Global Patent Index - EP 3647533 A1

EP 3647533 A1 20200506 - OPTIMISATION OF THE DRILLING OF A TUNNEL-BORING MACHINE ACCORDING TO LAND/MACHINE INTERACTIONS

Title (en)

OPTIMISATION OF THE DRILLING OF A TUNNEL-BORING MACHINE ACCORDING TO LAND/MACHINE INTERACTIONS

Title (de)

OPTIMIERUNG DER BOHRUNG EINES TUNNELBOHRERS IN ABHÄNGIGKEIT VON DEN WECHSELWIRKUNGEN ZWISCHEN BODEN UND MASCHINE

Title (fr)

OPTIMISATION DU FORAGE D'UN TUNNELIER EN FONCTION D'INTERACTIONS TERRAIN/MACHINE

Publication

EP 3647533 A1 20200506 (FR)

Application

EP 19207267 A 20191105

Priority

FR 1860155 A 20181105

Abstract (en)

[origin: AU2019257539A1] The invention relates to a method (S10) for optimizing the characteristics of a tunnel boring machine, particularly a tunnel boring 5 machine of the slurry pressure or VD type, said method comprising the following steps: SO: determining a ground/machine interaction model, S11: instantaneous measurement of the set of specific boring parameters of the tunnel boring machine, 10 S13: determining the group of individuals corresponding to the boring parameters measured in step S11 by means of the ground/machine interaction model, S14: optimizing the characteristics of the tunnel boring machine as a function of the group of individuals thus determined. Figure 3 Obtaining a set of boring parameters of at least one given Si tunnel boring machine over at least one boring site Identifying a set of formulas depending on all or a part S82 of the boring parameters Determining a set of variables based on the S3 SO< formulas thus identified Applying a non-supervised classification algorithm to the S4 variables so as to obtain groups of individuals Applying a supervised classification algorithm to the variables and to S5 the groups of individuals thus determined so as to obtain a ground/ machine interaction model Instantaneous measurement of the set of Sil boring parameter of the tunnel boring machine |Calculating all or a part of the variables determined in step S3 S12 based on the boring parameters measured in step S1 1 IDetermining the group of individuals corresponding to the instantaneous boring S13 parameters by means of the ground/machine interaction model Optimization of boring as a function of the group S14 of individuals thus determined

Abstract (fr)

L'invention concerne un procédé (S10) d'optimisation de caractéristiques d'un tunnelier, notamment d'un tunnelier de type à pression de boue ou VD, ledit procédé comprenant les étapes suivantes :S0 : détermination d'un modèle d'interactions terrain/machine,S11 : mesure instantanée de l'ensemble des paramètres spécifiques de forage du tunnelier,S13: détermination du groupe d'individus correspondant aux paramètres de forage mesurés à l'étape S11 à l'aide du modèle d'interactions terrain/machine,S14 : optimisation des caractéristiques du tunnelier en fonction du groupe d'individus ainsi déterminé.

IPC 8 full level

E21D 9/00 (2006.01); E21B 34/10 (2006.01)

CPC (source: EP US)

E21D 9/003 (2013.01 - EP US); E21D 9/1006 (2013.01 - US); E21D 9/108 (2013.01 - US)

Citation (applicant)

Citation (search report)

  • [A] CN 107577862 A 20180112 - CHINA RAILWAY ENGINEERING EQUIPMENT GROUP CO LTD
  • [A] KR 20180116922 A 20181026 - UNIV INHA RES & BUSINESS FOUND [KR]
  • [A] LAU S C ET AL: "Applying radial basis function neural networks to estimate next-cycle production rates in tunnelling construction", TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, ELSEVIER SCIENCE PUBLISHING, NEW YORK,NY, US, vol. 25, no. 4, 16 March 2010 (2010-03-16), pages 357 - 365, XP027035521, ISSN: 0886-7798, [retrieved on 20100316]

Designated contracting state (EPC)

AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

Designated extension state (EPC)

BA ME

DOCDB simple family (publication)

EP 3647533 A1 20200506; AU 2019257539 A1 20200521; FR 3088089 A1 20200508; FR 3088089 B1 20220408; SG 10201910312P A 20200629; US 11448068 B2 20220920; US 2020141237 A1 20200507

DOCDB simple family (application)

EP 19207267 A 20191105; AU 2019257539 A 20191101; FR 1860155 A 20181105; SG 10201910312P A 20191105; US 201916674882 A 20191105