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 ScienceDirect Available online at www.sciencedirect.com ScienceDirect

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

Procedia Structural Integrity 44 (2023) 1704–1711

© 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 Nowadays governments are encouraging the upgrading and the reuse (rather than the demolition) of older structures in order to reduce land use and environmental impact due to the construction of new buildings. The choice of the most suitable intervention for the seismic upgrading of existing structures could also be addressed combining member-level (e.g., FRP-confinement of single columns) and structural-level (e.g., insertion of steel bracing systems) techniques, although it may result in a complex technical challenge for engineers, since a huge number of combinations of technically feasible upgrading interventions are theoretically possible to accomplish the desired structural performance. In order to support the intervention choice, an “objective” approach could be implemented making use of recently-invented Artificial Intelligence (AI) procedures. Specifically, the application of Genetic Algorithms (GAs) is usually thought as a suitable optimization procedure in several civil engineering problems. By means of a Genetic Algorithm (GA), the design of upgrading interventions results to be based on one objective criterion related to the cost-effectiveness of the intervention, rather than on the highly subjective “engineering-judgement”. The present paper aims to highlight the capability of a Soft-Computing (SC) procedure in selecting the most cost-effective combination of the aforementioned member-level and structural-level interventions, among the technically consistent ones. To this aim a parametric study on a RC structure based on a similar GA procedure is reported, varying some “engineering parameters” related to the target seismic risk class of the upgraded structure. Relevant differences in the results are observed due to the variation of target of the analyses. © 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: RC buildings; structural optimization; seismic upgrading; Artificial Intelligence; Genetic Algorithm Abstract Nowadays governments are encouraging the upgrading and the reuse (rather than the demolition) of older structures in order to reduce land use a d vironmental impac due to the construction of new buildings. Th cho ce of the most suitabl intervention for the seismic upgrading of existing structures could also be addressed comb ni member-level (e.g., FRP-confineme of single c lumn ) and structural-level (e.g., insertion of steel bracing syst ms) techn ques, although it may result in a compl x tech ical halle ge for engineers, since a huge number of combinations of technically f asible pgrading interventions are theoretically possible to accomplish the d sired structural perfor ance. In order to support the intervention choice, a “ bjective” appro ch c uld be imple ented making use of recently-invented Artificial Intelligence (AI) proc dures. Spe ifically, the application of Genetic Algorithms (GAs) is us ally thought as a suitable op mization procedure in several civil engineering problems. By means of a Genetic Algorithm (GA), the design of upgrad ng interventions results to be based on one objective criterion related to the cost-effec veness of the intervention, rather than on the highly subjective “engineering-judgement”. The present paper aims to highlight he capability of a Soft-Computing (SC) procedure in selecting h most cost-effective combination of th aforementioned member-lev l nd structural-level interve tions, among th techni ally consistent on s. To this aim a parametric study o a RC structure based on a simil r GA proc dure s reported, varying some “engineeri g parameters” related to th targe seismic risk class of the upgraded structure. Relevant differ nces in the result ar observed due to the variation of targe of he analy es. © 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 u der re ponsibility of e scientific committe of the XIX ANIDIS C nference, Seismic Engineering in Italy Keywords: RC buildings; structural optimization; seismic upgrading; Artificial Intelligence; Gen tic Algorithm XIX ANIDIS Conference, Seismic Engineering in Italy Seismic upgrading of RC structures through an optimization XIX ANIDIS Conference, Seismic Engineering in Italy Seismic upgrading of RC structures through an optimization procedure based on Genetic Algorithm Francesco Nigro a , Roberto Falcone a , Enzo Martinelli a * a Department of Civil Engineering, University of Salerno, Salerno 84084, Italy procedure based on Genetic Algorithm Francesco Nigro a , Roberto Falcone a , Enzo Martinelli a * a Department of Civil Engineering, University of Salerno, Salerno 84084, Italy

* Corresponding author. Tel.: +39-089-96-4098; fax: +39-089-96-4098. E-mail address: e.martinelli@unisa.it * Corresponding author. Tel.: +39-089-96-4098; fax: +39-089-96-4098. E-mail address: e.martin lli@unisa.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.2023.01.218