Artificial neural network for technical feasibility prediction of seismic retrofitting in existing RC structures

Roberto Falcone, Angelo Ciaramella, Francesco Carrabs, Nicola Strisciuglio, Enzo Martinelli*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
111 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)1220-1234
Number of pages15
JournalStructures
Volume41
Early online date28 May 2022
DOIs
Publication statusPublished - Jul 2022

Keywords

  • Artificial Neural Networks
  • Computational Intelligence
  • Earthquake engineering
  • Seismic retrofitting
  • 22/3 OA procedure

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