EP3647533A1 - Optimierung der bohrung eines tunnelbohrers in abhängigkeit von den wechselwirkungen zwischen boden und maschine - Google Patents

Optimierung der bohrung eines tunnelbohrers in abhängigkeit von den wechselwirkungen zwischen boden und maschine Download PDF

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Publication number
EP3647533A1
EP3647533A1 EP19207267.6A EP19207267A EP3647533A1 EP 3647533 A1 EP3647533 A1 EP 3647533A1 EP 19207267 A EP19207267 A EP 19207267A EP 3647533 A1 EP3647533 A1 EP 3647533A1
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EP
European Patent Office
Prior art keywords
machine
terrain
drilling
tbm
individuals
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Pending
Application number
EP19207267.6A
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English (en)
French (fr)
Inventor
Estelle LORANT
Philippe MARIMOUTOU
Nicolas JANICAUD
Nicolas Braud
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Bouygues Travaux Publics SAS
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Bouygues Travaux Publics SAS
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Application filed by Bouygues Travaux Publics SAS filed Critical Bouygues Travaux Publics SAS
Publication of EP3647533A1 publication Critical patent/EP3647533A1/de
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
    • E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
    • E21D9/10—Making by using boring or cutting machines
    • E21D9/108—Remote control specially adapted for machines for driving tunnels or galleries
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
    • E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
    • E21D9/003—Arrangement of measuring or indicating devices for use during driving of tunnels, e.g. for guiding machines
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
    • E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
    • E21D9/10—Making by using boring or cutting machines
    • E21D9/1006—Making by using boring or cutting machines with rotary cutting tools

