Framework for the development of artificial neural networks for predicting the load carrying capacity of RC members

Afaq Ahmad*, Demitrios M. Cotsovos, Nikos D. Lagaros

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


This paper aims at establishing a framework for the development of artificial neural networks (ANNs) capable of realistically predicting the load-carrying capacity of reinforced concrete (RC) members. Multilayer back propagation neural networks are developed through the use of MATLAB and enriched databases which contain information describing the variation of load-carrying capacity in relation to key design parameters associated with the RC specimens (i.e. beams) considered. This work forms the basis for the development of a knowledge-based structural analysis tool capable of predicting RC structural response. A detailed discussion is provided on the different aspects of the proposed framework which include (1) the formation and analysis of the relevant (experimental) data, (2) the architecture of the ANNs, (3) the training/calibration process they undergo and finally, (4) ways of extending their applicability enabling them to predict the behaviour of RC structural forms with design parameters not represented in the available experimental database. Non-linear finite element analysis is used for validating the predictions of the ANN models developed. The comparative study reveals that the ANN models developed through the proposed framework are capable of effectively predicting the load-carrying capacity s of the RC structural forms considered quickly, accurately and without requiring significant computational resources.

Original languageEnglish
Article number545
JournalSN Applied Sciences
Issue number4
Early online date3 Mar 2020
Publication statusPublished - Apr 2020


  • Artificial neural network
  • Database
  • Failure
  • Finite element analysis
  • Latin hypercube sampling
  • Reinforced concrete
  • Sampling method
  • Training process
  • Ultimate limit state

ASJC Scopus subject areas

  • General Engineering
  • General Environmental Science
  • General Materials Science
  • General Physics and Astronomy
  • General Chemical Engineering
  • General Earth and Planetary Sciences


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