Dynamic characteristics of coal specimens with varying static preloading levels under low-frequency disturbance load

The mechanical properties of residual coal pillars under the influence of upward mining disturbances significantly affect the safety of residual mining activities on working faces.This study conducted low-frequency disturbance dynamic uniaxial compression tests on coal specimens using a self-developed dynamic-static load coupling electro-hydraulic servo system,and studied the strength evolutions,surface deformations,acoustic emission(AE)characteristic parameters,and the failure modes of coal specimens with different static preloading levels were studied.The disturbance damage is positively correlated with the coal specimen static preload level.Specifically,the cumulative AE count rates of the initial accelerated damage stage for the coal specimens with static preloading level of 60%and 70%of the uniaxial compressive strength(UCS)were 2.66 and 3.19 times that of the 50%UCS specimens,respectively.Macroscopically,this behaviour manifested as a decrease in the compressive strength,and the mean strengths of the disturbance-damaged coal specimens with 60%and 70%of UCS static preloading decreased by 8.53%and 9.32%,respectively,compared to those of the specimens under pure static loading.The crack sources,such as the primary fissures,strongly control the dynamic response of the coal specimen.The difference between the dynamic responses of the coal specimens and that of dense rocks is significant.

Machine learning-based constitutive models for cement-grouted coal specimens under shearing

Cement-based grouting has been widely used in mining engineering;its constitutive law has not been comprehensively studied.In this study,a novel constitutive law of cement-grouted coal specimens(CGCS)was developed using hybrid machine learning(ML)algorithms.Shear tests were performed on CGCS for the analysis of stress-strain curves and the preparation of the dataset.To maintain the interpretation of the trained ML models,regression tree(RT)was used as the main technique.The effect of maximum RT depth(Maxdepth)on its performance was studied,and the hyperparameters of RT were tuned using the genetic algorithm(GA).The RT performance was also compared with ensemble learning techniques.The optimum correlation coefficient on the training set was determined as 0.835,0.946,0.981,and 0.985 for RT models with Maxdepth=3,5,7,and 9,respectively.The overall correlation coefficient was over 0.9 when the Maxdepth≥5,indicating that the constitutive law of CGCS can be well described.However,the failure type of CGCS could not be captured using the trained RT models.Random forest was found to be the optimum algorithm for the constitutive modeling of CGCS,while RT with the Maxdepth=3 performed the worst.

Control of combustion area using electrical resistivity method for underground coal gasification

Underground coal gasification （UCG） is one of the clean technologies to collect heat energy and gases （hydrogen, methane, etc.） in an underground coal seam. It is necessary to further developing environ- mentally friendly UCG system construction. One of the most important UCG＇s problems is underground control of combustion area for efficient gas production, estimation of subsidence and gas leakage to the surface. For this objective, laboratory experiments were conducted according to the UCG model to iden- ti[y the process of combustion cavity development by monitoring the electrical resistivity activity on the coal samples to setup fundamental data for the technology engineering to evaluate combustion area. While burning coal specimens, that had been sampled from various coal deposits, electrical resistivity was monitored. Symmetric four electrodes system （ABMN） of direct and low-frequency current electric resistance method was used for laboratory resistivity measurement of rock samples. Made research and the results suggest that front-end of electro conductivity activity during heating and combusting of coal specimen depended on heating temperature. Combusting coal electro conductivity has compli- cated multistage type of change. Electrical resistivity method is expected to be a useful geophysical tool to for evaluation of combustion volume and its migration in the coal seam.