Strength distribution of cemented waste rock backfill: a similarity simulation experiment

Abstract

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Backfill of cemented waste rock into underground mined-out areas is an effective way to eliminate solid wastes and potential hazards in mines. To understand the backfill strength distribution law throughout the stope, similarity simulation experiments were conducted for direct-irrigating cemented waste rock backfill, and OpenCV and neural network were employed to analyze particle segregation and the spatial distribution of backfill strength. Results show that distinct gravitational segregation leads to an uneven and heterogeneous distribution of natural graded waste rocks in a similar model. Backfill strength near sidewalls and bottom of the model surpasses that of other areas. In the vertical direction, the average backfill strength increases with depth, ranging from 1.15 MPa at the topmost layer to 1.91 MPa at the bottommost layer. Horizontally, the average backfill strength near model boundaries is consistently higher than that at the model center, irrespective of the layer depth and orientation. Neural network prediction on spatial backfill strength proves reliable, exhibiting an average relative error of 4.12%, compared to the traditional surface fitting with a 10.20% error. Verification tests affirm the capability of the neural network model to accurately predict the anisotropic and nonlinear distribution of backfill strength in a large stope.

Keywords

• backfill strength
• similarity simulation experiment
• neural network
• OpenCV
• spatial distribution law