CN115142865A - Weathered granite stratum TBM cutter blocking prediction method and system - Google Patents
Weathered granite stratum TBM cutter blocking prediction method and system Download PDFInfo
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- CN115142865A CN115142865A CN202210597736.6A CN202210597736A CN115142865A CN 115142865 A CN115142865 A CN 115142865A CN 202210597736 A CN202210597736 A CN 202210597736A CN 115142865 A CN115142865 A CN 115142865A
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- 238000000034 method Methods 0.000 title claims abstract description 54
- 239000010438 granite Substances 0.000 title claims abstract description 26
- 230000000903 blocking effect Effects 0.000 title claims abstract description 20
- 239000011435 rock Substances 0.000 claims abstract description 47
- 238000003062 neural network model Methods 0.000 claims abstract description 12
- 230000005641 tunneling Effects 0.000 claims abstract description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 23
- 230000015572 biosynthetic process Effects 0.000 claims description 16
- 238000012360 testing method Methods 0.000 claims description 12
- 230000035515 penetration Effects 0.000 claims description 5
- 230000010287 polarization Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000005553 drilling Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 description 8
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000006378 damage Effects 0.000 description 2
- 238000009412 basement excavation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
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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
- E21D9/06—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining
- E21D9/08—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining with additional boring or cutting means other than the conventional cutting edge of the shield
- E21D9/087—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining with additional boring or cutting means other than the conventional cutting edge of the shield with a rotary drilling-head cutting simultaneously the whole cross-section, i.e. full-face machines
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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
- E21D9/003—Arrangement of measuring or indicating devices for use during driving of tunnels, e.g. for guiding machines
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
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Abstract
The invention discloses a weathered granite stratum TBM cutter head blocking prediction method and a weathered granite stratum TBM cutter head blocking prediction system, wherein the method comprises the following steps: acquiring mechanical parameters influencing a TBM cutter head card machine in the TBM tunneling process; establishing a relation model of geophysical prospecting parameters and mechanical parameters, collecting the geophysical prospecting parameters in front of a tunnel face through advanced geological forecast of a tunnel, obtaining corresponding mechanical parameters by using the relation model, and calculating a surrounding rock integrity coefficient; and obtaining a TBM torque by utilizing a trained fully-connected neural network model based on the mechanical parameters, the surrounding rock integrity coefficient, the initial ground stress and the mechanical parameters, comparing the TBM torque with a rated torque value, and judging whether the TBM cutter head is blocked or not. Aiming at the problem of TBM cutter head jamming, the invention establishes a cutter head jamming prediction model, determines mechanical parameters and mechanical parameters influencing the TBM cutter head jamming, can predict the torque of the TBM cutter head, compares the torque of the TBM cutter head with a torque rated value, and predicts whether the cutter head is jammed or not.
Description
Technical Field
The invention relates to the technical field of geological detection, in particular to a weathered granite stratum TBM cutter blocking prediction method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
At present, TBM construction is increasingly adopted by tunnel (tunnel) excavation due to the advantages of safety, high efficiency and the like. The mechanical strength of weathered rock mass becomes low, the permeability becomes big, the characteristics such as meeting water and easily softening into mud are met, TBM is at the tunnelling in-process, relatively poor to the adaptability of weathered rock, once meet the geological disasters such as the weathered granite stratum is very easily taken place surrounding rock collapse, gushing out water and suddenly mud, and then lead to TBM unable rotation, the unusual damage or even the accident such as machine destruction casualties, cause serious economic loss and casualties's scheduling problem, TBM card machine accident has become a great engineering difficult problem that needs to solve in the tunnel construction process urgently.
In the prior art, a mechanical model is mainly established to research the TBM cutterhead card machine, however, the influence factors of the TBM cutterhead card machine are numerous, the reasons for generating the TBM cutterhead card machine are numerous, and the numerous factors and reasons influencing the TBM cutterhead card machine cannot be well expressed only by the mechanical model.
Disclosure of Invention
In order to solve the problems, the invention provides a weathering granite stratum TBM cutter head blocking prediction method and system, which can predict a TBM cutter head blocking in front of a tunnel face in advance, ensure construction safety and improve construction efficiency.
In some embodiments, the following technical scheme is adopted:
a weathered granite stratum TBM cutter blocking prediction method comprises the following steps:
acquiring mechanical parameters influencing a TBM cutter head card machine in the TBM tunneling process;
establishing a relation model of geophysical prospecting parameters and mechanical parameters, collecting the geophysical prospecting parameters in front of a tunnel face through advanced geological forecast of a tunnel, obtaining corresponding mechanical parameters by using the relation model, and calculating a surrounding rock integrity coefficient;
and obtaining a TBM torque by using the trained fully-connected neural network model based on the mechanical parameters, the surrounding rock integrity coefficient, the initial ground stress and the mechanical parameters, comparing the TBM torque with a rated torque value, and judging whether the TBM cutter head is jammed or not.
