@article{Li2022, 
author = {Li Li and Lei Zhou and Ziyan Qian},
title = {Prediction of Three-point Bending Critical Load Using Artificial Neural Network Based on Simulation Data},
year = {2022},
journal = {Chinese Journal of Underground Space and Engineering},
volume = {18},
number = {1},
pages = {102-111},
keywords = {three-point bending, artificial neural network, simulation data driven, prediction parameters},
url = {https://www.sciopen.com/article/10.20174/j.juse.2022.01.012},
doi = {10.20174/j.juse.2022.01.012},
abstract = {Tensile strength of jointed rock mass can be obtained through three-point bending tests of notched specimens. Through numerical simulation, it is posted that critical load of notched rock specimen three-point bending test is subject to artificial parameters including notch angle, and natural parameters including Young’s module, tension strength, which show positive correlations, while there is a negative correlation between critical load and notch length. Limited to numerous factors and large amount of experiment work, it is difficult to build multi-factor mathematical fitting model which can be universally used. This paper set up and trained an artificial neural network model to predict notched rock three-point bending critical load utilizing numerical results of numerical simulation. Simulation data driven artificial neural network model showed reliable prediction performance on known data, and fine ability of generalization and extrapolation on unseen data.}
}