Prediction of lateral confinement coefficient in reinforced concrete columns using neural network simulation

Alacali S., Akbas B., DORAN B.

APPLIED SOFT COMPUTING, vol.11, no.2, pp.2645-2655, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 2
  • Publication Date: 2011
  • Doi Number: 10.1016/j.asoc.2010.10.013
  • Journal Name: APPLIED SOFT COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2645-2655
  • Keywords: Neural network, Confinement coefficient, Volumetric ratio, Reinforced concrete columns, Ductility, DAMAGE DETECTION, STRENGTH, DESIGN, DUCTILITY, BRIDGES
  • Yıldız Technical University Affiliated: Yes

Abstract

This paper presents an application of Neural Network (NN) simulation in civil engineering science. The confinement degree for confined concrete has been investigated by using a NN analysis as an alternative approach. To accurately predict the behavior of a confined concrete, it is important to understand the confinement degree and its individual components. For the purpose of investigating confinement effects, three empirical equations as a function of various parameters and an experimental work existing in the literature were considered in this study. However, these analytical models are time consuming to use. Therefore, there is still the need to develop simple but accurate method for determining the confinement coefficient. In this context, the NN algorithm has been established, in order to validate these empirical equations proposed for the confinement coefficient. The approach adapted in this study was shown to be capable of providing accurate estimates of lateral confinement coefficient, K-s by using the six design parameters. Finally, comparison with other empirical equations proposed for the lateral confinement coefficient illustrates the validity of the proposed algorithm. (C) 2010 Elsevier B.V. All rights reserved.