关键词: 分形维数, 颗粒流, 平行黏结模型, 参数标定, 神经网络

Abstract: The trial-and-error approach for calibrating the meso-parameters of the parallel bond model is cumbersome and time-consuming, and it fails to quantitatively assess the correlation between cracks from numerical simulations and laboratory tests. The meso-parameter ranges of the parallel bond model over the past decade were summarized, and the crack fractal dimensions post-failure in both numerical simulations and laboratory tests were calculated using the box-counting method. Based on this, a neural network model was developed using four macroscopic parameters, including elastic modulus, Poisson’s ratio, peak strength and crack fractal dimension, as inputs, and six mesoscopic parameters such as bond effective modulus, ratio of normal stiffness to shear stiffness, cohesion, friction angle, tensile strength and friction coefficient, as outputs. The calibration effects of the parallel bond model with and without considering crack fractal dimension were compared. The research indicates that: 1) The developed neural network model exhibits a good convergence rate, prediction accuracy, and generalization ability, with an error of approximately 3.34% between the test set output and the expected values. 2) Incorporating crack fractal dimension into the neural network model results in errors of less than 3.00% for macroscopic parameters like elastic modulus, peak strength, and Poisson’s ratio between numerical and laboratory tests, outperforming the calibration results without considering crack fractal dimension. 3) This approach quantitatively ensures the consistency of crack irregularity between numerical and laboratory tests. The calibrated results can partially correct the existing neural network model’s calibration, offering new insights for enhancing the calibration of the meso-parameters of the parallel bond model.

Key words: fractal dimension, particle flow code, parallel bond model, parameter calibration, neural network