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

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.

Automated summary

This paper proposes the use of Machine Learning as a substitute for traditional mechanistic analyses in seismic retrofitting of reinforced concrete structures. The obtained results demonstrate the effectiveness of an artificial neural network in rapidly and accurately assessing the performance of different structural configurations, and it can be used to speed up the search for the best retrofitting solution.