A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance-Reference-Cited by-同舟云学术

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A new hybrid grey wolf optimizer-feature weighted-multiple kernel-support vector regression technique to predict TBM performance

Published:2020-11-25 Issue: Volume: Page:
ISSN:0177-0667
Container-title:Engineering with Computers
language:en
Short-container-title:Engineering with Computers

Author:

Yang Haiqing,Wang Zhihui,Song Kanglei^ORCID

Funder

Project supported by graduate research and innovation foundation of Chongqing, China

Fundamental Research Funds for the Natural Science Fund of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,General Engineering,Modelling and Simulation,Software

Link

http://link.springer.com/content/pdf/10.1007/s00366-020-01217-2.pdf

Reference74 articles.

1. Blindheim OT (2004) TBM performance prediction models. Tunnels & Tunnelling International 36 (12):23,25–27

2. Kahraman S (2002) Correlation of TBM and drilling machine performances with rock brittleness. Eng Geol 65(4):269–283. https://doi.org/10.1016/S0013-7952(01)00137-5

3. Yang HQ, Li Z, Jie TQ, Zhang ZQ (2018) Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunn Undergr Space Technol 81:112–120. https://doi.org/10.1016/j.tust.2018.07.023

4. Liu BL, Yang HQ, Karekal S (2020) Effect of water content on argillization of mudstone during the tunnelling process. Rock Mech Rock Eng 53(2):799–813. https://doi.org/10.1007/s00603-019-01947-w

5. Hassanpour J, Vanani AAG, Rostami J, Cheshomi A (2016) Evaluation of common TBM performance prediction models based on field data from the second lot of Zagros water conveyance tunnel (ZWCT2). Tunnelling Underground Space Technol Incorporating Trenchless Technol Res 52:147–156. https://doi.org/10.1016/j.tust.2015.12.006

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