Steel and Composite Structures

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Steel-UHPC composite dowels' pull-out performance studies using machine learning algorithms

  • Zhihua Xiong (College of Water Resources and Architectural Engineering, Northwest A&F University) ;
  • Zhuoxi Liang (College of Water Resources and Architectural Engineering, Northwest A&F University) ;
  • Xuyao Liu (College of Water Resources and Architectural Engineering, Northwest A&F University) ;
  • Markus Feldmann (Institute of Steel Construction, RWTH Aachen University) ;
  • Jiawen Li (College of Water Resources and Architectural Engineering, Northwest A&F University)
  • Received : 2022.07.18
  • Accepted : 2023.08.03
  • Published : 2023.09.10
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Abstract

Composite dowels are implemented as a powerful alternative to headed studs for the efficient combination of Ultra High-Performance Concrete (UHPC) with high-strength steel in novel composite structures. They are required to provide sufficient shear resistance and ensure the transmission of tensile forces in the composite connection in order to prevent lifting of the concrete slab. In this paper, the load bearing capacity of puzzle-shaped and clothoidal-shaped dowels encased in UHPC specimen were investigated based on validated experimental test data. Considering the influence of the embedment depth and the spacing width of shear dowels, the characteristics of UHPC square plate on the load bearing capacity of composite structure, 240 numeric models have been constructed and analyzed. Three artificial intelligence approaches have been implemented to learn the discipline from collected experimental data and then make prediction, which includes Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Extreme Learning Machine (ELM). Among the factors, the embedment depth of composite dowel is proved to be the most influential parameter on the load bearing capacity. Furthermore, the results of the prediction models reveal that ELM is capable to achieve more accurate prediction.

Keywords

Acknowledgement

The research is supported by Elite Scholar Program of Northwest A&F University and CSC Funding (Grant No. Z111022001 and No. 202206305001).

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