MétaCan
Menu
Back to cohort

Application of constitutive model in simulation analysis of tunnel construction

2021· article· en· W3170868548 on OpenAlexaboutno aff
Yuanxin Cao, Junyan Li, Jing Hao, Xia Yang

Bibliographic record

VenueJournal of Applied Science and Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsViscoplasticityConstitutive equationGeotechnical engineeringYield surfaceSettlement (finance)Nonlinear systemGeologyStructural engineeringEngineeringComputer scienceFinite element method

Abstract

fetched live from OpenAlex

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. REFERENCES [1] G. Andreotti and C. G. Lai. A nonlinear constitutive model for beam elements with cyclic degradation and damage assessment for advanced dynamic analyses of geotechnical problems. Part II: validation and application to a dynamic soil–structure interaction problem. Bulletin of Earthquake Engineering, 15(7):2803–2825, Jul 2017. [2] Qiang Huang, Hongwei Huang, Bin Ye, Dongmei Zhang, and Feng Zhang. Evaluation of train-induced settlement for metro tunnel in saturated clay based on an elastoplastic constitutive model. Underground Space (China), 3(2):109–124, 2018. [3] HanpengWang, Yong Li, Shucai Li, Qingsong Zhang, and Jian Liu. An elasto-plastic damage constitutive model for jointed rock mass with an application. Geomechanics and Engineering, 11(1):77–94, 2016. [4] MingnianWang, Yucang Dong, and Li Yu. Analytical Solution for a Loess Tunnel Based on a Bilinear Strength Criterion. Soil Mechanics and Foundation Engineering, 57(4):296–304, Sep 2020. [5] Xi bing Li, Shi ming Wang, Lei Weng, Lin qi Huang, Tao Zhou, and Jian Zhou. Damage constitutive model of different age concretes under impact load. Journal of Central South University, 22(2):693–700, 2015. [6] Thayanan Boonyarak and Charles W.W. Ng. Effects of construction sequence and cover depth on crossing-tunnel interaction. Canadian Geotechnical Journal, 52(7):851–867, Oct 2014. [7] Shuling Huang, Chuanqing Zhang, and Xiuli Ding. Hardening–Softening Constitutive Model of Hard Brittle Rocks Considering Dilatant Effects and Safety Evaluation Index. Acta Mechanica Solida Sinica, 33(1):121–140, Feb 2020. [8] M. Mousivand and M. Maleki. Constitutive Models and Determining Methods Effects on Application of Convergence–Confinement Method in Underground Excavation. Geotechnical and Geological Engineering, 36(3):1707–1722, Jun 2018. [9] M Javadi, S Sayadi, and M. Sharifzadeh. Evaluation of Soil Constitutive Model Effects on Numerical Modeling of Settlement Induced by Tunneling in Urban Area, Case Study of the Amirkabir Tunnel. Journal of Analytical and Numerical Methods in Mining Engineering, 10(24):119–136, 2020. [10] Heyam H Shaalan, Romziah Azit, and Mohd Ashraf Mohamad Ismail. Numerical Analysis of TBM Tunnel Lining Behavior using Shotcrete Constitutive Model. Civil Engineering Journal, 4(5):1046, 2018. [11] C. W.W. Ng, H S Sun, G H Lei, J W Shi, and David Masin. Ability of three different soil constitutive models to predict a tunnel’s response to basement excavation. Canadian Geotechnical Journal, 52(11):1685–1698, Apr 2015. [12] Hui Wang, Wei zhong Chen, Qing biao Wang, and Peng qiang Zheng. Rheological properties of surrounding rock in deep hard rock tunnels and its reasonable support form. Journal of Central South University, 23(4):898–905, 2016. [13] Kang Bian, Jian Liu, Wei Zhang, Xiaoqing Zheng, Shaohu Ni, and Zhenping Liu. Mechanical Behavior and Damage Constitutive Model of Rock Subjected to Water-Weakening Effect and Uniaxial Loading. Rock Mechanics and Rock Engineering, 52(1):97–106, 2019. [14] Hua bing Zhao, Yuan Long, Xing hua Li, and Liang Lu. Experimental and numerical investigation of the effect of blast-induced vibration from adjacent tunnel on existing tunnel. KSCE Journal of Civil Engineering, 20(1):431–439, 2016. [15] Ningyu Zhao and Haifei Jiang. Mathematical methods to unloading creep constitutive model of rock mass under high stress and hydraulic pressure. Alexandria Engineering Journal, 60(1):25–38, 2021. [16] Hossain Md Shahin, Teruo Nakai, Kenji Ishii, Toshikazu Iwata, and Shou Kuroi. Investigation of influence of tunneling on existing building and tunnel: Model tests and numerical simulations. Acta Geotechnica, 11(3):679–692, Jun 2016. [17] Alessandra Paternesi, Helmut F. Schweiger, and Peter Schubert. Verification of a rheological constitutive model for shotcrete through back-analysis. Geomechanik und Tunnelbau, 9(4):356–361, aug 2016. [18] E. Soranzo, R Tamagnini, and W Wu. Face stability of shallow tunnels in partially saturated soil: Centrifuge testing and numerical analysis. Geotechnique, 65(6):454–467, Jun 2015. [19] Tomas Janda, Michal Sejnoha, and Jiˇri Sejnoha. Applying Bayesian approach to predict deformations during tunnel construction. International Journal for Numerical and Analytical Methods in Geomechanics, 42(15):1765–1784, Oct 2018. [20] Arash Alimardani Lavasan, Chenyang Zhao, Thomas Barciaga, Alexander Schaufler, Holger Steeb, and Tom Schanz. Numerical investigation of tunneling in saturated soil: the role of construction and operation periods. Acta Geotechnica, 13(3):671–691, Jun 2018. [21] Guan lin Ye and Bin Ye. Investigation of the overconsolidation and structural behavior of Shanghai clays by element testing and constitutive modeling. Underground Space (China), 1(1):62–77, 2016. [22] Osamah Ibrahim Khalaf, Kingsley A. Ogudo, and Manwinder Singh. A fuzzy-based optimization technique for the energy and spectrum efficiencies tradeoff in cognitive radio-enabled 5g network. Symmetry, 13(1):1–14, 2021. [23] Osamah Ibrahim Khalaf, F Ajesh, A. A. Hamad, Gia Nhu Nguyen, and Dac Nhuong Le. Efficient Dual-Cooperative Bait Detection Scheme for Collaborative Attackers on Mobile Ad-hoc Networks. IEEE Access, 2020

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.214
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Science and EngineeringSame topicGrouting, Rheology, and Soil MechanicsFrench-language works237,207