PSI - Issue 44

ScienceDirect Structural Integrity Procedia 00 (2022) 000–000 Structural Integrity Procedia 00 (2022) 000–000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com Sci nceDire t Available online at www.sciencedirect.com ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Procedia Structural Integrity 44 (2023) 2020–2027

© 2023 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 the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy. Abstract The “Italian guidelines for the maintenance of bridges” propose a qualitative method for the classification of structural, seismic, hydraulic and geotechnical risk of infrastructures. Focusing on the structural risk, one of the key parameters that significantly drives the evaluation of the vulnerability class is the level of defectiveness. The level of defectiveness can be determined only after the execution of the visual inspections, which are necessary to point out the type of damages that affect the structure, their intensity, size and position in each structural component of the bridge. Given the high number of structures to be checked and the time that is necessary to execute the visual inspections of all these bridges, an instrument to have a starting idea of the conservation status of the structure could be helpful to establish an order of priority for the bridges to be investigated. With such purpose in mind, this paper presents an ongoing activity based on the use of Artificial Intelligence to develop a smart tool that recognizes the different elements that compose the bridge, the defects, their intensity, size and position. This tool could be applied to an automatic image collection process, e.g. using a drone that, with minimal user interaction, captures images of the structure and provides quick feedback to the operators. © 2022 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 the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy Keywords: Bridges visual inspections; Damage detection; Artificial Intelligence; Deep Learning. XIX ANIDIS Conference, Seismic Engineering in Italy Artificial Intelligence tools to predict the level of defectiveness of existing bridges Agnese Natali a *, Milind G. Padalkar c , Vincenzo Messina b , Walter Salvatore a , Pietro Morerio c , Alessio Del Bue c , Carlos Beltrán-González c a Department of Industrial and Civil Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy b Consorzio Fabre, Lar o Lucio Lazzarino 1, 56122 Pisa, Italy c Pattern Analysis and Computer Vision (PAVIS), Fondazione Istituto Italiano di Tecnologia (IIT), 16163 Genova, Italy Abstract The “Italian guidelines for the maintenance of bridges” propose a qualitative method for the classification of structural, seismic, hydraulic a d geotechnical risk of infr structures. Focusing on the struc ural risk, one of the key p rameter tha significantly drives the evaluation of the vulnerability class is the level of defectiveness. The level of def ctiveness can be de ermined on after the xecution of the visua inspections, which are n cessary to poi t out t typ f amages that affect the structure, their in nsity, size and p sition n each tructural component of the bridge. Given he high number of structures to be checked and the time that s necessary to ex cute he visual inspections of all th se bridges, an i strument to hav a starting idea of conserv ion statu of the struct r could be helpful to establish an ord r of priority for the bridges to b investigated. With such purpose in mind, this paper presents an ongoing activity based o the use of A tificial Intell gence to develop a smart tool that recognizes the ifferent elements h t c mpose the bridge, the def cts, their intensity, size a d p sition. This tool c u d be applied to an automatic image collection process, e.g. using a drone that, with minimal u er interaction, capture images of th structure and provides quick f edback t the perators © 2022 The Authors. Publish d 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 u der re ponsibility of scientific committe of the XIX ANIDIS C nference, Seismic Engineering in Italy Keywords: Bridges visual inspections; Damage detection; Artificial Intelligence; Deep Learning. XIX ANIDIS Conference, Seismic Engineering in Italy Artificial Intelligence tools to predict the level of defectiveness of existing bridges Agnese Natali a *, Milind G. Padalkar c , Vincenzo Messina b , Walter Salvatore a , Pietro Morerio c , Alessio Del Bue c , Carlos Beltrán-González c a Department of Industrial and Civil Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy b Consorzio Fabre, Largo Lucio Lazzarino 1, 56122 Pisa, Italy c Pattern Analysis and Computer Vision (PAVIS), Fondazione Istituto Italiano di Tecnologia (IIT), 16163 Genova, Italy

* Agnese Natali. Tel.: +39-050-22-18-246 E-mail address: agnese.natali@dici.unipi.it * Agnese Natali. Tel.: +39-050-22-18-246 E-mail address: agnese.natali@dici.unipi.it

2452-3216 © 2022 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 the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy 2452-3216 © 2022 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 the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy

2452-3216 © 2023 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 the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy. 10.1016/j.prostr.2023.01.258