RC-XGBoost-Based Mechanical Parameters Back Analysis of Rock Mass in Heavily Fractured Tunnel: A Case in Yunnan, China-Reference-Cited by-同舟云学术

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RC-XGBoost-Based Mechanical Parameters Back Analysis of Rock Mass in Heavily Fractured Tunnel: A Case in Yunnan, China

Published:2023-12-28 Issue: Volume: Page:
ISSN:0723-2632
Container-title:Rock Mechanics and Rock Engineering
language:en
Short-container-title:Rock Mech Rock Eng

Author:

Zhu Menglong,Peng Hao,Liang Ming,Song Guanxian,Huang Nenghao,Xie Weiwei,Han Yu

Funder

Fund for Guangxi Science and Technology Program

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00603-023-03659-8.pdf

Reference44 articles.

1. Cao W, Jiang Y, Sakaguchi O, Li N, Han W (2022) Predication of displacement of tunnel rock mass based on the back-analysis method-BP neural network. Geotech Geol Eng 40:531–544. https://doi.org/10.1007/s10706-021-01874-0

2. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Presented at the KDD ’16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco California USA, pp 785–794

3. Das AJ, Mandal PK, Paul PS, Sinha RK, Tewari S (2019) Assessment of the strength of inclined coal pillars through numerical modelling based on the ubiquitous joint model. Rock Mech Rock Eng 52:3691–3717. https://doi.org/10.1007/s00603-019-01826-4

4. Enrique Sucar L, Bielza C, Morales EF, Hernandez-Leal P, Zaragoza JH, Larrañaga P (2014) Multi-label classification with Bayesian network-based chain classifiers. Pattern Recogn Lett 41:14–22. https://doi.org/10.1016/j.patrec.2013.11.007

5. Gan L, Shen X, Zhang H (2017) New deformation back analysis method for the creep model parameters using finite element nonlinear method. Clust Comput 20(S1):3225–3236

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