Shear Capacity of Headed Studs in Steel-Concrete Structures: Analytical Prediction via Soft Computing

Miguel Abambres (1) , Jun He (2)

1 Abambres' Lab
2 School of Civil Eng, Changsha University of Science and Technology

Headed studs are commonly used as shear connectors to transfer longitudinal shear force at the interface between steel and concrete in composite structures (e.g., bridge decks). Code-based equations for predicting the shear capacity of headed studs are summarized. An artificial neural network (ANN)-based analytical model is proposed to estimate the shear capacity of headed steel studs. 234 push-out test results from previous published research were collected into a database in order to feed the simulated ANNs. Three parameters were identified as input variables for the prediction of the headed stud shear force at failure, namely the steel stud tensile strength and diameter, and the concrete (cylinder) compressive strength. The proposed ANN-based analytical model yielded, for all collected data, maximum and mean relative errors of 3.3 % and 0.6 %, respectively. Moreover, it was illustrated that, for that data, the neural network approach clearly outperforms the existing code-based equations, which yield mean errors greater than 13 %.

Mots clés: Headed Studs; Analytical Model; Steel-Concrete Structures; Shear Capacity; Artificial Neural Networks; Shear Connectors; Push-Out Test

Domaines: Génie civil; Réseau de neurones [cs.NE]; Ingénierie assistée par ordinateur; Intelligence artificielle [cs.AI]; Ingénierie, finance et science [cs.CE]

Miguel Abambres, Jun He. Shear Capacity of Headed Studs in Steel-Concrete Structures: Analytical Prediction via Soft Computing. 2019. ⟨hal-02074833v3⟩