Classification of upper-soft lower-hard shield tunnel face and risk evaluation based on Kriging method and Naive Bayes

被引：0
|
作者：
Qiushi W. [1 ,2 ]
Wenqi D. [1 ,2 ]
Zhijian Z. [3 ]
Aiguo L. [4 ]
Yafei Q. [1 ,2 ]
机构：
[1] College of Civil Engineering, Tongji University, Shanghai
[2] Key Laboratory of Geoteehnieal and Underground Engineering of Ministry of Education, Tongji University, Shanghai
[3] China Railway Southern Investment Group Co., Ltd., Shenzhen
[4] Shenzhen Geotechnieal Investigation & Surveying Institute (Croup) Co.. Ltd., Shenzhen
来源：
Tumu Gongcheng Xuebao/China Civil Engineering Journal | 2022年 / 55卷
关键词：
Kriging method; Naive Bayes; risk classification; shield; upper-soft lower-hard stratum;
D O I：
10.15951/j.tmgcxb.2022.s2.04
中图分类号：
学科分类号：
摘要：
Im order to quantitatively analyze the risk of shield tunnel faee in the upper-soft and lower-hard stratum, this paper proposes a tunnel faee visualization method based on Kriging and a tunnel faee risk classification method based on Naive Bayes. First, adopting Kriging method, the stratum distribution of tunnel face is calculated and visualized according to the geological data and the designed tunnel axis data. Tlien, the tunnel face is classified according to their physical and mechanical parameters, which is stored in the form of an array. Second, the relationship between the seven mechanical parameters of the tunnel face and the three risks (i. e cutter head wear, shield attitude adjustment and cutter head torque fluctuation) is proposed through the literature analysis. The tunnel face parameters are efficiently calculated via the stored array, and the tunnel face risk is divided into 5 levels from low to high, I ~ V. An automatic tunnel face risk classification method based on Naive Bayes is then proposed, which considers both discrete and continuous parameters. Finally, the geological data and designed tunnel axis data of a certain interzone of 2nd phase of Shenzhen line 11 were collected and used to verify the feasibility of the proposed method. The accuracy of automatic tunnel face risk classification can reach 89%. The proposed method predicts the risk of the tunnel face in the upper-soft and lower-hard strata during the survey and design stage, providing a reference for the optimization of design and construction. © 2022 Editorial Office of China Civil Engineering Journal. All rights reserved.
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页码：66 / 73
页数：7
相关论文
共 18 条
  • [1] He Xiangfan, Research on the disturliance mechanism ami counter measures of shield tunnel crossing upper-soft lower-hard stralurn, (2017)
  • [2] Toth A, Gong Q, Zhao J., Case studies of IBM tunneling performance in rock-soil interface mixed ground, Tunnelling and Underground Sjiace Technology, 38, pp. 140-150, (2013)
  • [3] Kostami J., Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground, Tunnelling and Underground Space Technology, 57, pp. 173-182, (2016)
  • [4] Balta G C K, Dikmen I, Birgouul M T., Bayesian network based decision support for predicting and mitigating delay risk in TBM tunnel projects, Automation in Construction, 129, (2021)
  • [5] Hyun K C, Min S, Choi H, Et al., Risk analysis using fault-tree analysis (FT A) and analytic' hierarchy process (AHP) applicable to shield TBM tunnels, Tunnelling and Underground Space Technology, 49, pp. 121-129, (2015)
  • [6] Zhou C, Kong T, Zhou Y, Unsupervised spectral clustering for shield tunneling machine monitoring data with complex network theory, Automation in Construction, 107, (2019)
  • [7] Shen S L, Elbal K, Shalun W M, Et al., Real-time prediction of shield moving trajectory during tunnelling, Acta Geotechnica, 17, pp. 1533-1549, (2022)
  • [8] Hasanpour R, Rostami J, Schmitt J, Et al., Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks, Journal of Rock Mechanics and Geotechnical Engineering, 12, 1, pp. 21-31, (2020)
  • [9] Xue Y, Bai C H, Qiu D H, Et al., Predicting roekburst with database using particle swarm optimization and extreme learning machine, Tunnelling and Underground Space Technology, 98, (2020)
  • [10] Hassanpour J., Development of an empirical model to estimate disc cutter wear for sedimentary and low to medium grade metamorphic rocks, Tunnelling and Underground Space Technology, 75, pp. 90-99, (2018)
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