CN112733432A - Tunneling control method and system under extremely complex geological conditions of TBM - Google Patents
Tunneling control method and system under extremely complex geological conditions of TBM Download PDFInfo
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Abstract
The invention discloses a tunneling control method and a tunneling control system under extremely complex geological conditions of TBM, which comprise the following steps: establishing a TBM tunneling sample database, and establishing a TBM control decision model according to the dynamic interaction rule of TBM tunneling parameters and rock mass mechanical parameters; acquiring TBM working state information, and evaluating the TBM health state; and if the health state of the TBM is not optimal, the TBM controls the decision model to adjust the tunneling parameters and state so that the TBM tunnels in the optimal health state.
Description
Technical Field
The invention belongs to the technical field of tunnel engineering, and particularly relates to a tunneling control method and system under extremely complex geological conditions of a TBM (tunnel boring machine).
Background
The statements herein merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, the construction of large and large TBM tunnels has shifted towards remote areas, which are large and have extremely complicated geological conditions, and there are complicated geological disasters such as granite erosion zones, active fracture zones, high ground pressure, large scale fracture zones, and the like. The rapid tunneling of the TBM meets the unprecedented challenge, the construction progress of the engineering is puzzled by the problems of slow excavation, frequent blocking and the like, and the necessary requirement of important engineering construction is to improve the tunneling efficiency of the TBM under the extremely complicated geological conditions.
The selection and control of tunneling parameters in TBM construction basically depend on human experience to make judgment and adjustment, and other TBM tunneling intelligent systems improve the matching of the tunneling parameters and rock state parameters of the TBM to obtain satisfactory results, but the methods are only suitable for uniform distribution of rock mechanics parameters and relatively constant rock mechanics parameters. The extremely complex geological conditions have the following two characteristics:
firstly, the distribution of rock mass mechanical parameters is not uniform. The simplest example is a rock soft-hard interbed, but under extremely complicated geological conditions, the distribution of rock mechanical parameters of the tunnel face can be in various possible situations, which results in that the determination of the tunneling parameters of the TBM by using a certain rock mechanical parameter of the current tunnel face is very inaccurate, and a negative effect is likely to be generated.
Secondly, the mechanical parameters of the rock mass are not constant. In general, when the rock mechanics parameters of the tunnel face are tunneled in the same rock stratum, the rock mechanics parameters are relatively constant. That is, the rock mechanics parameters are substantially the same when there is no large change in lithology. However, under extremely complex geological conditions, the rock mechanics parameters of the same lithology can be mutated, such as a granite altered zone, and the rock mechanics parameters of the same lithology can be mutated from the rock close to granite to the rock mechanics parameters after desertification, so that the two rock mechanics parameters have a great difference.
Under the circumstances, it is very important to research and invent a TBM high-efficiency tunneling system suitable for extremely complicated geological conditions.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a tunneling control method and a tunneling control system under the extremely complex geological condition of a TBM (tunnel boring machine).
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, an embodiment of the present invention provides a tunneling control method under a TBM extremely complex geological condition, including the following steps:
establishing a TBM tunneling sample database, and establishing a TBM control decision model according to the dynamic interaction rule of TBM tunneling parameters and rock mass mechanical parameters;
acquiring TBM working state information, and evaluating the TBM health state; and if the health state of the TBM is not optimal, the TBM controls the decision model to adjust the tunneling parameters and state so that the TBM tunnels in the optimal health state.
As a further technical scheme, the data information of the TBM tunneling sample database comprises TBM tunneling parameters and rock mechanics parameter information.
As a further technical scheme, the dynamic interaction rule of the TBM tunneling parameters and the rock mechanics parameters is obtained by the following steps:
the method comprises the steps of analyzing the mutual relation between the rock mass mechanics parameters and the TBM tunneling parameters in the TBM tunneling process by using a deep learning algorithm, and establishing a dynamic interaction rule of the TBM tunneling parameters and the rock mass mechanics parameters.
