The prediction and identification of the faults and changes of the properties of rock mass based on the analysis of the data logged in real time during boring

被引:0
|
作者
Krupa, V [1 ]
Lazarova, E [1 ]
机构
[1] Slovak Acad Sci, Inst Geotech, Kosice, Slovakia
关键词
D O I
暂无
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
The effectiveness and efficiency of the fullface tunnel boring machines during driving depend mainly on the properties of the disintegrated rocks and applied regimes of the boring. During advance of the tunnel boring machine through rockmass under given geological conditions the rockmass properties are constantly changing. The penetration rate of boring, the energy consumption for disintegration per unit of the rock volume, the average wear intensity of the disc cutters on the cutterhead of the TBM and consequently the costs of this international destruction of the rock depends on the physical and mechanical properties of the rocks, geological conditions of the tunnel, tectonic structure and fissuring of the rockmass, applicable regimes of boring, etc. From the analysis of the data of monitoring of regimes of boring results there is functional binding between input parameters (thrust, revolutions) and output parameters of boring (penetration rate of boring, power output of disintegration, size of chips as product of disintegration, etc.). The values of this binding contains information of quasi average properties of rocks on the face. The identification of qualitative and quantitative properties of this binding directly during boring enables relative evaluation of the strength properties of the just disintegrated rocks and so immediate identification of marked, in its substance stochastic, changes of the rockmass properties. Since in most cases the dangerous tectonic faults are accompanied with alteration of the rocks it is possible to predict with some time distance the risk area from the point of view of this technology of tunnelling. The distance depends on character and spreading of the altered rockmass in surroundings of the fault. The monitoring system with attached risk evaluation subsystem can be used as the additional or exacting system of geological reconnaissance data or technical simplification, or substitution, of the drilling probes in the risk areas of the machine tunnelling. The description of the system and some experiences with using this system are described in this paper.
引用
收藏
页码:559 / 564
页数:6
相关论文
共 50 条
  • [1] Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining
    Sun, Mingshe
    Chen, Song
    He, Huafei
    Wang, Wenzheng
    Song, Kezhi
    Lin, Xuebing
    FRONTIERS IN EARTH SCIENCE, 2024, 12
  • [2] Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series
    Junzhou, Huo
    Guopeng, Jia
    Bin, Liu
    Shiwu, Nie
    Junbo, Liang
    Hanyang, Wu
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2022, 236 (10) : 5618 - 5633
  • [3] Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning
    Hou, Shaokang
    Liu, Yaoru
    Yang, Qiang
    JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING, 2022, 14 (01) : 123 - 143
  • [4] Rock fragmentation indexes reflecting rock mass quality based on real-time data of TBM tunnelling
    Xu Li
    Lei-jie Wu
    Yu-jie Wang
    Jin-hui Li
    Scientific Reports, 13
  • [5] Rock fragmentation indexes reflecting rock mass quality based on real-time data of TBM tunnelling
    Li, Xu
    Wu, Lei-jie
    Wang, Yu-jie
    Li, Jin-hui
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [6] Empirical Performance Prediction for Raise Boring Machines Based on Rock Properties, Pilot Hole Drilling Data and Raise Inclination
    Shaterpour-Mamaghani, Aydin
    Copur, Hanifi
    ROCK MECHANICS AND ROCK ENGINEERING, 2021, 54 (04) : 1707 - 1730
  • [7] Empirical Performance Prediction for Raise Boring Machines Based on Rock Properties, Pilot Hole Drilling Data and Raise Inclination
    Aydin Shaterpour-Mamaghani
    Hanifi Copur
    Rock Mechanics and Rock Engineering, 2021, 54 : 1707 - 1730
  • [8] Influence Analysis of Rock Mass Mechanical Properties on Tunnel Rock Stability Based on Sensor Data Integration
    Li, Song
    Chen, Yanxiang
    Guo, Enping
    Huang, Linhua
    JOURNAL OF SENSORS, 2023, 2023
  • [9] Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning附视频
    Shaokang Hou
    Yaoru Liu
    Qiang Yang
    Journal of Rock Mechanics and Geotechnical Engineering, 2022, (01) : 123 - 143
  • [10] DATA ASSIMILATION FOR SIMULATION-BASED REAL-TIME PREDICTION/ANALYSIS
    Hu, Xiaolin
    PROCEEDINGS OF THE 2022 ANNUAL MODELING AND SIMULATION CONFERENCE (ANNSIM'22), 2022, : 404 - 415