Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach

Abstract

Coal mining waste in the form of coal gangue (CG) was established recently as a potential fill material in earthworks. To ascertain this potential, this study forecasts the strength and California Bearing Ratio (CBR) characteristics of chemically stabilized CG by deploying two widely used artificial intelligence approaches, i.e., artificial neural network (ANN) and random forest (RF) regression. In this research work, varied dosage levels of lime (2, 4, and 6%) and gypsum (0.5, 1, and 1.5%) were employed for determining the unconfined compression strength (UCS) and CBR of stabilized CG mixes. An experimental study comprising 384 datasets was conducted and the resulting database was used to develop the ANN and RF regression models. Lime content, gypsum dosage, and 28 d curing period were considered as three input attributes in obtaining three outputs (i.e., UCS, unsoaked CBR, and soaked CBR). While modelling with the ANN technique, different algorithms, hidden layers, and the number of neurons were studied while selecting the optimum model. In the case of RF regression modelling, optimal grid comprising maximal depth of tree, number of trees, confidence, random splits, enabled parallel execution, and guess subset ratio were investigated, alongside the variable number of folds, to obtain the best model. The optimum models obtained using the ANN approach manifested relatively better performance in terms of correlation coefficient values, equaling 0.993, 0.995, and 0.997 for UCS, unsoaked CBR and soaked CBR, respectively. Additionally, the MAE values were observed as 45.98 kPa, 1.41%, and 1.18% for UCS, unsoaked CBR, and soaked CBR, respectively. The models were also validated using 2-stage validation processes. In the first stage of validation of the model (using unseen 30% of the data), it was revealed that reliable performance of the models was attained, whereas in the second stage (parametric analysis), results were achieved which are corroborated with those in existing literature.

Study on the Reactivity Activation of Coal Gangue for Efficient Utilization

“…The activation process parameters of coal gangue for the optimal scheme obtained from the GA-BP model were 76 min, 749 °C, and 54 min for the grinding duration, calcination temperature, and calcination duration, respectively. The specific surface area of the coal gangue powder after grinding is 1287.55 m 2 /kg, and the predicted and measured 28-day compressive strengths were 44.29 MPa and 46.72 MPa, respectively, with an error of 5.20%, weight was w [ 1 ] = [1.5684, 2.2593, 0.4351, 1.0764, −2.8181, 0.4598, −2.6355, −0.2698, −2.0496, 1.7247, −2.3366, 0.5831], and w [ 2 ] = [−2.4970, 2.0319, −1.7904, −1.3782], biases were b [ 1 ] = [1.2971, 2.7980, −1.0025, −2.8049] and b [ 2 ] = [0.1836], respectively.…”

“…The above rules were consistent with the results of particle size analysis. [2] = [−2.4970, 2.0319, −1.7904, −1.3782], biases were b [1] = [1.2971, 2.7980, −1.0025, −2.8049] and b [2] = [0.1836], respectively. Keeping the initial feeding mass at 3 kg, the changes in the passing mass of 0.212 mm square hole sieves and the specific surface area of the coal gangue powders with grinding duration are shown in Figure 6.…”

“…In recent years, coal consumption has accounted for over 50% of the one-time energy consumption in China [ 1 ]. Coal gangue is the main solid waste generated during the coal mining and washing process, which accounts for 15% to 20% of the coal production [ 2 ]. The cumulative emission of coal gangue has exceeded 7 billion tons in China [ 3 ], and it is still growing at a rapid rate of 0.5 billion tons per year [ 4 ].…”

Study on Aeolian Sand-Box Backfill Geomechanical Characteristics and Overlying Strata Control Effects

“…The stability of backfill material is crucial for effective subsidence control, with scholars providing key insights:Amin, Muhammad N [15], in mechanical experiments on coal gangue waste, analyzed the impact of lime and gypsum doses on the unconfined compressive strength of the coal gangue mixture. Gu Wei [16], combining lab experiments and numerical simulations, explored overlying strata movement patterns in gangue backfill The stability of backfill material is crucial for effective subsidence control, with scholars providing key insights:Amin, Muhammad N [15], in mechanical experiments on coal gangue waste, analyzed the impact of lime and gypsum doses on the unconfined compressive strength of the coal gangue mixture. Gu Wei [16], combining lab experiments and numerical simulations, explored overlying strata movement patterns in gangue backfill mining.…”

Multi-Scale Toolbox: An Automated ArcGIS Tool for Evaluating Pupil–Teacher Ratios in U.S. Public School Districts

“…This is due to two existing education research of the "mechanism based" paradigm and the "effect base" paradigm that Maroulis et al (2020) mentioned [21]. In addition, we utilized random forest tree modelling of machine learning to expose the association between pupil-teacher ratios and 23 educational factors, thanks to machine learning algorithms having high data processing speeds and accuracies [22][23][24][25].…”