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