The study presented in this paper analyses and investigates the possibility of introducing a general and rapid methodology based on an artificial neural network (ANN) to assess the seismic response of existing reinforced concrete (RC) buildings. Starting from investigations carried out on buildings located in the outskirts of Bologna, 928 finite element models have been developed on the basis of the most recurrent data. The input parameters representing the characteristics of the structures were systematically varied and, through modal dynamic and non-linear static analyses, the outputs representing the seismic response were recorded. The resulting dataset was used to create a function, based on ANN, that can reliably predict the seismic behaviour of a RC structure. Finally, by means of k-fold cross-validation, the instruction of the function was optimised and simultaneously verified, obtaining a coefficient of determination for the totality of the samples and the previously unseen cases of 0,94 and 0,88, respectively. The result obtained not only aims at enriching the existing framework on the subject, increasing the awareness of the seismic issues affecting this building typology, but also constitutes a prioritization system that could highlight the need for structural renovation.

Lorenzo Stefanini, Lorenzo Badini, Giovanni Mochi, Giorgia Predari, Annarita Ferrante (2022). Neural networks for the rapid seismic assessment of existing moment-frame RC buildings. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 67, 1-21 [10.1016/j.ijdrr.2021.102677].

Neural networks for the rapid seismic assessment of existing moment-frame RC buildings

Lorenzo Stefanini
Primo
;
Lorenzo Badini
Secondo
;
Giorgia Predari;Annarita Ferrante
2022

Abstract

The study presented in this paper analyses and investigates the possibility of introducing a general and rapid methodology based on an artificial neural network (ANN) to assess the seismic response of existing reinforced concrete (RC) buildings. Starting from investigations carried out on buildings located in the outskirts of Bologna, 928 finite element models have been developed on the basis of the most recurrent data. The input parameters representing the characteristics of the structures were systematically varied and, through modal dynamic and non-linear static analyses, the outputs representing the seismic response were recorded. The resulting dataset was used to create a function, based on ANN, that can reliably predict the seismic behaviour of a RC structure. Finally, by means of k-fold cross-validation, the instruction of the function was optimised and simultaneously verified, obtaining a coefficient of determination for the totality of the samples and the previously unseen cases of 0,94 and 0,88, respectively. The result obtained not only aims at enriching the existing framework on the subject, increasing the awareness of the seismic issues affecting this building typology, but also constitutes a prioritization system that could highlight the need for structural renovation.
2022
Lorenzo Stefanini, Lorenzo Badini, Giovanni Mochi, Giorgia Predari, Annarita Ferrante (2022). Neural networks for the rapid seismic assessment of existing moment-frame RC buildings. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 67, 1-21 [10.1016/j.ijdrr.2021.102677].
Lorenzo Stefanini; Lorenzo Badini; Giovanni Mochi; Giorgia Predari; Annarita Ferrante
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Descrizione: Fast overall assessment of seismic vulnerability of existing RC buildings through generation and training of neural networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/840816
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