Academic Journal
Prediction of Rock Unloading Strength Based on PSO-XGBoost Hybrid Models
| العنوان: | Prediction of Rock Unloading Strength Based on PSO-XGBoost Hybrid Models |
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| المؤلفون: | Baohua Liu, Hang Lin, Yifan Chen, Chaoyi Yang |
| المصدر: | Materials, Vol 17, Iss 17, p 4214 (2024) |
| بيانات النشر: | MDPI AG |
| سنة النشر: | 2024 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | rock strength, unloading, decreasing confining stress and increasing axial stress, prediction modeling, machine learning, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85 |
| الوصف: | Rock excavation is essentially an unloading behavior, and its mechanical properties are significantly different from those under loading conditions. In response to the current deficiencies in the peak strength prediction of rocks under unloading conditions, this study proposes a hybrid learning model for the intelligent prediction of the unloading strength of rocks using simple parameters in rock unloading tests. The XGBoost technique was used to construct a model, and the PSO-XGBoost hybrid model was developed by employing particle swarm optimization (PSO) to refine the XGBoost parameters for better prediction. In order to verify the validity and accuracy of the proposed hybrid model, 134 rock sample sets containing various common rock types in rock excavation were collected from international and Chinese publications for the purpose of modeling, and the rock unloading strength prediction results were compared with those obtained by the Random Forest (RF) model, the Support Vector Machine (SVM) model, the XGBoost (XGBoost) model, and the Grid Search Method-based XGBoost (GS-XGBoost) model. Meanwhile, five statistical indicators, including the coefficient of determination (R 2 ), mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), were calculated to check the acceptability of these models from a quantitative perspective. A review of the comparison results revealed that the proposed PSO-XGBoost hybrid model provides a better performance than the others in predicting rock unloading strength. Finally, the importance of the effect of each input feature on the generalization performance of the hybrid model was assessed. The insights garnered from this research offer a substantial reference for tunnel excavation design and other representative projects. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1996-1944 |
| Relation: | https://www.mdpi.com/1996-1944/17/17/4214; https://doaj.org/toc/1996-1944; https://doaj.org/article/f1b1c8ff109b489c83c409fd8f0ed6f0 |
| DOI: | 10.3390/ma17174214 |
| الاتاحة: | https://doi.org/10.3390/ma17174214 https://doaj.org/article/f1b1c8ff109b489c83c409fd8f0ed6f0 |
| رقم الانضمام: | edsbas.119C3BFF |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 19961944 |
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| DOI: | 10.3390/ma17174214 |