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Steel and Composite Structures Volume 36, Number 5, September10 2020 , pages 493-506 DOI: https://doi.org/10.12989/scs.2020.36.5.493 |
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Load-slip curves of shear connection in composite structures: prediction based on ANNs |
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Kai Guo and Guotao Yang
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| Abstract | ||
| The load-slip relationship of the shear connection is an important parameter in design and analysis of composite structures. In this paper, a load-slip curve prediction method of the shear connection based on the artificial neural networks (ANNs) is proposed. The factors which are significantly related to the structural and deformation performance of the connection are selected, and the shear stiffness of shear connections and the transverse coordinate slip value of the load-slip curve are taken as the input parameters of the network. Load values corresponding to the slip values are used as the output parameter. A two-layer hidden layer network with 15 nodes and 10 nodes is designed. The test data of two different forms of shear connections, the stud shear connection and the perforated shear connection with flange heads, are collected from the previous literatures, and the data of six specimens are selected as the two prediction data sets, while the data of other specimens are used to train the neural networks. Two trained networks are used to predict the load-slip curves of their corresponding prediction data sets, and the ratio method is used to study the proximity between the prediction loads and the test loads. Results show that the load-slip curves predicted by the networks agree well with the test curves. | ||
| Key Words | ||
| load-slip curve; artificial neural networks; shear stiffness; stud shear connection; perforated shear connection with flange heads | ||
| Address | ||
| Kai Guo: School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China; College of Architecture and Civil Engineering, Beijing University of Technology, Beijinng 100124, China Guotao Yang: School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China | ||