Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

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1.60

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Yuanxin Cao This email address is being protected from spambots. You need JavaScript enabled to view it.1, Junyan Li1, Jing Hao1, and Xia Yang1

1School of Baotou Railway Vocational Technical College, Bao Tou 010010, Inner Mongolia Autonomous region, China


 

Received: February 2, 2021
Accepted: March 8, 2021
Publication Date: June 12, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202110_24(5).0015  


ABSTRACT


There are great difficulties in tunnel construction. In order to improve the effect and safety of tunnel construction, this paper establishes the basic method of rock elasto-viscoplastic constitutive model. On the basis of drawing lessons from the under-load surface model of over-consolidated soil, according to the idea of relative over-stress, with the lower load surface adjusted to the rock as the reference yield surface, an elastic-viscoplastic constitutive model that can characterize the characteristics of rock softening time-dependent deformation is established. Moreover, this paper uses the newly established elastic viscoplastic model to calculate and simulate the rate sensitivity test results of quartz sandstone and argillaceous sand-stone respectively. In addition, this paper combines the actual needs of tunnel construction to conduct a simulation analysis of the tunnel construction process, and statistics related data to analyze the reliability of the method, and analyze the satisfaction of the method proposed in this paper. The research results show that the model constructed in this paper has certain practical effects and can provide theoretical references for related research.

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Keywords: Constitutive model, Tunnel construction, Simulation analysis, Machine learning