Analytical model for the Load-Slip behavior of headed stud shear connectors

2022 ◽  
Vol 252 ◽  
pp. 113631
Author(s):  
Hao Meng ◽  
Wei Wang ◽  
Rongqiao Xu
2020 ◽  
Author(s):  
Abambres M ◽  
He J

<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>


2021 ◽  
Author(s):  
Miguel Abambres ◽  
He J

<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>


2019 ◽  
Author(s):  
Miguel Abambres ◽  
Jun He

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 %.


2020 ◽  
Author(s):  
Abambres M ◽  
He J

<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>


Author(s):  
Mohammed Abdulhussein Al-Shuwaili ◽  
Alessandro Palmeri ◽  
Maria Teresa Lombardo

Push-out tests (POTs) have been widely exploited as an alternative to the more expensive full-scale bending tests to characterize the behaviour of shear connections in steel-concrete composite beams. In these tests, two concrete slabs are typically attached to a steel section with the connectors under investigation, which are then subjected to direct shear. The results allow quantifying the relationship between applied load and displacements at the steel-concrete interface. Since this relationship is highly influenced by the boundary conditions of POT samples, different experimental setups have been used, where the slabs are either restricted or free to slide horizontally, as researchers have tried to reduce any discrepancy between POT and full-scale composite beam testing. Based on a critical review of various POT configurations presented in the dedicated literature, this paper presents an efficient one-sided POT (OSPOT) method. While OSPOT and POT specimens are similar, in the proposed OPSPOT setup only one of the two slabs is directly loaded in each test, and the slab is free to move vertically. Thus, two results can be obtained from one specimen, i.e. one from each slab. A series of POTs and OSPOTs have been conducted to investigate the behaviour and the shear resistance of headed stud connectors through the two methods of testing. The results of this study than were compared with those of different POTs setups conducted by other researchers. The new OSPOT results show in general an excellent agreement with the analytical predictions offered by both British and European standards, as well as the estimated shear resistance proposed other researchers in the literature. These findings suggest that the proposed one-sided setup could be used as an efficient and economical option for conducting the POT, as it has the potential not only to double the number of results, but also to simplify the fabrication of the samples, which is important in any large experimental campaign, and to allow testing with limited capacity of the actuator. 


2011 ◽  
Vol 8 (1) ◽  
pp. 29-34
Author(s):  
M. Youcef ◽  
M. Mimoune ◽  
F. Mimoune

This paper describes the reliability analysis of shear connection in composite beams with profiled steel sheeting. The profiled steel sheeting had transverse ribs perpendicular to the steel beam. The level of safety of shear connection, and failure modes were determinate. An extensive parametric study was conducted to study the effects on the safety and behaviour of shear connection by changing the profiled steel sheeting geometries, the diameter and height of headed stud, as well as the strength of concrete. We compared the level safety calculated using the American specification, British standard and European code for headed stud shear connectors in composite slabs with profiled steel sheeting perpendicular to the steel beam. It is found that the design overestimated the level safety of shear connection.


ce/papers ◽  
2021 ◽  
Vol 4 (2-4) ◽  
pp. 627-634
Author(s):  
Valentino Vigneri ◽  
Christoph Odenbreit ◽  
Dennis Lam ◽  
François Hanus

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