Prediction of Shear Strength of RC Deep Beams using Neuro-Fuzzy Inference system and Meta-Heuristic Algorithms

Document Type : Research Papers


1 Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan, Bandar Abbas, Iran

2 Department of Civil Engineering, University of Hormozgan

3 Department of Civil Engineering, University of Birjand, Birjand, Iran


It is generally accepted that shear strength of reinforced concrete deep beams varies depending on the mechanical and geometrical parameters of the beam. Accurate estimation of shear strength is a substantial issue in engineering design. However, the prediction of shear strength in this type of beams is not very accurate. One of the relatively accurate methods of estimating shear strength is the use of artificial intelligence. In this study, the applicability of adaptive neuro-fuzzy inference system using meta-heuristic algorithms for predicting shear strength of reinforced concrete beams was examined and the results were compared with existing regulations. For this purpose, the parameters of concrete compressive strength, cross-section width, effective depth, beam length, shear span-to-depth beam ratio (a/d), as well as percentage of longitudinal and transverse reinforcement were selected as input, and shear strength of reinforced concrete deep beam as output parameter. Here, after the selection of appropriate input and outputs, K-fold validation method with k = 10 was used to train and test the algorithms. The results showed that the model of neural fuzzy inference system with differential evolutionary optimization algorithm with second root mean square error of 25.968 and correlation coefficient of 0.914 is more accurate than other methods.


Articles in Press, Accepted Manuscript
Available Online from 03 July 2022
  • Receive Date: 03 December 2021
  • Revise Date: 11 June 2022
  • Accept Date: 18 June 2022
  • First Publish Date: 03 July 2022