As an optional scheme, the mechanical parameters affecting the TBM cutter chuck include: the rotating speed and the penetration degree of the cutter head.
As an optional scheme, establishing a relationship model between geophysical prospecting parameters and mechanical parameters specifically includes:
measuring the water content, the resistivity and the longitudinal wave velocity of the rock core through an indoor test; testing the tensile strength and the shear strength of the standard core;
and respectively establishing a relation model of resistivity and water content, a relation model of shear strength and water content and a relation model of tensile strength and longitudinal wave velocity by carrying out related test on a plurality of rock cores.
As an optional scheme, collecting geophysical prospecting parameters in front of the tunnel face through advance geological forecast of the tunnel specifically comprises:
the method comprises the steps of obtaining resistivity parameters in front of a tunnel face through an induced polarization method, obtaining longitudinal wave velocity of a rock body in front of the tunnel face through a seismic wave method, obtaining a rock core through advanced drilling and obtaining longitudinal wave velocity of the rock core.
As an optional scheme, the relationship model is used to obtain corresponding mechanical parameters, specifically:
obtaining water content data based on a relation model of the resistivity parameter, the resistivity and the water content; obtaining shear strength data through a relation model of the water content data, the shear strength and the water content;
and obtaining rock tensile strength data based on a relation model of the core longitudinal wave velocity, the tensile strength and the longitudinal wave velocity.
As an optional scheme, the integrity coefficient of the surrounding rock is calculated, and specifically the square of the velocity ratio of the longitudinal wave of the rock mass to the longitudinal wave of the rock core.
As an optional scheme, the method for judging whether the TBM cutter head is jammed by comparing the TBM torque with a rated torque value specifically includes:
and when the TBM torque is larger than the rated torque value, judging that the TBM is jammed.
In other embodiments, the following technical solutions are adopted:
a weathered granite formation TBM cutter blocking prediction system comprises:
the data acquisition module is used for acquiring mechanical parameters influencing a TBM cutterhead card machine in the TBM tunneling process;
the parameter calculation module is used for establishing a relation model of geophysical prospecting parameters and mechanical parameters, collecting the geophysical prospecting parameters in front of the tunnel face through advanced geological forecast of the tunnel, obtaining corresponding mechanical parameters by using the relation model, and calculating the integrity coefficient of the surrounding rock;
and the cutter head clamping judgment module is used for obtaining a TBM torque by utilizing a trained full-connection neural network model based on the mechanical parameters, the surrounding rock integrity coefficient, the initial ground stress and the mechanical parameters, comparing the TBM torque with a rated torque value and judging whether the TBM cutter head can be clamped or not.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions which are suitable for being loaded by the processor and executing the weathered granite formation TBM cutterhead blocking prediction method.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the above-described weathered granite formation TBM cutterhead sticking prediction method.
Compared with the prior art, the invention has the beneficial effects that:
(1) Aiming at the problem of TBM cutter head blocking, the invention establishes a cutter head blocking prediction model and determines mechanical parameters and mechanical parameters influencing the TBM cutter head blocking; based on the parameters, TBM cutter torque is obtained through prediction, and the TBM cutter torque is compared with a rated torque value, so that whether the cutter is jammed or not can be predicted.
(2) According to the invention, through indoor tests, a relation model of geophysical prospecting parameters and mechanical parameters is established, the geophysical prospecting parameters in front of a tunnel face are obtained through a geophysical method (a seismic wave method and an induced polarization method), the mechanical parameters influencing the TBM cutter head card machine are obtained through the relation model, accurate data support is provided for the TBM cutter head card machine, and safe and efficient construction of the TBM is ensured.
Additional features and advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a weathered granite formation TBM cutterhead blocking prediction method in an embodiment of the invention;
fig. 2 is a schematic diagram of a fully-connected neural network in an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, a weathered granite formation TBM cutterhead blocking prediction method is disclosed, and with reference to FIG. 1, the method specifically comprises the following steps:
s101: acquiring mechanical parameters influencing a TBM cutter head card machine in the TBM tunneling process;
in the embodiment, the mechanical parameters influencing the TBM cutter chucking machine comprise the rotation speed and the penetration degree of the cutter; the two data are directly obtained by data recording in the TBM construction process.
S102: and establishing a relation model of geophysical prospecting parameters and mechanical parameters, collecting the geophysical prospecting parameters in front of the tunnel face through advanced geological forecast of the tunnel, obtaining corresponding mechanical parameters by using the relation model, and calculating the integrity coefficient of the surrounding rock.
Specifically, through an indoor test, firstly measuring the water content, the resistivity and the longitudinal wave velocity of the rock core, and then testing the tensile strength and the shear strength of a standard rock core; and carrying out related test tests on the plurality of rock cores, and establishing a relation model of resistivity and water content, a relation model of shear strength and water content and a relation model of tensile strength and longitudinal wave velocity. The model is established by acquiring data through experiments and fitting the data through a spreadsheet, the fitting forms are not necessarily the same, and the method comprises the following steps: linear fitting, power exponential fitting, etc., and obtaining the correlation coefficient.
Acquiring resistivity parameters in front of the tunnel face by an induced polarization method, acquiring longitudinal wave velocity of a rock body in front of the tunnel face by a seismic wave method, and acquiring a rock core by advanced drilling to acquire longitudinal wave velocity of the rock core;
obtaining water content data based on a relation model of the resistivity parameter, the resistivity and the water content; obtaining shear strength data through a relation model of the water content data, the shear strength and the water content; and obtaining the tensile strength data of the rock mass based on a relation model of the longitudinal wave velocity of the rock core, the tensile strength and the longitudinal wave velocity.
The method for calculating the integrity coefficient of the surrounding rock comprises the following steps: the square of the velocity ratio of the longitudinal wave of the rock mass to the longitudinal wave of the rock core.
S103: and obtaining a TBM torque by utilizing a trained fully-connected neural network model based on the mechanical parameters, the surrounding rock integrity coefficient, the initial ground stress and the mechanical parameters, comparing the TBM torque with a rated torque value, and judging whether the TBM cutter head is blocked or not.
In the embodiment, the obtained tensile strength, the obtained shear strength and the surrounding rock integrity coefficient, as well as the tunnel face front ground stress value, the TBM rotating speed and the penetration degree obtained from a construction unit are substituted into the trained neural network model, the TBM torque is output, and the TBM torque is compared with the TBM rated torque to judge whether the TBM is subjected to cutter head jamming. And when the torque in the TBM tunneling process is larger than the rated torque of the TBM, judging that the TBM is blocked.
In this embodiment, the neural network model adopts a fully connected neural network model as shown in fig. 2; the training process for the fully-connected neural network model comprises the following steps:
the mechanical parameters and mechanical parameters influencing the TBM cutterhead card machine are provided based on a large number of field cases and literature investigation and analysis of the TBM cutterhead card machine. Wherein the mechanical parameters comprise tensile strength, shear strength, ground stress and surrounding rock integrity coefficient; the mechanical parameters include: the rotating speed and the penetration degree of the cutter head.
On the basis, multiple in-tunnel tests are carried out, mechanical parameters and mechanical parameters affecting the TBM cutter head card machine are obtained, and a sample data set for neural network training is established.
And training a full-connection neural network model through the collected mechanical parameters and mechanical parameters, wherein the input parameters are the mechanical parameters and mechanical parameters influencing the TBM cutter head clamping machine, and the output parameters are the TBM cutter head torque.
According to the method, the prediction result of the cutter head card machine is obtained by determining the mechanical parameters and the mechanical parameters which influence the cutter head card machine and through the neural network prediction model, the obtained result is accurate and reliable, guarantee can be provided for building a TBM tunnel, and safe and efficient construction of the TBM is guaranteed.
Example two
In one or more embodiments, a weathered granite formation TBM cutterhead blocking prediction system is disclosed, which specifically comprises:
the data acquisition module is used for acquiring mechanical parameters influencing a TBM cutterhead card machine in the TBM tunneling process;
the parameter calculation module is used for establishing a relation model of geophysical prospecting parameters and mechanical parameters, collecting the geophysical prospecting parameters in front of the tunnel face through advanced geological forecast of the tunnel, obtaining corresponding mechanical parameters by using the relation model, and calculating the integrity coefficient of the surrounding rock;
and the cutter head jamming judgment module is used for obtaining a TBM torque by utilizing a trained full-connection neural network model based on the mechanical parameters, the surrounding rock integrity coefficient, the initial ground stress and the mechanical parameters, comparing the TBM torque with a rated torque value, and judging whether jamming can occur to a TBM cutter head.
