Predicting diagonal cracking strength of RC slender beams without stirrups using ANNs

Keskin R. S. O., ARSLAN G.

COMPUTERS AND CONCRETE, vol.12, no.5, pp.697-715, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 5
  • Publication Date: 2013
  • Doi Number: 10.12989/cac.2013.12.5.697
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.697-715
  • Keywords: artificial neural networks, reinforced concrete, slender beams, diagonal cracking, shear strength, REINFORCED-CONCRETE BEAMS, ARTIFICIAL NEURAL-NETWORKS, SHEAR DESIGN PROCEDURE, WEB REINFORCEMENT, DEEP BEAMS, FAILURE, SIZE, INTELLIGENCE, PROPAGATION, CAPACITY
  • TED University Affiliated: No


Numerous studies have been conducted to understand the shear behavior of reinforced concrete (RC) beams since it is a complex phenomenon. The diagonal cracking strength of a RC beam is critical since it is essential for determining the minimum amount of stirrups and the contribution of concrete to the shear strength of the beam. Most of the existing equations predicting the diagonal cracking strength of RC beams are based on experimental data. A powerful computational tool for analyzing experimental data is an artificial neural network (ANN). Its advantage over conventional methods for empirical modeling is that it does not require any functional form and it can be easily updated whenever additional data is available. An ANN model was developed for predicting the diagonal cracking strength of RC slender beams without stirrups. It is shown that the performance of the ANN model over the experimental data considered in this study is better than the performances of six design code equations and twelve equations proposed by various researchers. In addition, a parametric study was conducted to study the effects of various parameters on the diagonal cracking strength of RC slender beams without stirrups upon verifying the model.