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Open AccessArticle
Application of a Multi-Algorithm-Optimized CatBoost Model in Predicting the Strength of Multi-Source Solid Waste Backfilling Materials
by
Jianhui Qiu
Jianhui Qiu ,
Jielin Li
Jielin Li *
,
Xin Xiong
Xin Xiong
and
Keping Zhou
Keping Zhou
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2025, 9(8), 203; https://doi.org/10.3390/bdcc9080203 (registering DOI)
Submission received: 29 June 2025
/
Revised: 26 July 2025
/
Accepted: 30 July 2025
/
Published: 7 August 2025
Abstract
Backfilling materials are commonly employed materials in mines for filling mining waste, and the strength of the consolidated backfill formed by the binding material directly influences the stability of the surrounding rock and production safety in mines. The traditional approach to obtaining the strength of the backfill demands a considerable amount of manpower and time. The rapid and precise acquisition and optimization of backfill strength parameters hold utmost significance for mining safety. In this research, the authors carried out a backfill strength experiment with five experimental parameters, namely concentration, cement–sand ratio, waste rock–tailing ratio, curing time, and curing temperature, using an orthogonal design. They collected 174 sets of backfill strength parameters and employed six population optimization algorithms, including the Artificial Ecosystem-based Optimization (AEO) algorithm, Aquila Optimization (AO) algorithm, Germinal Center Optimization (GCO), Sand Cat Swarm Optimization (SCSO), Sparrow Search Algorithm (SSA), and Walrus Optimization Algorithm (WaOA), in combination with the CatBoost algorithm to conduct a prediction study of backfill strength. The study also utilized the Shapley Additive explanatory (SHAP) method to analyze the influence of different parameters on the prediction of backfill strength. The results demonstrate that when the population size was 60, the AEO-CatBoost algorithm model exhibited a favorable fitting effect (R2 = 0.947, VAF = 93.614), and the prediction error was minimal (RMSE = 0.606, MAE = 0.465), enabling the accurate and rapid prediction of the strength parameters of the backfill under different ratios and curing conditions. Additionally, an increase in curing temperature and curing time enhanced the strength of the backfill, and the influence of the waste rock–tailing ratio on the strength of the backfill was negative at a curing temperature of 50 °C, which is attributed to the change in the pore structure at the microscopic level leading to macroscopic mechanical alterations. When the curing conditions are adequate and the parameter ratios are reasonable, the smaller the porosity rate in the backfill, the greater the backfill strength will be. This study offers a reliable and accurate method for the rapid acquisition of backfill strength and provides new technical support for the development of filling mining technology.
Share and Cite
MDPI and ACS Style
Qiu, J.; Li, J.; Xiong, X.; Zhou, K.
Application of a Multi-Algorithm-Optimized CatBoost Model in Predicting the Strength of Multi-Source Solid Waste Backfilling Materials. Big Data Cogn. Comput. 2025, 9, 203.
https://doi.org/10.3390/bdcc9080203
AMA Style
Qiu J, Li J, Xiong X, Zhou K.
Application of a Multi-Algorithm-Optimized CatBoost Model in Predicting the Strength of Multi-Source Solid Waste Backfilling Materials. Big Data and Cognitive Computing. 2025; 9(8):203.
https://doi.org/10.3390/bdcc9080203
Chicago/Turabian Style
Qiu, Jianhui, Jielin Li, Xin Xiong, and Keping Zhou.
2025. "Application of a Multi-Algorithm-Optimized CatBoost Model in Predicting the Strength of Multi-Source Solid Waste Backfilling Materials" Big Data and Cognitive Computing 9, no. 8: 203.
https://doi.org/10.3390/bdcc9080203
APA Style
Qiu, J., Li, J., Xiong, X., & Zhou, K.
(2025). Application of a Multi-Algorithm-Optimized CatBoost Model in Predicting the Strength of Multi-Source Solid Waste Backfilling Materials. Big Data and Cognitive Computing, 9(8), 203.
https://doi.org/10.3390/bdcc9080203
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