Short-term rockburst prediction in underground project: insights from an explainable and interpretable ensemble learning model-Reference-Cited by-同舟云学术

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Short-term rockburst prediction in underground project: insights from an explainable and interpretable ensemble learning model

Published:2023-07-10 Issue:12 Volume:18 Page:6655-6685
ISSN:1861-1125
Container-title:Acta Geotechnica
language:en
Short-container-title:Acta Geotech.

Author:

Qiu Yingui,Zhou Jian^ORCID

Funder

National Natural Science Foundation of China

Distinguished Young Scholar Foundation of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Geotechnical Engineering and Engineering Geology

Link

https://link.springer.com/content/pdf/10.1007/s11440-023-01988-0.pdf

Reference107 articles.

1. Adoko AC, Gokceoglu C, Wu L, Zuo QJ (2013) Knowledge-based and data-driven fuzzy modeling for rockburst prediction. Int J Rock Mech Min Sci 61:86–95. https://doi.org/10.1016/j.ijrmms.2013.02.010

2. Afraei S, Shahriar K, Madani SH (2019) Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 1: literature review and data preprocessing procedure. Tunn Undergr Space Technol 83:324–353. https://doi.org/10.1016/j.tust.2018.09.022

3. Alcott JM, Kaiser PK, Simser BP (1999) Use of microseismic source parameters for rockburst hazard assessment. Seism Caused Mines Fluid Inject Reserv Oil Extr. https://doi.org/10.1007/978-3-0348-8804-2_4

4. Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit RA (2019) Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res. https://doi.org/10.12688/wellcomeopenres.15191.1

5. Askaripour M, Saeidi A, Rouleau A, Mercier-Langevin P (2022) Rockburst in underground excavations: a review of mechanism, classification, and prediction methods. Undergr Space. https://doi.org/10.1016/j.undsp.2021.11.008

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