PSI - Issue 62

ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2022) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2022) 000 – 000 Available online at www.sciencedirect.com Procedia Structural Integrity 62 (2024) 832–839

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II Fabre Conference – Existing bridges, viaducts and tunnels: research, innovation and applications (FABRE24) A Bayesian network-based framework for SHM data fusion supporting bridge management II Fabre Conference – Existing bridges, viaducts and tunnels: research, innovation and applications (FABRE24) A Bayesian network-based framework for SHM data fusion supporting bridge management Laura Ierimonti a, *, Francesco Mariani a , Ilaria Venanzi a , Filippo Ubertini a a) Department of Civil and Environmental Engineering, Via G. Duranti, 06125, Perugia, Italy Abstract Guidelines for Risk Classification and Management, Safety Assessment and Monitoring of Bridges, issued in 2020 by the Italian Ministry of Sustainable Mobility, promote the use of Structural Health Monitoring (SHM) systems for risk assessment of bridges. Prestressed or ordinary reinforced concrete bridges are designed to maintain their functionality over a long period of time. However, during their service life, such structures may be exposed to ever- increasing traffic volumes, extreme weather conditions and/or marine environments and so on. Therefore, constant maintenance and timely interventions are crucial to ensure the functionality of the transportation network. The current management process is mainly based on visual inspections, while the aggregation of information coming from multiple sources is still a major challenge. In this context, the main objective of the present work is to develop a general Bayesian framework capable of processing the different sources of information based on a Bayesian network (BN), a probabilistic graphical model that represents a set of variables and their conditional dependencies. A BN is a useful tool for the management of bridges since it allows the prediction of damage states and failure conditions based on the knowledge by visual inspections and SHM. The combined use of SHM and Bayesian approaches can reduce the overall risk of failure increasing the efficiency of the infrastructure system. The procedure is exemplified and validated with reference to a simply supported post-tensioned case study bridge. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Scientific Board Members Laura Ierimonti a, *, Francesco Mariani a , Ilaria Venanzi a , Filippo Ubertini a a) Department of Civil and Environmental Engineering, Via G. Duranti, 06125, Perugia, Italy Abstract Guidelines for Risk Classification and Management, Safety Assessment and Monitoring of Bridges, issued in 2020 by the Italian Ministry of Sustainable Mobility, promote the use of Structural Health Monitoring (SHM) systems for risk assessment of bridges. Prestressed or ordinary reinforced concrete bridges are designed to maintain their functionality over a long period of time. However, during their service life, such structures may be exposed to ever- increasing traffic volumes, extreme weather conditions and/or marine environments and so on. Therefore, constant maintenance and timely interventions are crucial to ensure the functionality of the transportation network. The current management process is mainly based on visual inspections, while the aggregation of information coming from multiple sources is still a major challenge. In this context, the main objective of the present work is to develop a general Bayesian framework capable of processing the different sources of information based on a Bayesian network (BN), a probabilistic graphical model that represents a set of variables and their conditional dependencies. A BN is a useful tool for the management of bridges since it allows the prediction of damage states and failure conditions based on the knowledge by visual inspections and SHM. The combined use of SHM and Bayesian approaches can reduce the overall risk of failure increasing the efficiency of the infrastructure system. The procedure is exemplified and validated with reference to a simply supported post-tensioned case study bridge. Keywords: Structural health monitoring; Bayesian networks; Bayesian model class selection; Decision making, Bridges.

Keywords: Structural health monitoring; Bayesian networks; Bayesian model class selection; Decision making, Bridges.

* Corresponding author. E-mail address: laura.ierimonti@unipg.it

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Scientific Board Members 10.1016/j.prostr.2024.09.112 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4. 0 ) Peer-review under responsibility of Scientific Board Member s 2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4. 0 ) Peer-review under responsibility of Scientific Board Member s * Corresponding author. E-mail address: laura.ierimonti@unipg.it