Abstract
The uniaxial compressive strength (UCS) of rock is one of the essential data in engineering planning and design. Correctly testing UCS of rock to ensure its accuracy and authenticity is a prerequisite for assuring the design of any rock engineering project. UCS of rock has a broad range of applications in mining, geotechnical, petroleum, geomechanics, and other fields of engineering. The application of the gradient boosting machine learning algorithms has been rarely used, especially for UCS prediction, and has performed well, based on the relevant literature of the study. In this study, four gradient boosting machine learning algorithms, namely, gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were developed to predict the UCS in MPa of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan, using four input variables such as wet density (ρw) in g/cm3; moisture in %; dry density (ρd) in g/cm3; and Brazilian tensile strength (BTS) in MPa. Then, 106-point dataset was allocated identically for each algorithm into 70% for the training phase and 30% for the testing phase. According to the results, the XGBoost algorithm outperformed the GBR, Catboost, and LightGBM with coefficient of correlation (R2) = 0.99, mean absolute error (MAE) = 0.00062, mean square error (MSE) = 0.0000006, and root mean square error (RMSE) = 0.00079 in the training phase and R2 = 0.99, MAE = 0.00054, MSE = 0.0000005, and RMSE = 0.00069 in the testing phase. The sensitivity analysis showed that BTS and ρw are positively correlated, and the moisture and ρd are negatively correlated with the UCS. Therefore, in this study, the XGBoost algorithm was shown to be the most accurate algorithm among all the investigated four algorithms for UCS prediction of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan.
Highlights
uniaxial compressive strength (UCS) of rock has a wide range of applications in mining, geotechnical, petroleum, geomechanics, and other fields of engineering. e study of rock mechanical properties is the basis for innovative advances associated with energy supply. e significance of rock mechanics is acknowledged in the advancement of natural assets, for example, the protection of energy sources, as well as the Advances in Civil Engineering protection of the surrounding rock environment
We proposed an innovative adaptation of four gradient boosting machine learning algorithms such as gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) that improves the handling of concept drift. e database employed in this work has been collected from soft sedimentary rocks of the Block-IX at ar Coalfield, Pakistan
E existence of the boosting mechanism is from the response of Schapire to Keran’s inquiry [49, 50] (Kearns): Is a combination of a weak learner an alternative to distinguish strong learner? Weak Learner is defined as the algorithm that is working well as compared to random approximation; a strong base framework is a more authentic classification or regression algorithm that is inconsistent that is efficiently corresponding with the problem. e response to such an inquiry is very significant. e evaluation of a weak framework is often unchallenging as compared with a strong framework
Summary
Some researchers have employed indirect testing methods, i.e., multiple linear regression (MLR) [17, 22, 23], artificial neural network (ANN) [23, 24], adaptive neurofuzzy inference system (ANFIS) [25], and other machine learning algorithms to estimate the accuracy and reliability of rock data [26,27,28], rather than using direct tests recommended by international standards, which are considered time-consuming, expensive, and unreliable [29, 30].
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