Neural network-based prediction: The case of reinforced concrete members under simple and complex loading

Afaq Ahmad, Nikos D. Lagaros, Demetrios M. Cotsovos

Research output: Contribution to journalArticlepeer-review

11 Downloads (Pure)

Abstract

The objective of this study is to compare conventional models used for estimating the load carrying capacity of reinforced concrete (RC) members, i.e., Current Design Codes (CDCs), with the method based on different assumptions, i.e., the Compressive Force Path (CFP) method and a nonconventional problem solver, i.e., an Artificial Neural Network (ANN). For this purpose, four different databases with the details of the critical parameters of (i) RC beams in simply supported conditions without transverse steel or stirrups (BWOS) and RC beams in simply supported conditions with transverse steel or stirrups (BWS), (ii) RC columns with cantilever-supported conditions (CWA), (iii) RC T-beams in simply supported conditions without transverse steel or stirrups (TBWOS) and RC T-beams in simply supported conditions with transverse steel or stirrups (TBWS) and (iv) RC flat slabs in simply supported conditions under a punching load (SCS) are developed based on the data from available experimental studies. These databases obtained from the published experimental studies helped us to estimate the member response at the ultimate limit-state (ULS). The results show that the predictions of the CFP and the ANNs often correlate closer to the experimental data as compared to the CDCs.

Original languageEnglish
Article number4975
JournalApplied Sciences
Volume11
Issue number11
Early online date28 May 2021
DOIs
Publication statusPublished - 1 Jun 2021

Keywords

  • ACI
  • ANN
  • CFP
  • Columns
  • EC2
  • RC beam
  • Slab
  • T-beam

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint

Dive into the research topics of 'Neural network-based prediction: The case of reinforced concrete members under simple and complex loading'. Together they form a unique fingerprint.

Cite this