Abstract
To accurately and intuitively study the influence of microscopic parameters and mechanical responses of the consolidation process of cemented paste backfill (CPB), a method is proposed for characterizing its geometric and morphological characteristics and its mechanical response. A set of microstructure parameter software is developed for analyzing the CPB consolidation process, which quantitatively analyzes the mechanical response of CPBs at a microscopic scale. Based on the fuzzy clustering method, CPB microscopic pore images are extracted via digital image processing technology. Microscopic CPB pores are extracted from images via cluster analysis, binarization, and denoising techniques. Then, images are evaluated for porosity, number of pores, average pore width, fractal dimension, weighted probability entropy, and 11 more indicators to quantitatively analyze pores. Thus, the proposed method forms nonlinear relationships between microstructure parameters and mechanical responses based on a deep learning TensorFlow framework under different curing times. Results show that the multiparameter predictive mechanical response at the microscopic scale has a good effect, and the predicted average error is 9.51%. The accuracy of the proposed method is higher than that of the traditional method. Therefore, the proposed method provides a new method to quantitatively analyze the mechanical response strength prediction at a microscale.
Highlights
Owing to the gradual depletion of mineral resources in the shallow parts of the Earth, deep mineral resource mining has become commonplace and increasingly important. e strength of the backfill material is critical
By comparing and analyzing cemented paste backfill (CPB) under different curing conditions, they learned that strengths differ according to the law of increasing backfill strength [1]
Of Central South University, optimized the CPB ratio by using a neural network, which took the concentration of slurry and the amount of each component as input. e respective slump measures of compression strength at 7 and 28 days were regarded as output factors, and the matching experimental data for training and testing samples were established using a back propagation neural network prediction model [3]
Summary
Owing to the gradual depletion of mineral resources in the shallow parts of the Earth, deep mineral resource mining has become commonplace and increasingly important. e strength of the backfill material is critical. Is paper summarizes studies of CPBs at di erent curing times and characterizes the geometrical characteristics and morphological structures of the pore network based on measuring indexes, such as number of pores, total area of pores, maximum area of pores, average area of pores, average length of long axis, porosity, coe cient of uniformity, sorting coe cient, curvature coe cient, fractal dimension, and weighted probability entropy, by conducting indoor microscopic tests and extracting the microscopic pore images using an image processing technique. Is paper analyzes the e ects of the CPB microscopic parameters on the mechanical response strength, using a slurry concentration of 72% and a cement-sand ratio of 1 : 4 at di erent curing times. A visual quanti cation method is o ered for analyzing the relationship between the pore structure and the mechanical response of the CPB during solidi cation on a microscopic scale
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