Definitions

  • the invention relates generally to the drilling of a tunnel, and more particularly the optimization of drilling by a tunneling machine of the mud pressure or variable density type depending on the nature of the terrain at the face of the face.
  • Tunneling machines are known, called tunneling machines, which have a large mobile structure consisting of a large mobile plant at the front of which is disposed a shield having a section compatible with the future section in line with the final shape. of the tunnel (tunnel of circular, bilobed, rectangular section %)
  • the front part of the shield which comes into contact with the working face to cut the geological formation traversed by the tunnel has a cutting head supporting working tools, in particular knobs and knives, driven in rotation at a variable speed and coupled with a thrust that is adapted to the nature of the ground to be dug.
  • the pilot must systematically deduce from the values measured by the various sensors the conditions imposed by the terrain crossed and adjust different characteristics of the TBM (forward speed, torque, etc.) as a function of these values. This adjustment must however take into account the type of TBM used, and therefore requires significant know-how on the part of the pilot.
  • the nature of the terrain is generally not homogeneous, so that the values thus obtained may not be representative of the real nature of the terrain at face size.
  • An objective of the invention is therefore to propose a method for optimizing the characteristics of a tunnel boring machine, in particular a boring machine of the mud pressure or variable density type, which makes it possible to optimize the characteristics of the boring machine in real time in order to improve drilling without risking damage to the TBM, regardless of the type of terrain at the face or the type of TBM used.
  • the invention also provides a computer program product comprising code instructions for the execution of a method for determining a model of terrain / machine interactions during drilling of a terrain. by a tunnel boring machine, in particular a boring machine of the mud pressure or VD type as described above and / or of a method for optimizing the characteristics of a tunnel boring machine, in particular a boring machine of the mud pressure or VD type as described above.
  • the invention provides a storage means readable by computer equipment on which a computer program product comprises code instructions for the execution of a method for determining a model of field / interaction interactions.
  • machine during a drilling of a ground by a tunnel boring machine in particular of a boring machine of type of mud pressure or DV as described above and / or of a process of optimization of characteristics of a boring machine, in particular of a mud pressure or DV type tunneling machine as described above.
  • the invention proposes to determine beforehand a field / machine interaction model (S0 process). Optimization of the characteristics of TBM 1 is then carried out from this model of terrain / machine interactions (S10 optimization process), and no longer solely according to the type of theoretical terrain at the face.
  • S10 optimization process in fact starts from the principle according to which terrains of different nature can interact in a similar way with the front part 3 of the tunnel boring machine 1 according for example to the depth where these terrains are located but also according to the state of the machine.
  • the method S0 for determining a model of terrain / machine interactions and the method S10 for optimization can be implemented by a processing unit 4 adapted to execute the method or methods S10, for example a computer (which can be housed in tunneling machine 1 or remote and connected by wire to said tunneling machine 1) or a remote server having processing means.
  • the processing unit 4 can for example comprise a memory 5 in which are stored the code instructions for the execution of the method or methods and a computer 6 of processor, microprocessor, microcontroller, etc. type, configured to execute said methods. instructions.
  • this processing unit 4 receives as input specific drilling parameters, which can include measurements obtained by one or more sensors C1-C5 fixed on the tunnel boring machine 1 and giving information on the drilling (pressure confinement to the axis of the tunnel boring machine 1, torque of the cutting wheel 2 of the boring machine 1, speed of advance of the boring machine 1), or of the characteristics T1-T3 which can be entered directly into the processing unit 4 and which characterize the TBM 1 itself (radius of the cutting wheel 2 of the TBM 1, surface of the cutting wheel 2 of the TBM 1, etc.)
  • the specific parameters are therefore either from C1-C5 sensors placed on the TBM 1 and capable of measuring an instantaneous value of the corresponding parameter, i.e. linked to TBM 1 itself and are fixed (parameters T1-T3 on the figure 2 ).
  • the model of terrain / machine interactions during drilling of a terrain by a tunneling machine 1 is determined, preferably in the case of a tunneling machine 1 of the mud pressure or VD type .
  • a set of specific drilling parameters by at least one tunnel boring machine 1 given on at least one drilling site is obtained.
  • the drilling parameters are preferably obtained for such tunneling machines.
  • such specific parameters are obtained for different types of construction sites and the nature of the terrain of which differs, so that the terrain / machine interaction model is as complete as possible and can then be applied to any type of construction site.
  • these specific parameters may have been pre-recorded in a database gathering all of these specific parameters measured for different drilling sites. During step S1, the specific parameters are then obtained by interrogating said database.
  • the drilling parameters may in particular include all or part of the following specific parameters: machine parameters recorded by sensors C1-C4 of the processing unit 4 of the tunnel boring machine 1 on the site concerned, characteristics reflecting long geological profiles, measurements obtained by C1-C5 sensors during in situ tests, measurements obtained by C1-C5 sensors during laboratory tests, C1-C5 sensors placed on instrumented wheels of tunnel boring machine 1, characteristics taken from reports provided by a tunneling station 1 sludge treatment station, characteristics obtained by digging risk analysis, surface compaction measurements obtained by dedicated sensors, measurements carried out during the maintenance of the wheels and knives on the cutting wheel 2 , measurements determining the state of wear of the knobs and knives on the cutting wheel 2, characteristics reflecting the configuration of TBM 1 (radius of cutting wheel 2, surface of cutting wheel 2, etc.).
