TY - JOUR
T1 - Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue
T2 - ANN and Random Forest Tree Approach
AU - Amin, Muhammad Nasir
AU - Iqbal, Mudassir
AU - Ashfaq, Mohammed
AU - Salami, Babatunde Abiodun
AU - Khan, Kaffayatullah
AU - Faraz, Muhammad Iftikhar
AU - Alabdullah, Anas Abdulalim
AU - Jalal, Fazal E.
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/6/18
Y1 - 2022/6/18
N2 - 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.
AB - 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.
KW - ANN model
KW - chemical stabilization
KW - coal gangue
KW - gypsum
KW - lime dosage
KW - random forest model
UR - http://www.scopus.com/inward/record.url?scp=85132719973&partnerID=8YFLogxK
U2 - 10.3390/ma15124330
DO - 10.3390/ma15124330
M3 - Article
AN - SCOPUS:85132719973
SN - 1996-1944
VL - 15
JO - Materials
JF - Materials
IS - 12
M1 - 4330
ER -