Creep characteristics of coal and rock investigated by nanoindentation

In coal mining industry,with the depth growing of coal mines,the creep behaviours of coal and rock can extensively affect the mining safety,coalbed methane recovery and geo-sequestration.To acquire a better insight into their creep characteristics,an efficient and robust researching technique,nanoindentation,was applied to investigate the creep performances of coal and rock samples obtained from two coal mines in the east of China.Creep characteristics were reflected by evaluating the curves of creep depth versus creep time of nanoindentation tests during the load-holding period at the peak load of 30 mN.These curves can be divided into two stages:transient stage and steady stage;and the time of load-holding period of 5 s,which is the dividing point between two stages,can efficiently avoid the influence of creep displacement on the unloading curves.The exponential function can perfectly fit creep curves and Kelvin model can be used to calculate the rheological parameters of coal and rock samples.Calculated results yield values for the creep modulus and viscosity terms of coal and rock.This study also settled a particular emphasis on the selection of the positions of indentations to obtain the rheological properties of mineralogical constituents in heterogonous coal and rock samples and their elastic aftereffect.

Development of ensemble learning models to evaluate the strength of coal-grout materials

In the loose and fractured coal seam with particularly low uniaxial compressive strength(UCS),driving a roadway is extremely difficult as roof falling and wall spalling occur frequently.To address this issue,the jet grouting(JG)technique(high-pressure grout mixed with coal particles)was first introduced in this study to improve the self-supporting ability of coal mass.To evaluate the strength of the jet-grouted coal-grout composite(JG composite),the UCS evolution patterns were analyzed by preparing 405 specimens combining the influential variables of grout types,curing time,and coal to grout(C/G)ratio.Furthermore,the relationships between UCS and these influencing variables were modeled using ensemble learning methods i.e.gradient boosted regression tree(GBRT)and random forest(RF)with their hyperparameters tuned by the particle swarm optimization(PSO).The results showed that the chemical grout composite has higher short-term strength,while the cement grout composite can achieve more stable strength in the long term.The PSO-GBRT and PSO-RF models can both achieve high prediction accuracy.Also,the variable importance analysis demonstrated that the grout type and curing time should be considered carefully.This study provides a robust intelligent model for predicting UCS of JG composites,which boosts JG design in the field.

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.