Geomechanics and Engineering

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Optimised neural network prediction of interface bond strength for GFRP tendon reinforced cemented soil

  • Zhang, Genbao (College of Civil Engineering, Hunan City University) ;
  • Chen, Changfu (Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University) ;
  • Zhang, Yuhao (School of Civil and Environmental Engineering, University of New South Wales) ;
  • Zhao, Hongchao (School of Geology and Mining Engineering, Xinjiang University) ;
  • Wang, Yufei (Institute for Smart City of Chongqing University in Liyang, Chongqing University) ;
  • Wang, Xiangyu (School of Design and Built Environment, Curtin University)
  • Received : 2021.05.30
  • Accepted : 2022.02.04
  • Published : 2022.03.25
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Abstract

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

Keywords

Acknowledgement

The research described in this paper was financially supported by the National Natural Science Foundation of China (grant numbers 51908201 and 51978254), Natural Science Foundation of Hunan Province (grant number 2020JJ5024), and the Key R&D Project of Hunan Province Intelligent Disaster Prevention and Mitigation and Ecological Restoration in Civil Engineering (grant number 2020SK2109). Meanwhile, this work was supported by Hunan Key Laboratory of Intelligent Disaster Prevention and Mitigation and Ecological Restoration in Civil Engineering, Hunan Provincial Engineering Research Center, Catastrophe and Reinforcement of Dangerous Engineering Structures. This research was also supported by Academic Research Council of Australia Linkage Projects for Asset Intelligence: Maximising Operational Effectiveness for Digital Era (Grant No. LP180100222).

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