Frontiers in Materials (Nov 2021)
Strength Prediction of Coal-Based Solid Waste Filler Based on BP Neural Network
Abstract
The compressive strength of filling body is an important index to characterize the filling and mining effect of coal mine. In order to accurately predict the strength of coal-based solid waste filler (CBSWF) to guide the safe, efficient, and green mining of coal mine, coal gangue is used as coarse material; fly ash, desulfurization gypsum, gasification slag, and furnace bottom slag are used as fine materials; and cement is used as gelling agent. The compressive strength and bleeding rate of CBSWF are tested through orthogonal test, and the strength of CBSWF at different curing ages is predicted by using a 4-11-3 three-layer BP neural network structure. The results show that the correlation coefficient r of strength prediction of CBSWF is 0.99987, which can accurately predict the strength of CBSWF. Orthogonal test combined with the BP neural network can reduce the number of tests without losing generality, make full use of the advantages of adaptive nonlinear optimization of the BP neural network, and improve the operation efficiency of the model, fast prediction speed, and high accuracy.
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