Steel and Composite Structures

  • 제36권5호
  • /
  • Pages.493-506
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  • 2020
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Load-slip curves of shear connection in composite structures: prediction based on ANNs

  • Guo, Kai (School of Civil Engineering, Qingdao University of Technology) ;
  • Yang, Guotao (School of Civil Engineering, Qingdao University of Technology)
  • 투고 : 2019.11.21
  • 심사 : 2020.07.31
  • 발행 : 2020.09.10
⟨ 이전 논문 다음 논문 ⟩

초록

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 twolayer 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.

키워드

과제정보

This research is sponsored by National Natural Science Foundation of China (No. 51808308 and No. 51978351) and State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, China (No. SLDRCE17-02). This support is gratefully acknowledged.

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