Open AccessPerformance assessment of gaussian process regression to predict the bond strength of FRP sheets to concrete
Author(s) -
Nguyen Thuy Anh,
Ly Hai Bang
Publication year - 2021
Publication title -
tạp chí khoa học giao thông vận tải/transport and communications science journal
Language(s) - English
Resource type - Journals
eISSN - 2615-9554
pISSN - 1859-2724
DOI - 10.47869/tcsj.72.4.2
Subject(s) - fibre reinforced plastic , bond strength , ground penetrating radar , structural engineering , shear strength (soil) , materials science , mean squared error , kriging , regression analysis , compressive strength , gaussian , composite material , mathematics , computer science , statistics , engineering , geology , physics , layer (electronics) , soil science , radar , telecommunications , adhesive , quantum mechanics , soil water
A Gaussian process regression (GPR) model for predicting the bond strength of FRP-to-concrete is proposed in this study. Published single-lap shear test specimens are used to predict the bond strength of externally bonded FRP systems adhered to concrete prisms. A database of 150 experimental results collected from published works is used for the training and testing phases of the proposed GPR model, containing 6 input parameters (width of concrete prism, concrete compressive strength, FRP thickness, FRP width, FRP length, and FRP modulus of elasticity). The output parameter of the prediction problem is bond strength. Three statistical indicators, namely the coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE) are used to evaluate the performance of the proposed GPR model over 500 simulations. The results of this study indicate that the GPR provides an efcient alternative method for predicting the bond strength of FRP-to-concrete when compared to experimental results.