Tsang, Long and He, Biao and Rashid, Ahmad Safuan A. and Jalil, Abduladheem Turki and Sabri, Mohanad Muayad Sabri (2022) Predicting the young's modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques. Applied Sciences-Basel, 12 (20). ISSN 2076-3417, DOI https://doi.org/10.3390/app122010258.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Science and Higher Education of the Russian Federation [075-15-2021-1333] |
| Uncontrolled Keywords: | Rock deformation; Petrographic study; Rock index tests; Boosting intelligence technique; Bagging intelligence technique |
| Subjects: | Q Science > QD Chemistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 26 Sep 2023 00:51 |
| Last Modified: | 26 Sep 2023 00:51 |
| URI: | http://eprints.um.edu.my/id/eprint/40872 |
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