As a further technical scheme, the rock mass mechanics parameter obtaining process comprises the following steps:
obtaining rock mass mechanics parameters through a rock mass database corresponding to the TBM tunneling parameters; carrying out statistical analysis on the scale distribution of the diameters of the muck particles through image acquisition to obtain rock mechanical parameters; and performing weighted fusion on the rock mass mechanical parameters obtained by the two methods through an artificial neural network to obtain a real solution of the distribution of the rock mass mechanical parameters of the tunnel face.
As a further technical scheme, the TBM working state information comprises cutter thrust, cutter torque, penetration and propulsion speed.
As a further technical scheme, the evaluation grades of the TBM health state are divided into excellent, good, medium and poor:
the evaluation of the TBM health state is carried out according to a set parameter threshold interval, the TBM health state is in a first set threshold interval, and the evaluation grade is excellent; within a second set threshold interval, the evaluation grade is good; within a third set threshold interval, the evaluation grade is medium; within the fourth set threshold interval, the evaluation level is poor.
As a further technical scheme, if the health state of the TBM is good, a unit with a problem of the TBM is determined according to the working state information of the TBM, and corresponding tunneling parameters are adjusted to obtain the optimal solution for adjusting the current tunneling parameters and the TBM state of the TBM.
As a further technical scheme, if the health state of the TBM is medium or poor, the TBM is controlled to stop tunneling, various information is analyzed, and tunneling is continued after the problem is solved.
As a further technical scheme, TBM tunneling information, rock mass mechanics parameter information, grouting information and muck image information are collected, a TBM construction information base is established, deep learning training is carried out on the information of the TBM with a poor health state, the optimal solution of the relation between the TBM tunneling parameters and the rock mass mechanics parameters is fitted again, and the tunneling parameters of the TBM under the same condition are adjusted.
In a second aspect, an embodiment of the present invention further provides a tunneling control system under a TBM extremely complex geological condition, including:
the model building module is used for building a TBM tunneling sample database and building a TBM control decision model according to the dynamic interaction rule of the TBM tunneling parameters and the rock mass mechanical parameters;
the state evaluation module is used for acquiring the working state information of the TBM and evaluating the health state of the TBM; and if the health state of the TBM is not optimal, the TBM controls the decision model to adjust the tunneling parameters and state so that the TBM tunnels in the optimal health state.
The beneficial effects of the above-mentioned embodiment of the present invention are as follows:
the method of the invention fully utilizes the advantages of big data and an intelligent platform, on one hand, various data of TBM construction can be collected and used as important data support for adjusting the tunneling parameters of the TBM in the future, and the more the engineering construction quantity is, the more intelligent the control system is; on the other hand, based on a cloud intelligent platform, all data in the TBM construction process are shared in real time, remote monitoring of the TBM construction process is achieved, the health state of each part of the TBM can be transmitted to the mobile end of a relevant responsible person in real time, and real intelligent construction is achieved.
The method of the invention provides an acquisition mechanism of rock mass mechanics parameter distribution, combines the TBM muck image acquisition technology with the interaction rule of TBM tunneling parameters and rock mass mechanics parameters, and combines the two by using an intelligent algorithm, thereby realizing the real-time monitoring of the rock mass mechanics parameter distribution of the tunnel face in front of the TBM. The method can ensure that the TBM automatically takes effective corresponding measures when meeting extremely complex geological conditions, and serious accidents such as TBM blocking and the like are prevented.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic diagram of the principle of a tunneling control method according to one or more embodiments of the present invention;
in the figure: the spacing or dimensions between each other are exaggerated to show the location of the various parts, and the illustration is for illustrative purposes only.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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 invention 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 exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and/or "the" are intended to include the plural forms as well, unless the invention expressly state otherwise, 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;
for convenience of description, the words "up", "down", "left" and "right" in the present invention, if any, merely indicate correspondence with up, down, left and right directions of the drawings themselves, and do not limit the structure, but merely facilitate the description of the invention and simplify the description, rather than indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
The terms "mounted", "connected", "fixed", and the like in the present invention should be understood broadly, and for example, the terms "mounted", "connected", "fixed", and the like may be fixedly connected, detachably connected, or integrated; the two components can be connected mechanically or electrically, directly or indirectly through an intermediate medium, or connected internally or in an interaction relationship, and the terms used in the present invention should be understood as having specific meanings to those skilled in the art.
As introduced in the background art, the selection and control of tunneling parameters in TBM construction basically depend on human experience to make judgment and adjustment, and other intelligent TBM tunneling systems improve the matching of the tunneling parameters and rock state parameters of the TBM to obtain satisfactory results, but the methods are only suitable for the conditions that the rock mechanical parameters are uniformly distributed and the rock mechanical parameters are relatively constant, and once the TBM meets extremely complex geological conditions, a series of significant problems occur. In order to solve the technical problems, the invention provides a tunneling control method and a tunneling control system under extremely complex geological conditions of a TBM.
Example 1:
in a typical embodiment of the invention, as shown in fig. 1, a high-efficiency tunneling method based on a cloud intelligent platform under extremely complex geological conditions of a TBM is provided, the cloud intelligent platform is an artificial intelligent cloud platform based on various deep learning and machine learning frameworks, and has strong hardware resource management capability and high-efficiency model development capability, and the distribution condition of rock mechanical parameters in front of a tunnel face can be rapidly and efficiently calculated by taking big data information constructed by the TBM as data support.
The method comprises the following steps:
establishing a TBM tunneling sample database according to the TBM tunneling parameter information, the rock mass mechanics parameter information and the like, and solving a real solution of the rock mass mechanics parameter distribution of the tunnel face by combining a high-definition muck image acquisition technology;
establishing a TBM control decision model and a TBM tunneling big data cloud intelligent platform of a TBM on-line monitoring system, determining tunneling parameters and working states of the TBM according to rock mechanics parameter distribution and the TBM health state, and sending specific information to an intelligent terminal;
when the TBM health evaluation is not excellent, the cloud intelligent platform can carry out deep learning on the related information, refit the related data, and add the information after the interaction rule is updated into the sample database as sample data.
Before the TBM tunneling sample database is established, information acquisition of the TBM is carried out, wherein the information acquisition comprises the acquisition of TBM tunneling parameters, TBM current working state information, rock mass mechanics parameter information and unfavorable geologic body information, and the information constitutes a basic information source of the TBM tunneling cloud intelligent platform.
The method comprises the steps of establishing a TBM tunneling sample database through various collected information, obtaining a dynamic interaction relation between rock mass mechanical parameters and TBM tunneling parameters in the TBM tunneling process by applying a deep learning algorithm, establishing a dynamic interaction rule between the TBM tunneling parameters and the rock mass mechanical parameters, and establishing a TBM control decision model based on the dynamic interaction relation, wherein the TBM control decision model can control and adjust the tunneling parameters.
It should be noted that the interaction rule is not constant, and as the TBM tunneling distance increases, the sample database of the dynamic interaction relationship between the TBM tunneling parameters and the rock mass mechanics parameters gradually increases, and the deep learning algorithm can continuously adjust the interaction rule between the rock mass mechanics parameters and the TBM tunneling parameters according to more learning samples, so as to achieve the purpose of dynamic interaction.
In an optional embodiment, according to a TBM tunneling database and a muck image acquisition technology, a real solution of the mechanical parameter distribution of the tunnel face rock mass is obtained through artificial neural network calculation.
Specifically, the TBM tunneling parameters are collected through a TBM online monitoring system; the TBM online monitoring system is provided with a collecting end, wherein the collecting end comprises 10 sensors distributed on the TBM, 4 sensors are arranged on a cutter head and a cutter head driving system, 2 sensors are arranged on a supporting system, 2 sensors are arranged on a propelling system, and 2 sensors are arranged on a hydraulic and electric control system.
The current working state information of the TBM is acquired through an information terminal of the TBM, the TBM is provided with a master control chamber, and the information terminal collects the working state information of the TBM, including cutter thrust (F), cutter torque (T), penetration (P) and propulsion speed (R).
The rock mass mechanics parameter information is acquired through a real-time information acquisition terminal, the terminal acquires rock mass mechanics parameter distribution through two modes, one mode is that the integral strength of the rock mass mechanics parameter is judged through a rock mass database corresponding to TBM tunneling parameters, and the other mode is that the scale distribution of the diameter of muck particles is acquired and analyzed through a high-definition muck image acquisition technology, so that the rock mass mechanics parameter distribution of a tunnel face is obtained.
In a further embodiment, the cloud intelligent platform simultaneously realizes multiple functions of data collection, online monitoring, data analysis, decision making and information submission, and really realizes intelligent and efficient tunneling of the TBM; the TBM online monitoring system can evaluate the health state of the TBM, the evaluation grades are divided into four grades of excellent, good, medium and poor, and under the non-excellent condition, the cloud intelligent platform can deeply learn related data and take corresponding countermeasures.
In the embodiment, the intelligent terminal comprises various clients of the cloud platform, including a PC (personal computer) end and an intelligent mobile phone end, the related information of the cloud intelligent platform can be synchronized at all the clients, and technicians with different authorization certificates have different operation authorities, so that the information of the TBM is remotely monitored and controlled; and performing man-machine interaction on the collected various information and the decision suggestion of the TBM.
In order to make the technical solutions of the present invention more clearly understood by those skilled in the art, the technical solutions of the present invention will be described in detail below with reference to specific embodiments.
The invention provides a tunneling method based on a cloud intelligent platform under extremely complex geological conditions of TBM, which comprises the following specific implementation steps:
acquiring TBM tunneling parameters and rock mass mechanics parameter information, establishing a TBM tunneling sample database, analyzing the correlation (namely the TBM rock-machine relationship) between the TBM tunneling parameters and the rock mass mechanics parameters by using a deep learning algorithm, and establishing a dynamic interaction rule of the TBM tunneling parameters and the rock mass mechanics parameters, thereby establishing a TBM control decision model to adjust the tunneling parameters; in the step, a TBM tunneling parameter is obtained by monitoring through a sensor arranged on the TBM; and (3) carrying out statistical analysis on the scale distribution of the diameter of the muck particles by a muck image acquisition technology, classifying according to the range of the current tunneling parameter of the TBM, and finally calculating the rock mechanics parameter of the current tunnel face based on the distribution of the diameter of the muck particles by a deep learning algorithm.
The TBM rock-machine relationship and the muck particle identification obtain different rock mechanical parameters, the TBM rock-machine relationship can predict the corresponding rock mechanical parameters, the muck particle identification can also obtain the corresponding rock mechanical parameters, the weights of the two methods are determined through an artificial neural network, the rock mechanical parameters obtained by the two methods are subjected to weighted fusion, and finally the real solution of the distribution of the rock mechanical parameters on the tunnel face is obtained.
According to the obtained real solution of the distribution of the rock mass mechanical parameters, the cloud intelligent platform can automatically adjust the TBM tunneling parameters, meanwhile, the TBM online monitoring system can automatically monitor the working state information, namely the health condition, of each main unit of the TBM, and evaluate the current health state of the TBM, wherein the evaluation grades are excellent, good, medium and poor and are respectively represented as green, yellow, orange and red. The TBM online monitoring system is provided with a data analysis end, processes, analyzes and contrasts and verifies data, judges the current health state of the TBM according to the result of data processing and analysis, and makes health evaluation.
The evaluation of the TBM health state is carried out according to a set parameter threshold interval, the TBM health state is in a first set threshold interval, and the evaluation grade is excellent; within a second set threshold interval, the evaluation grade is good; within a third set threshold interval, the evaluation grade is medium; within the fourth set threshold interval, the evaluation level is poor.
If the current health status of the TBM is excellent, no parameter adjustment is needed.
