Development of predictive models for shear strength of HSC slender beams without web reinforcement using machine-learning based techniques

Document Type : Article

Authors

1 Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of ‎Science and Technology, Narmak, Tehran, P.O. Box 16846-13114, Iran‎

2 Department of Civil Engineering, Faculty of Technology and Engineering, University of Guilan, Rudsar-Vajargah, Iran.

3 School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, P.O. Box 16846-13114, Iran

Abstract

Shear failure of slender beams made of high strength concrete (HSC) is one of the most crucial failures in design of reinforced concrete members. The accuracy of the existing design codes for HSC unlike the normal strength concrete (NSC) beams seems to be limited in prediction of shear capacity. This paper proposes a new set of shear strength models for HSC slender beams without web reinforcement using conventional multiple linear regression, advanced machine learning methods of multivariate adaptive regression splines (MARS) and group method of data handling (GMDH) network. In order to achieve high-fidelity and robust regression models, this study employs a comprehensive database including 250 experimental tests. Various influencing parameters including the longitudinal steel ratio, shear span-to-depth ratio, compressive strength of concrete, size of the beam specimens, and size of coarse aggregate are considered. The results indicate that the MARS approach has the best estimation in terms of both accuracy and safety aspects in comparison with regression methods and GMDH approach. Moreover, the accuracy and safety of predictions of MARS model is also remarkably more than the most common design equations. Furthermore, the robustness of proposed models is confirmed through sensitivity and parametric analyses.

Keywords

Main Subjects

References

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Scientia Iranica
Volume 26, Issue 2 - Serial Number 2
Transactions on Civil Engineering (A)
March and April 2019
Pages 709-725

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History

  • Receive Date: 30 March 2017
  • Revise Date: 20 July 2017
  • Accept Date: 11 September 2017

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