Machine learning approach to predicting the macro-mechanical properties of rock from the meso-mechanical parameters-Reference-Cited by-同舟云学术

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Machine learning approach to predicting the macro-mechanical properties of rock from the meso-mechanical parameters

Published:2024-02 Issue: Volume:166 Page:105933
ISSN:0266-352X
Container-title:Computers and Geotechnics
language:en
Short-container-title:Computers and Geotechnics

Author:

Wu Zhijun,Wu You,Weng Lei,Li Mengyi,Wang Zhiyang,Chu Zhaofei

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Computer Science Applications,Geotechnical Engineering and Engineering Geology

Reference60 articles.

1. Abbaszadeh, Maliheh, Soltani-Mohammadi Saeed, Ahmed, Ali, Najah, 2022. Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method. Comput. Geosci. 165, 105140. 10.1016/j.cageo.2022.105140.

2. Abdelaziz, Aly, Zhao, Qi, Grasselli, Giovanni, 2018. Grain based modelling of rocks using the combined finite-discrete element method. Comput. Geotech. 103, 73-81. 10.1016/j.compgeo.2018.07.003.

3. Askaripour, Mahdi, Saeidi, Ali, Rouleau, Alain et al., 2022. Rockburst in underground excavations: a review of mechanism, classification, and prediction methods. Underground Space 7, 577-607. 10.1016/j.undsp.2021.11.008.

4. Banko, M., Brill, E., 2001. Scaling to very very large corpora for natural language disambiguation.

5. Estimating the strength of rock materials;Bieniawski;J. South Afr. Inst. Min. Metall.,1974

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1. Experimental study on evaluating fracture processes of different rocks using multiple physical parameters;Theoretical and Applied Fracture Mechanics;2024-10

2. Data-driven hierarchical multiscale FDEM for simulating rock meso-macro mechanical behavior;Computer Methods in Applied Mechanics and Engineering;2024-07

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