Prediction of rockhead using a hybrid N-XGBoost machine learning framework
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2021/12/01
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Indian UGC (journal)
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Refrences58
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Citations57
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Xing Zhu
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Jian Chu
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Kangda Wang ORCID (unauthenticated)
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Shifan Wu
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Wei Yan
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Kiefer Chiam
Cite
Zhu, Xing, et al. “Prediction of Rockhead Using a Hybrid N-XGBoost Machine Learning Framework”. Journal of Rock Mechanics and Geotechnical Engineering, vol. 13, no. 6, 2021, pp. 1231-45, https://doi.org/10.1016/j.jrmge.2021.06.012.
Zhu, X., Chu, J., Wang, K., Wu, S., Yan, W., & Chiam, K. (2021). Prediction of rockhead using a hybrid N-XGBoost machine learning framework. Journal of Rock Mechanics and Geotechnical Engineering, 13(6), 1231-1245. https://doi.org/10.1016/j.jrmge.2021.06.012
Zhu, Xing, Jian Chu, Kangda Wang, Shifan Wu, Wei Yan, and Kiefer Chiam. “Prediction of Rockhead Using a Hybrid N-XGBoost Machine Learning Framework”. Journal of Rock Mechanics and Geotechnical Engineering 13, no. 6 (2021): 1231-45. https://doi.org/10.1016/j.jrmge.2021.06.012.
Zhu X, Chu J, Wang K, Wu S, Yan W, Chiam K. Prediction of rockhead using a hybrid N-XGBoost machine learning framework. Journal of Rock Mechanics and Geotechnical Engineering. 2021;13(6):1231-45.
Journal Categories
Science
Geology
Science
Geology
Mineralogy
Science
Geology
Petrology
Technology
Engineering (General)
Civil engineering (General)
Engineering geology
Rock mechanics
Soil mechanics
Underground construction
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization | Geoscience Frontiers |
| 415 | 2021 |
| Efficient reliability analysis of earth dam slope stability using extreme gradient boosting method | Acta Geotechnica |
| 161 | 2020 |
| Application of artificial intelligence to rock mechanics: An overview | Journal of Rock Mechanics and Geotechnical Engineering |
| 76 | 2020 |
The Usefulness of Seismic Surveys for Geotechnical Engineering in Karst: Some Practical Examples | Geosciences |
| 10 | 2020 |
| Prediction of interfaces of geological formations using the multivariate adaptive regression spline method | Underground Space | 20 | 2020 |
Refrences Analysis
The category
Science: Geology 28
is the most frequently represented among the references in this article. It primarily includes studies from Engineering Geology and Geoscience Frontiers. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| ICMFKC with optimize XGBoost classification for breast cancer image screening and detection | Multimedia Tools and Applications |
| 2024 | |
| Projection and identification of vulnerable areas due to heavy snowfall using machine learning and K-means clustering with RCP scenarios | Climate Services |
| 2024 | |
| Predicting peak deviatoric stress of gravels with a novel segmented feature transformation approach | Computers and Geotechnics |
| 2024 | |
| Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine | Energy |
| 1 | 2024 |
| Machine learning-based model for moment capacity prediction and reliability analysis of PSC beams | Structures |
| 2024 |
Citations Analysis
The category
Science: Geology 16
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms and was published in 2021. The most recent citation comes from a 2024 study titled Perakende Sektöründe Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Performans Analizi: Black Friday Satış Tahminlemesi. This article reached its peak citation in 2023, with 29 citations. It has been cited in 48 different journals, 20% of which are open access. Among related journals, the Journal of Rock Mechanics and Geotechnical Engineering cited this research the most, with 4 citations. The chart below illustrates the annual citation trends for this article.