio Ruggieri, Andrea Nettis, Vito Renò & Giuseppina Uva. (2023) Physical interpretation of machine learning-based recognition of defects for the risk management of existing bridge heritage. Engineering Failure Analysis 149, pages 107237.
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Chao Jiang, Wen Xiong, Zichen Wang, Chunsheng Cai & Juan Yang. (2023) Transverse Connectivity and Durability Evaluation of Hollow Slab Bridges Using Surface Damage and Neural Networks: Field Test Investigation. Applied Sciences 13:8, pages 4851.
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Numa J. Bertola, Guillaume Henriques & Eugen Brühwiler. (2023) Assessment of the information gain of several monitoring techniques for bridge structural examination. Journal of Civil Structural Health Monitoring.
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Numa Bertola, Yves Reuland & Eugen Brühwiler. (2023) Sensing the structural behavior: A perspective on the usefulness of monitoring information for bridge examination. Frontiers in Built Environment 8.
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Xiaoming Lei, Ye Xia, Seyedmilad Komarizadehasl & Limin Sun. (2022) Condition level deteriorations modeling of RC beam bridges with U-Net convolutional neural networks. Structures 42, pages 333-342.
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