Predicting the young's modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
Document Type
Article
Publication Date
10-1-2022
Abstract
Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree methods for predicting the Young's modulus of rock material. These techniques were applied to a dataset comprising 45 data samples from a mountain range in Malaysia. The final input variables of these models, including p-wave velocity, interlocking coarse-grained crystals of quartz, dry density, and Mica, were selected through a likelihood ratio test. In total, six models were developed: standard artificial neural networks, boosted artificial neural networks, bagged artificial neural networks, classification and regression trees, extreme gradient boosting trees (as a boosted decision tree), and random forest (as a bagging decision tree). The performance of these models was appraised utilizing correlation coefficient (R), mean absolute error (MAE), and lift chart. The findings of this study showed that, firstly, extreme gradient boosting trees outperformed all models developed in this study; secondly, boosting models outperformed the bagging models.
Keywords
Rock deformation, Petrographic study, Rock index tests, Boosting intelligence technique, Bagging intelligence technique
Funders
Ministry of Science and Higher Education of the Russian Federation [075-15-2021-1333]
Publication Title
Applied Sciences-Basel
Volume
12
Issue
20
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND