A Hybrid Artificial Intelligence Model for Predicting the Strength of Foam-Cemented Paste Backfill

Abstract

Foam-cemented paste backfill (FCPB) has become a trend to solve the problem of roof-contacted filling. In order to solve the time-consuming and labor-intensive disadvantages of laboratory uniaxial compressive strength (UCS) tests, a hybrid artificial intelligence model which combines random forest (RF) algorithm and grid search optimizer (GSO) was proposed for FCPB strength prediction. Moreover, the effects of foaming agent on cement hydration and pore structure characteristics were studied. The results showed that GSO can effectively tune the hyper-parameters of the proposed GSO-RF model and the developed model is an efficient and accurate tool to predict the UCS for FCPB. Though the foaming agent will not change the influence trend of cement-tailings ratio, solid content and curing time, the influence degree will be weakened by the foaming agent. The ranking of the relative importance of influencing variables is: cement-tailings ratio > curing time > foaming agent dosage > solid content. In addition, the foaming agent has no significant effect on the hydration of cement. The foaming agent mainly changes the strength by changing the pore structure characteristics (especially the large pore volume). This research can provided some guidance for developing UCS prediction model for FCPB.

Keywords

• Foam-cemented paste backfill
• uniaxial compressive strength
• random forest
• grid search optimizer
• variable importance