Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield

Abstract

Elastic modulus (E) is a key parameter in predicting the ability of a material to withstand pressure and plays a critical role in the design of rock engineering projects. E has broad applications in the stability of structures in mining, petroleum, geotechnical engineering, etc. E can be determined directly by conducting laboratory tests, which are time consuming, and require high-quality core samples and costly modern instruments. Thus, devising an indirect estimation method of E has promising prospects. In this study, six novel machine learning (ML)-based intelligent regression models, namely, light gradient boosting machine (LightGBM), support vector machine (SVM), Catboost, gradient boosted tree regressor (GBRT), random forest (RF), and extreme gradient boosting (XGBoost), were developed to predict the impacts of four input parameters, namely, wet density (ρwet) in gm/cm3, moisture (%), dry density (ρd) in gm/cm3, and Brazilian tensile strength (BTS) in MPa on output E (GPa). The associated strengths of every input and output were systematically measured employing a series of fundamental statistical investigation tools to categorize the most dominant and important input parameters. The actual dataset of E was split as 70% for the training and 30% for the testing for each model. In order to enhance the performance of each developed model, an iterative 5-fold cross-validation method was used. Therefore, based on the results of the study, the XGBoost model outperformed the other developed models with a higher accuracy, coefficient of determination (R2 = 0.999), mean absolute error (MAE = 0.0015), mean square error (MSE = 0.0008), root mean square error (RMSE = 0.0089), and a20-index = 0.996 of the test data. In addition, GBRT and RF have also shown high accuracy in predicting E with R2 values of 0.988 and 0.989, respectively, but they can be used conditionally. Based on sensitivity analysis, all parameters were positively correlated, while BTS was the most influential parameter in predicting E. Using an ML-based intelligent approach, this study was able to provide alternative elucidations for predicting E with appropriate accuracy and run time at Thar coalfield, Pakistan.

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

“…CatBoost is a gradient-boosting tree construction method 36 , which makes use of both symmetric and non-symmetric construction methods. In CatBoost, a tree is learned at each iteration with the aim of reducing the error made by previous trees.…”

Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples

“…Shahani et al (2021) utilized a novel XGBoost algorithm. The applied model, XGBoost achieved a high level of accuracy in predicting E. Furthermore, Shahani et al (2022a) developed six ML models such as "LightGBM, SVM, Catboost, GBRT, RF, and XGBoost" to estimate E of the Thar Coalfield. Thus, the XGBoost model showed better results than the other models.…”

“…Key properties assessed included wet density (WD), moisture, dry density (DD), Brazilian tensile strength (BTS), shore hardness (SH), elastic modulus (E), and uniaxial compressive strength (UCS). Previously (Shahani et al, 2022a), we used 106 The UCS test carried out following ISRM standards, involved the use of a uniaxial testing machine (UTM) on standardized core samples with NX dimensions, featuring a diameter of 54 mm, and applied a loading rate of 0.5 MPa/s. This test was performed to ascertain the UCS and E of the rock samples.…”

Evaluation of Uniaxial Compressive Strength of Basalts using Machine Learning Methods and Comparison of Their Performances

“…Shahani vd. [21] Pakistan Thar kömür alanında yer alan sedimanter kayaların UCS'lerini tahmin etmeye yönelik dört gradyan artırma (gradient boosting) makine öğrenimi yöntemi uygulamışlardır. Çalışmada XGBoost algoritmasının diğer yöntemlerle karşılaştırıldığında en doğru sonucu verdiği vurgulanmıştır.…”