  • step S1 all or part of the following parameters can be obtained, for each site and each TBM 1 examined: a torque of the cutting wheel 2 of the TBM 1 examined, a speed of rotation of the wheel 2 of the tunnel boring machine 1 examined, a speed of advance of the boring machine 1 examined, a surface of the cutting wheel 2 of the boring machine 1 examined, a radius of the cutting wheel 2 of the boring machine 1 examined, a confining pressure at l axis of TBM 1 examined.
  • the torque is for example calculated from measurements obtained by several sensors C1 measuring the torque, for example using frequency converters of each of the motors, placed on each of the main motors of the main drive.
  • the speed of rotation of the cutting wheel 2 is for example measured using a sensor C2 of the angular encoder type, which can be read optically, placed on the rotary joint of the cutting wheel 2.
  • the advancement speed of the tunnel boring machine 1 can be calculated from the measurements obtained by several cylinders elongation sensors C3, for example cable position sensors C2 placed on the various cylinders of the tunnel boring machine, then derived from time to obtain speed. Such a calculation is carried out in real time by the processing unit 4.
  • the contact force is for example calculated from measurements obtained by several sensors according to the configuration of the tunnel boring machine, in particular according to its articulations and can for example be measured using C4 sensors of the pressure sensor type placed on the hydraulic cylinders of the cutting head, on the cylinders of the joints and on the thrust cylinders of the rings.
  • the confinement pressure at the axis is for example measured using a sensor C5 of the pressure sensor type, placed in the confinement chamber as close as possible to the axis of rotation of the cutting wheel.
  • a step of sorting the drilling parameters can also be carried out, so as to not take account of outliers during the following steps of the S0 method. For this, the out-of-limit values of the various C1-C5 sensors used to obtain these parameters are discarded.
  • a set of formulas depending on all or part of the drilling parameters are identified.
  • the formulas can in particular include or be established from business formulas using all or part of the drilling parameters in order to create combinations having great statistical power and which are capable of being interpreted in the field.
  • the business formulas used during this step can be known and correspond, for example, to specific energy formulas for excavating the drilling environment and the desired dimensioning in order to be able to generalize the data thus obtained to all sites. If necessary, these business formulas can be adjusted so as to increase their statistical power.
  • each business formula can be a combination of all or part of the following drilling parameters: the torque of the cutting wheel 2, the speed of rotation of the cutting wheel 2, the speed of advance of the TBM 1, the surface of the cutting wheel 2, the radius of the cutting wheel 2, the confining pressure to the axis.
  • the formulas thus identified can be dimensioned according to the type of TBM 1 used so that the model is applicable to any type of TBM 1 and that the values are comparable to each other.
  • the formulas are each divided by the diameter of the tunnel boring machine 1 used on the site concerned.
  • a set of variables can then be determined from the formulas thus identified. For this, the formulas are calculated using the value of the drilling parameters obtained in step S1. If necessary, these formulas can possibly be transformed so as to increase their statistical power and / or reduce the amount of information processed.
  • the variables can be obtained by determining, on a complete ring, the average of the formulas applied to the drilling parameters, their maximum and / or their standard deviation.
  • the variables are aggregated at the ring scale, by calculating the mean, the maximum and / or the standard deviation for all the formulas.
  • the variables can be calculated by determining, for each formula, the average of the formula over a complete ring, and by determining in addition the maximum of the friction and its standard deviation for each complete ring.
  • PCA principal component analysis
  • the first axis opposes the coefficient of friction to the energies in rotation and in translation.
  • this axis when the individuals (that is to say the rings on which the variables were calculated) lie in the positives of this axis, this implies that there is a lot of friction for little energy expended.
  • This phenomenon is expressed in the business sense by the clogging effect of the cutting wheel 2 (for example the clogging effect of very clayey ground).
  • individuals are in the negatives of this axis, they do not experience this clogging effect.
  • This first axis therefore reflects the clogging of the machine by the ground.
  • the second axis is mainly based on energies in rotation and translation. If the individuals are in the positives of this axis, this implies a significant energy expenditure to advance and excavate the ground, which translates in the business sense by a significant hardness of the ground in front of the cutting wheel 2 (as c 'is the case for granite rock). Conversely, when individuals are in the negatives of this axis, the ground is rather loose so that little energy is needed to move forward. This second axis therefore reflects the hardness of the ground.
  • the third axis is based on strong but stable friction in association with the energy in rotation and in opposition with the energy in translation. If the individuals are in the positive of this axis, this implies that there is a significant friction and a high consumption of the energy in rotation, which results in the trade sense by the heterogeneity of the grounds vis-a-vis the cutting wheel 2 (as is the case for example for a polylithological phase such as a terrain comprising clay lenses or rock blocks within another type of terrain - which will be identified using other axes). Conversely, when individuals are in the negatives of this axis, this results in jolts. This third axis therefore reflects the homogeneity of the terrain.
  • an unsupervised classification of the rings described by these variables is therefore determined so as to obtain a set of groups of rings (or groups of individuals) according to a predefined criterion of the algorithm.
  • This unsupervised classification step is to identify, in the set of five variables applied to the set of complete rings, one or more homogeneous groups which meet the predefined criterion.
  • the unsupervised classification may in particular include a segmentation algorithm (clustering), for example a K-MEANS algorithm.
  • clustering for example a K-MEANS algorithm.
  • DBSCAN English acronym for Density-Based Spatial Clustering of Applications with Noise, for density-based partitioning
  • the K-MEANS algorithm is based on the random initialization of K points becoming K-centers of gravities, then realizes the allocation of points to groups of individuals with respect to their (minimum) distance at the k-centers.
  • the algorithm then recalculates the position of the k-center of gravity as soon as a new point is assigned to the group. It is therefore an iterative algorithm which stops according to a predefined criterion which can include in particular a limit number of iterations, a stabilization of the k-centers of gravities, an allocation of all the points to a group, etc.
  • calculations of coefficients such as the silhouette coefficient can give an optimal number of groups of individuals, corresponding to homogeneous groups of individuals and well separated from each other, depending on the variables obtained during the third step S3 of the S0 process and the drilling parameters obtained in step S1.
  • This number of groups of individuals can, if necessary, be modified in order to obtain a degree of precision and refinement more suitable for the business application of the S0 process, and in particular the need to detail certain field / machine interactions in view to optimize drilling by TBM 1.
  • the K-MEANS algorithm makes it possible to segment the variables into six groups of optimal individuals.
  • the Applicant has however increased the number of groups of individuals to ten in order to obtain a more precise refinement of the different groups of individuals.
  • a supervised classification algorithm is applied to the variables and groups of individuals determined in step S4.
  • This supervised classification step S5 is to obtain the terrain / machine interaction model connecting said variables to said groups of individuals.
  • the supervised classification can notably include a random forest algorithm. This is not, however, limiting, other supervised classification algorithms that can be implemented without going beyond the scope of the present application, such as, by way of nonlimiting examples, a decision tree, a vector machine support or a k-nearest neighbor method.
  • the random forest algorithm uses a set of decision trees to which only a fragmented view of the problem is provided (a sample taken randomly both in terms of individuals and of variables), this in order to predict classification.
  • Each tree will thus classify the individual into one of the groups of individuals determined in step S4 as a function of the information that has been provided to him.
  • the random forest then counts the decisions of each tree and allocates the group of individuals who will have received the most votes to the individual (the ring).
  • the algorithm thus makes it possible to obtain a model connecting the variables to the groups of individuals determined in step S4 and thus translating the possible terrain / machine interactions during drilling by a tunnel boring machine 1.
  • the method S0 makes it possible to determine the terrain / machine interactions, and not the type of terrain encountered by the front part 3 of the shield. Two different types of terrain can thus generate substantially the same terrain / machine interaction and belong to the same group of individuals. And it is this interaction which is determined by the S0 process and which is relevant to optimize drilling, and not the nature of each terrain.
  • a second phase corresponding to the drilling of a tunnel by a tunnel boring machine 1, in particular of the mud pressure or VD type, characteristics of the boring machine 1 can then be optimized (step S10) by applying the interaction model land / machine thus determined.
  • this optimization S10 is preferably carried out for each ring posed by the tunnel boring machine 1.
  • the specific drilling parameters are obtained instantaneously, for each ring.
  • the specific parameters include the drilling parameters necessary for establishing the variables identified in step S2, and can either be entered directly into the processing unit 4, or measured instantaneously by sensors C1-C5 of the tunnel boring machine 1.
  • step S11 the calibration of the various C1-C5 sensors used is carried out in a similar manner between the sensors used to obtain the drilling parameters in step S1 and the sensors used for the instantaneous measurement of said drilling parameters. drilling in step S11.
  • step S12 the variables which have been identified in step S3 are calculated for each ring.
  • the average of the energy in rotation on the ring is calculated, the average of the energy index in translation on the ring, and the average, the maximum and the standard deviation of the friction for the ring, ie five variables in total.
  • step S13 the terrain / machine interaction model is applied to the drilling parameters so as to determine the group of individuals corresponding to the instantaneous drilling parameters which have been measured for this ring. More precisely, during step S13, the variables determined in step S12 are calculated for each ring, the terrain / machine interaction model being applied to said variables.
  • the terrain / machine interaction model is of course applied to said slices of terrain.
  • the characteristics of the tunnel boring machine 1 which can be modified according to the groups of individuals notably include the speed of advancement of the boring machine 1, the force contact, the speed of rotation of the cutting wheel 2 of the boring machine 1 and the torque of the wheel 2 of tunnel boring machine 1.
  • these characteristics correspond substantially to the parameters specific measurements by the different C1-C5 sensors.
  • the optimized characteristics on TBM 1 may be different from the specific parameters used in the terrain / machine interaction model.
  • TBM 1 The modification of the characteristics of TBM 1 can be done manually, by the operator of TBM 1.
  • statistics grouping for each group of individuals identified, information relating to the characteristics of the tunnel boring machine 1, can be produced.
  • These statistics can for example include an average, a minimum, a maximum and / or an optimal value of each of these characteristics of the tunnel boring machine 1 within each group.
  • each group of individuals can be associated with an average, a minimum, a maximum and / or an optimal value of the speed of advance of the tunnel boring machine 1, of the force contact, of the speed of rotation of the wheel 2 cutting the tunnel boring machine 1 and / or the torque of the wheel 2 cutting the boring machine 1.
  • this information can be used to automate said decision making.
  • the invention also relates to a computer program product comprising code instructions for the execution of the method of determining a model of field / machine interactions or of the method of optimizing characteristics for a conforming tunnel boring machine 1 to the invention, as well as storage means readable by computer equipment (for example a hard disk of the computer 6) on which this computer program product is found.

