Abstract
Coal-grout composites were fabricated in this study using the jet grouting (JG) technique to enhance coal mass in underground conditions. To evaluate the mechanical properties of the created coal-grout composite, its unconfined compressive strength (UCS) needed to be tested. A mathematical model is required to elucidate the unknown nonlinear relationship between the UCS and the influencing variables. In this study, six computational intelligence techniques using machine learning (ML) algorithms were used to develop the mathematical models, which includes back-propagation neural network (BPNN), random forest (RF), decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR). In addition, the hyper-parameters in these typical algorithms (e.g., the hidden layers in BPNN, the gamma in SVM, and the number of neighbor samples in KNN) were tuned by the recently developed beetle antennae search algorithm (BAS). To prepare the dataset for these ML models, three types of cementitious grout and three types of chemical grout were mixed with coal powders extracted from the Guobei coalmine, Anhui Province, China to create coal-grout composites. In total, 405 coal-grout specimens in total were extracted and tested. Several variables such as grout types, coal-grout ratio, and curing time were chosen as input parameters, while UCS was the output of these models. The results show that coal-chemical grout composites had higher strength in the short-term, while the coal-cementitious grout composites could achieve stable and high strength in the long term. BPNN, DT, and SVM outperform the others in terms of predicting the UCS of the coal-grout composites. The outstanding performance of the optimum ML algorithms for strength prediction facilitates JG parameter design in practice and could be the benchmark for the wider application of ML methods in JG engineering for coal improvement.
Highlights
Jet grouting (JG) has been a widely applied approach for the stabilization of loose materials such as soil, fragmented rock, coal, etc. [1]
It can be seen that the 7-day unconfined compressive strength (UCS) of the coal-MP 364 composite is higher than other chemical grout composites with the same coal-grout ratio
As for the cementitious grout composites with the same coal-grout ratio and curing time, the UCS of superfine cement (SF-C) composites was the highest followed by coal-P.O 42.5, while coal-P.O 32.5 had the lowest UCS
Summary
Jet grouting (JG) has been a widely applied approach for the stabilization of loose materials such as soil, fragmented rock, coal, etc. [1]. Jet grouting (JG) has been a widely applied approach for the stabilization of loose materials such as soil, fragmented rock, coal, etc. A stabilizing fluid is injected into soft materials under high velocity and pressure [2]. The injecting equipment is composed of a JG string with a nozzle at the end, which can inject the fluid into the soft materials via a rotary motion as the string is raised and rotated slowly [3,4]. Cementitious grout or chemical grout can be utilized as blinders to consolidate the loose materials in order to improve their mechanical properties, such as unconfined compressive strength (UCS).
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