Summary
This investigation studied the coalcrete, a new supporting material produced by jet grouting (JG) for supporting surrounding coal seams. For support design, the unconfined compressive strength (UCS) of the coalcrete is an essential parameter to evaluate the jet grouting effect in coal mines. In this study, an intelligent technique was proposed for predicting the UCS of the coalcrete by combining back propagation neural network (BPNN) and beetle antennae search (BAS). The architecture of BPNN was first tuned by BAS, and then, the optimized BPNN‐BAS model was subsequently used for nonlinear relationship modeling. Several crucial influencing variables including water‐cement ratio, coal‐grout ratio, and curing time were selected as the inputs. By combining these variables, 360 coalcrete samples were prepared in a controlled laboratory environment and tested for establishing the dataset. The results demonstrate that BAS can tune the BPNN architecture more efficiently compared with random selection. Moreover, in comparison with multiple regression (MLR) and logistic regression (LR), and support vector machine (SVM), the optimized BPNN‐BAS model is more reliable and accurate for predicting coalcrete strength. Sensitivity analysis (SA) was used to obtain the variable importance, and the results demonstrate that curing time affects the UCS of the coalcrete most strongly, followed by water‐cement ratio and coal‐grout ratio. The success of this study provides an accurate and brief approach to coalcrete strength prediction.
Roadway stability in extremely soft coal mass is a typical challenge in deep underground mines, as the most widely adopted support structure such as rock bolting, compound support system normally fails. This paper proposed a novel prereinforcement method for coal roadway, that is, jet grouting (JG) technique, to improve the quality of surrounding soft coal mass and form a stable support structure namely jet grouted coalcrete. The field and laboratory tests were conducted for evaluating the applicability of JG and the mechanical parameters of coalcrete. A numerical model was verified by comparing the calculated results with field measurements. According to the confirmed model, three typical JG support schemes were performed to assess the roadway stability after JG. The results showed that the proposed JG schemes can reduce the deformation and failure zone of the roadway and optimize the stress states around the roadway. The mechanism related to these advantages was investigated.This study provides a promising idea for supporting soft coal roadway in deep underground coal mines, which can promote the JG application in the field.
K E Y W O R D Scoalcrete, jet grouting technique, numerical modeling, roadway stability, soft coal mass
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