<p>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 %.</p>