A new approach to determine strength of Perfobond rib shear connector in steel-concrete composite structures by employing neural network

Hamed Allahyari, Iman M. Nikbin, Saman Rahimi R., Amin Heidarpour

Research output: Contribution to journalArticleResearchpeer-review

22 Citations (Scopus)


The main objective of this study is to introduce a novel numerical approach, based on Artificial Neural Network (ANN), to predict the shear strength of Perfobond rib shear connector (PRSC). For this purpose, 90 records were extracted from the literature and were used to develop a number of Bayesian neural network models for predicting the shear strength of PRSC. An accurate ANN model was attained with a high value of correlation coefficient for the train and test subsets. Having a reliable ANN, a parametric study on the shear strength of PRSC was carried out to establish the trend of main contributing factors. The majority of assumptions, considered by empirical equations, were predicted by the developed ANN. Moreover, a sensitivity analysis of input variables was conducted; the outcomes revealed that the area of concrete dowels had the strongest influence on the shear strength of PRSC. Eventually, using the validated ANN, an abundant number of curves (Master Curves) were generated to introduce a user-friendly equation. According to the results, both the ANN model and the proposed equation reflect a higher accuracy than other existing empirical equations.

Original languageEnglish
Pages (from-to)235-249
Number of pages15
JournalEngineering Structures
Publication statusPublished - 15 Feb 2018


  • ANN
  • Composite
  • Empirical equation
  • Parametric study
  • Sensitivity analysis
  • Shear connector

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