Estimation of rock strength parameters from petrological contents using tree-based machine learning techniques

Authors:
Open Access
Publisher website
Google Scholar
Abstract
The demand for construction materials in Pakistan has experienced a significant increase, particularly due to the China-Pakistan Economic Corridor (CPEC) project, which necessitates substantial amounts of resilient resources for infrastructure development. Parameters of rock strength, including uniaxial compressive strength (UCS), Young’s modulus (E), and Poisson’s ratio (ν), are critical attributes of rock materials vital for applications such as rock slope stability assessment, tunnel construction, and foundation design. Conventionally, the measurement of UCS, E, and ν in laboratory settings resource-intensive, requiring considerable time and financial investment. This study proposes to provide a comprehensive assessment framework using an adaptive boosting machine (AdaBoost), extreme gradient boosting machine (XGBoost), and category gradient boosting machine (CatBoost), to indirectly estimate UCS, E, and ν through streamlined mineralogical analyses. The performance of the boosting trees was analyzed using Taylor diagrams and a suite of five regression metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), variance accounted for (VAF), and the A-20 index. The results indicate that the proposed boosting trees robust predictive capabilities for the constructed database. Notably, AdaBoost demonstrated the highest efficacy in predicting the strength of carbonate rock, achieving R2 values of 0.98, 0.99, and 0.97, with the lowest RMSE values of 0.3164, 0.63, and 0.18, for UCS, E, and ν, respectively. Moreover, variable importance analysis highlighted that the presence of micrite and calcite has a significant impact on predicting UCS, E, and ν of carbonate rock. Furthermore, the AdaBoost model was validated using an independent dataset, which corroborated its predictive reliability. In conclusion, the proposed models present a highly effective methodology for the indirect prediction of essential mechanical properties of carbonate rocks, offering substantial time and cost efficiencies compared to traditional laboratory techniques.

This publication has 57 references indexed in Scilit: