TY - JOUR
T1 - Artificial neural network for technical feasibility prediction of seismic retrofitting in existing RC structures
AU - Falcone, Roberto
AU - Ciaramella, Angelo
AU - Carrabs, Francesco
AU - Strisciuglio, Nicola
AU - Martinelli, Enzo
N1 - Funding Information:
This paper is part of the research project entitled “Tecniche di ottimizzazione strutturale per l’adeguamento sismico di edifici in cemento armato” developed under the financial support provided by the Department of Civil Engineering (DICiv) of the University of Salerno (FARB 2019).
Publisher Copyright:
© 2022 Institution of Structural Engineers
PY - 2022/7
Y1 - 2022/7
N2 - The seismic analysis of reinforced concrete (RC) structures generally requires significant computational effort, which can be challenging or at least time-consuming also for the modern computing systems. Particularly, huge computational effort is required for running optimisation procedures intended at selecting the “best” retrofitting solution among the wide set of technical feasible ones. Therefore, this paper proposes the use of Machine Learning instead of the mechanistic analyses executed as part of an optimisation procedure for seismic retrofitting of RC existing structures recently proposed by the authors. Specifically, an Artificial Neural Network is trained and employed as a possible substitute of finite element analysis for a rapid and accurate assessment of the relevant performance exhibited by the enhanced configurations of an RC existing building typology. The obtained results demonstrate the effectiveness of an artificial neural network as a computational model to approximate a finite element analysis in seismic retrofitting of RC structures by considering several structural configurations. The proposed methodology can be used to speed-up the search of a viable RC strengthening configuration within the whole parametric field of relevance, which can be subsequently refined using more detailed and computationally expensive FE methods.
AB - The seismic analysis of reinforced concrete (RC) structures generally requires significant computational effort, which can be challenging or at least time-consuming also for the modern computing systems. Particularly, huge computational effort is required for running optimisation procedures intended at selecting the “best” retrofitting solution among the wide set of technical feasible ones. Therefore, this paper proposes the use of Machine Learning instead of the mechanistic analyses executed as part of an optimisation procedure for seismic retrofitting of RC existing structures recently proposed by the authors. Specifically, an Artificial Neural Network is trained and employed as a possible substitute of finite element analysis for a rapid and accurate assessment of the relevant performance exhibited by the enhanced configurations of an RC existing building typology. The obtained results demonstrate the effectiveness of an artificial neural network as a computational model to approximate a finite element analysis in seismic retrofitting of RC structures by considering several structural configurations. The proposed methodology can be used to speed-up the search of a viable RC strengthening configuration within the whole parametric field of relevance, which can be subsequently refined using more detailed and computationally expensive FE methods.
KW - Artificial Neural Networks
KW - Computational Intelligence
KW - Earthquake engineering
KW - Seismic retrofitting
KW - 22/3 OA procedure
UR - http://www.scopus.com/inward/record.url?scp=85131095242&partnerID=8YFLogxK
U2 - 10.1016/j.istruc.2022.05.008
DO - 10.1016/j.istruc.2022.05.008
M3 - Article
AN - SCOPUS:85131095242
SN - 2352-0124
VL - 41
SP - 1220
EP - 1234
JO - Structures
JF - Structures
ER -