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

Genbao Zhang, Changfu Chen, Yuhao Zhang, Hongchao Zhao, Yufei Wang and Xiangyu Wang

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.

Key Words

back propagation neural network; cemented soil; element pullout test; glass fibre reinforced polymer reinforcement; interface bond strength; machine learning; particle swarm optimisation

Address

Genbao Zhang: College of Civil Engineering, Hunan City University, Yiyang, Hunan 413000, PRC; Hunan Engineering Research Center of Structural Safety and Disaster Prevention for Urban Underground Infrastructure, Yiyang, Hunan 413000, PRC Changfu Chen: Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, Changsha, Hunan 410082, PRC; College of Civil Engineering, Hunan University, Changsha, Hunan 410082, PRC Yuhao Zhang: School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia Hongchao Zhao: School of Geology and Mining Engineering, Xinjiang University, Urumchi 830000, China Yufei Wang: Institute for Smart City of Chongqing University in Liyang, Chongqing University, Jiangsu, 213300, China Xiangyu Wang: School of Design and Built Environment, Curtin University, Perth, WA 6102, Australia

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