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A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

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Abstract

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

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Correspondence to Ali Shariati.

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Shariati, M., Mafipour, M.S., Mehrabi, P. et al. A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques. Engineering with Computers (2020). https://doi.org/10.1007/s00366-019-00930-x

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Keywords

  • Tilted angle shear connectors
  • System identification
  • Adaptive neuro-fuzzy inference system
  • Extreme learning machine
  • Artificial neural network