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

Muhammad Nasir Amin; Mudassir Iqbal; Mohammed Ashfaq; Babatunde Abiodun Salami; Kaffayatullah Khan; Muhammad Iftikhar Faraz; Anas Abdulalim Alabdullah; Fazal E Jalal

doi:10.3390/ma15124330

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

Materials (Basel). 2022 Jun 18;15(12):4330. doi: 10.3390/ma15124330.

Authors

Muhammad Nasir Amin¹, Mudassir Iqbal^{2

3}, Mohammed Ashfaq⁴, Babatunde Abiodun Salami⁵, Kaffayatullah Khan¹, Muhammad Iftikhar Faraz⁶, Anas Abdulalim Alabdullah¹, Fazal E Jalal²

Affiliations

¹ Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
² Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
³ Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan.
⁴ Department of Civil Engineering, National Institute of Technology Warangal, Warangal 506004, India.
⁵ Interdisciplinary Research Center for Construction and Building Materials, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
⁶ Department of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

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.

Keywords: ANN model; chemical stabilization; coal gangue; gypsum; lime dosage; random forest model.

Grants and funding

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. GRANT537]/King Faisal University