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  • Engineering & Computer Science (AREA)
  • Mining & Mineral Resources (AREA)
  • Environmental & Geological Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Geology (AREA)
  • Excavating Of Shafts Or Tunnels (AREA)
EP19207267.6A 2018-11-05 2019-11-05 Optimierung der bohrung eines tunnelbohrers in abhängigkeit von den wechselwirkungen zwischen boden und maschine Pending EP3647533A1 (de)

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FR1860155A FR3088089B1 (fr) 2018-11-05 2018-11-05 Optimisation du forage d'un tunnelier en fonction d'interactions terrain/machine

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EP3647533A1 true EP3647533A1 (de) 2020-05-06

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US (1) US11448068B2 (de)
EP (1) EP3647533A1 (de)
AU (1) AU2019257539A1 (de)
FR (1) FR3088089B1 (de)
SG (1) SG10201910312PA (de)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112796768A (zh) * 2021-03-08 2021-05-14 盾构及掘进技术国家重点实验室 一种双模隧道掘进机施工掘进参数选择方法
FR3121705A1 (fr) * 2021-04-13 2022-10-14 Vinci Construction Grands Projets Procédé de détection du risque de colmatage d’une roue de coupe d’un tunnelier

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110295915B (zh) * 2019-07-02 2020-08-04 中国科学院武汉岩土力学研究所 一种实现三向力检测的联合破岩tbm复杂地层掘进方法

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FR2874959A1 (fr) 2004-09-07 2006-03-10 Bouygues Travaux Publics Sa Procede et dispositifs pour informer en permanence le conducteur d'un tunnelier de la nature du terrain en fond de taille
CN107577862A (zh) * 2017-08-30 2018-01-12 中铁工程装备集团有限公司 一种tbm在掘岩体状态实时感知系统和方法
KR20180116922A (ko) * 2017-04-18 2018-10-26 인하대학교 산학협력단 쉴드 tbm의 순굴진속도 예측 장치 및 그 방법

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112796768A (zh) * 2021-03-08 2021-05-14 盾构及掘进技术国家重点实验室 一种双模隧道掘进机施工掘进参数选择方法
FR3121705A1 (fr) * 2021-04-13 2022-10-14 Vinci Construction Grands Projets Procédé de détection du risque de colmatage d’une roue de coupe d’un tunnelier

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FR3088089B1 (fr) 2022-04-08
FR3088089A1 (fr) 2020-05-08
AU2019257539A1 (en) 2020-05-21
SG10201910312PA (en) 2020-06-29
US11448068B2 (en) 2022-09-20
US20200141237A1 (en) 2020-05-07

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