AUTHOR=Feng Feisheng , Li Lirong , Zhang Jiqiang , Yang Zhen , Chi Xiaolou 

TITLE=Strength Prediction of Coal-Based Solid Waste Filler Based on BP Neural Network

JOURNAL=Frontiers in Materials

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2021.767031

DOI=10.3389/fmats.2021.767031

ISSN=2296-8016

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 coal-based solid waste filler(CBSWF) are tested through orthogonal test, and the strength of coal-based solid waste filler(CBSWF) at different curing ages is predicted by using 4-11-3 three-layer BP neural network structure. The results show that the correlation coefficient r of strength prediction of coal-based solid waste filler(CBSWF) is 0.99987, which can accurately predict the strength of coal-based solid waste filler(CBSWF), Orthogonal test combined with BP neural network can reduce the number of tests without losing generality, make full use of the advantages of adaptive nonlinear optimization of BP neural network, improve the operation efficiency of the model, fast prediction speed and high accuracy.