Multi-level Machine Learning-Driven Tunnel Squeezing Prediction: Review and New Insights-Reference-Cited by-同舟云学术

Multi-level Machine Learning-Driven Tunnel Squeezing Prediction: Review and New Insights

Published:2022-06-10 Issue:7 Volume:29 Page:5493-5509
ISSN:1134-3060
Container-title:Archives of Computational Methods in Engineering
language:en
Short-container-title:Arch Computat Methods Eng

Author:

Fathipour-Azar Hadi^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s11831-022-09774-z.pdf

Reference81 articles.

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3. Hoek E, Marinos P (2009) Tunnelling in overstressed rock. In: Proceedings of the Regional Symposium of the International Society for Rock Mechanics, EUROCK, pp 49–60

4. Hoek E, Marinos P (2000) Predicting tunnel squeezing problems in weak heterogeneous rock masses. Tunn Tunn Int 32(11):45–51

5. Shrestha GL, Broch E (2008) Influences of the valley morphology and rock mass strength on tunnel convergence: with a case study of Khimti 1 headrace tunnel in Nepal. Tunn Undergr Space Technol 23(6):638–650. https://doi.org/10.1016/j.tust.2007.12.006

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