@Article{jrm.2022.021845,
AUTHOR = {Bo Zhang, Keqing Li, Siqi Zhang, Yafei Hu, Bin Han},
TITLE = {A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM},
JOURNAL = {Journal of Renewable Materials},
VOLUME = {10},
YEAR = {2022},
NUMBER = {12},
PAGES = {3539--3558},
URL = {http://www.techscience.com/jrm/v10n12/48804},
ISSN = {2164-6341},
ABSTRACT = {Cemented paste backfill (CPB) is a sustainable mining technology that is widely used in mines and helps to improve
the mine environment. To investigate the relationship between aggregate grading and different affecting factors and
the uniaxial compressive strength (UCS) of the cemented paste backfill (CPB), Talbol gradation theory and neural
networks is used to evaluate aggregate gradation to determine the optimum aggregate ratio. The mixed aggregate ratio
with the least amount of cement (waste stone content river sand content = 7:3) is obtained by using Talbol grading
theory and pile compactness function and combined with experiments. In addition, the response surface method is
used to design strength-specific ratio experiments. The UCS prediction model which uses the LSTM and considers
the aggregates gradation have high accuracy. The root mean square error (RMSE) of the prediction results is 0.0914,
the coefficient of determination (R<sup>2</sup>
) is 0.9973 and the variance account for (VAF) is 99.73. Compared with back
propagation neural network (BP-ANN), extreme learning machine (ELM) and radial basis function neural network
(RBF-ANN), LSTM can effectively characterize the nonlinear relationship between UCS and individual affecting
factors and predict UCS with high accuracy. The sensitivity analysis of different affecting factors on UCS shows that
all 4 factors have significant effect on UCS and sensitivity is in the following ranking: cement content (0.9264) > slurry
concentration (0.9179) > aggregate gradation (waste rock content) (0.9031) > curing time (0.9031).},
DOI = {10.32604/jrm.2022.021845}
}