It should be noted that, the specific implementation of each module described above has been described in the first embodiment, and is not described in detail here.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, comprising a server comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the weathered granite formation TBM cutterhead blocking prediction method of example one. For brevity, further description is omitted herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processor, a digital signal processor DSP, an application specific integrated circuit ASIC, an off-the-shelf programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
Example four
In one or more embodiments, a computer-readable storage medium is disclosed having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the weathered granite formation TBM cutterhead sticking prediction method described in example one.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive changes in the technical solutions of the present invention.
Claims (10)
1. The weathered granite stratum TBM cutter blocking prediction method is characterized by comprising the following steps:
acquiring mechanical parameters influencing a TBM cutter head card machine in the TBM tunneling process;
establishing a relation model of geophysical prospecting parameters and mechanical parameters, collecting the geophysical prospecting parameters in front of a tunnel face through advanced geological forecast of a tunnel, obtaining corresponding mechanical parameters by using the relation model, and calculating a surrounding rock integrity coefficient;
and obtaining a TBM torque by utilizing a trained fully-connected neural network model based on the mechanical parameters, the surrounding rock integrity coefficient, the initial ground stress and the mechanical parameters, comparing the TBM torque with a rated torque value, and judging whether the TBM cutter head is blocked or not.
2. The weathered granite formation TBM cutter sticking prediction method of claim 1, wherein said mechanical parameters affecting TBM cutter sticking comprise: the rotating speed and the penetration degree of the cutter head.
3. The weathered granite formation TBM cutterhead blocking prediction method according to claim 1, wherein a relational model of geophysical exploration parameters and mechanical parameters is established, and the method specifically comprises the following steps:
measuring the water content, the resistivity and the longitudinal wave velocity of the rock core through an indoor test; testing the tensile strength and the shear strength of the standard core;
and respectively establishing a relation model of resistivity and water content, a relation model of shear strength and water content and a relation model of tensile strength and longitudinal wave velocity by carrying out related test on a plurality of rock cores.
4. The weathered granite formation TBM cutterhead jamming prediction method of claim 1, wherein geophysical prospecting parameters in front of a tunnel face are collected through tunnel advanced geological forecast, and the method specifically comprises the following steps:
the method comprises the steps of obtaining resistivity parameters in front of a tunnel face through an induced polarization method, obtaining longitudinal wave velocity of a rock body in front of the tunnel face through a seismic wave method, obtaining a rock core through advanced drilling, and obtaining the longitudinal wave velocity of the rock core.
5. The weathered granite formation TBM cutterhead jamming prediction method of claim 4, wherein a relational model is used to obtain corresponding mechanical parameters, specifically:
obtaining water content data based on a relation model of the resistivity parameter, the resistivity and the water content; obtaining shear strength data through a relation model of the water content data, the shear strength and the water content;
and obtaining rock tensile strength data based on a relation model of the core longitudinal wave velocity, the tensile strength and the longitudinal wave velocity.
6. The weathered granite formation TBM cutterhead blocking prediction method according to claim 4, characterized in that a surrounding rock integrity coefficient is calculated, specifically, the square of the velocity ratio of the longitudinal wave of the rock mass to the longitudinal wave of the core.
7. The weathered granite formation TBM cutter seizing prediction method according to claim 1, wherein the TBM torque is compared with a rated torque value to determine whether the TBM cutter seizes, and the method specifically comprises:
and when the torque of the TBM is larger than the rated torque value, judging that the TBM is jammed.
8. The utility model provides a morals and manners granite stratum TBM blade disc card machine prediction system which characterized in that includes:
the data acquisition module is used for acquiring mechanical parameters influencing a TBM cutterhead card machine in the TBM tunneling process;
the parameter calculation module is used for establishing a relation model of geophysical prospecting parameters and mechanical parameters, collecting the geophysical prospecting parameters in front of the tunnel face through advanced geological forecast of the tunnel, obtaining corresponding mechanical parameters by using the relation model, and calculating the integrity coefficient of the surrounding rock;
and the cutter head jamming judgment module is used for obtaining a TBM torque by utilizing a trained full-connection neural network model based on the mechanical parameters, the surrounding rock integrity coefficient, the initial ground stress and the mechanical parameters, comparing the TBM torque with a rated torque value, and judging whether jamming can occur to a TBM cutter head.
9. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions, wherein the instructions are suitable for being loaded by the processor and executing the weathered granite formation TBM cutterhead sticking prediction method of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the weathered granite formation TBM cutterhead seize prediction method of any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117371111A (en) * | 2023-11-21 | 2024-01-09 | 石家庄铁道大学 | TBM card machine prediction system and method based on deep neural network and numerical simulation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117371111A (en) * | 2023-11-21 | 2024-01-09 | 石家庄铁道大学 | TBM card machine prediction system and method based on deep neural network and numerical simulation |
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