If the health state of the TBM is good, the cloud intelligent platform analyzes data of the TBM on-line monitoring system, judges which units of the TBM have problems, adjusts related parameters to obtain the optimal solution of the current TBM tunneling parameters and TBM state adjustment, and sends prompt information to a PC end and a mobile end of the cloud intelligent platform to attract attention of constructors. Specifically, when the TBM state is good, the TBM tunneling system information terminal will prompt specific data of several working state information parameters, such as cutter head thrust (F), cutter head torque (T), penetration (P), and propulsion speed (R), and will prompt which parameter has a problem, and the TBM control decision model will automatically adjust the corresponding tunneling parameter and state according to the data of the TBM tunneling system information terminal, and finally keep the health state of the TBM in a "good" state.
The PC end and the mobile end can display a human-computer interaction interface, so that constructors can visually see the current health state of the TBM and can check specific parameters of each sensor of the TBM.
If the health state of the TBM is medium or poor, the TBM stops tunneling, various information is analyzed, reasons are summarized, relevant information is sent to relevant personnel through a cloud intelligent platform, and after the problem is solved, a tunneling command is sent out by a TBM construction responsible person.
The cloud intelligent platform can collect all construction information including tunneling information, rock parameter information, grouting information, muck image recognition information and the like, establish a TBM construction information base, perform deep learning training on the information that the health state of the TBM is not optimal, re-fit the optimal solution of the TBM rock mechanism system, and adjust the tunneling parameters of the TBM under similar conditions. Specifically, the cloud intelligent platform can automatically adjust the TBM tunneling parameters to keep the state of the TBM tunneling parameters in an optimal state, learning samples are obtained once whether the TBM tunneling parameters fail or succeed in each adjustment, and with the increase of the number of the samples, the adjusting success rate of a TBM control decision model established by the cloud intelligent platform through a deep learning framework is higher and higher, so that the TBM is basically kept in the optimal state.
In the TBM tunneling process, various data are collected and analyzed, data with obvious errors are removed, and proper data samples are reserved to form a big data sample library. The cloud intelligent platform adds multiple functions on the basis of the existing TBM intelligent platform, such as judging TBM faults, adjusting related parameters, sending prompt information to a PC end and a mobile end of the cloud intelligent platform, and the like. The cloud intelligent platform performs data mining through various deep learning and machine learning frameworks.
Example 2:
this embodiment provides a tunnelling control system under extreme complicated geological conditions of TBM, includes:
the model building module is used for building a TBM tunneling sample database and building a TBM control decision model according to the dynamic interaction rule of the TBM tunneling parameters and the rock mass mechanical parameters;
the state evaluation module is used for acquiring the working state information of the TBM and evaluating the health state of the TBM; and if the health state of the TBM is not optimal, the TBM controls the decision model to adjust the tunneling parameters and state so that the TBM tunnels in the optimal health state.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A tunneling control method under extremely complex geological conditions of a TBM (tunnel boring machine) is characterized by comprising the following steps:
establishing a TBM tunneling sample database, and establishing a TBM control decision model according to the dynamic interaction rule of TBM tunneling parameters and rock mass mechanical parameters;
acquiring TBM working state information, and evaluating the TBM health state; and if the health state of the TBM is not optimal, the TBM controls the decision model to adjust the tunneling parameters and state so that the TBM tunnels in the optimal health state.
2. The tunneling control method under the extremely complex geological condition of the TBM according to claim 1, wherein the data information of the TBM tunneling sample database comprises TBM tunneling parameters and rock mechanics parameter information.
3. The tunneling control method under the extremely complex geological conditions of the TBM according to claim 1, wherein the dynamic interaction rule of the TBM tunneling parameters and the rock mechanics parameters is obtained by the following steps:
the method comprises the steps of analyzing the mutual relation between the rock mass mechanics parameters and the TBM tunneling parameters in the TBM tunneling process by using a deep learning algorithm, and establishing a dynamic interaction rule of the TBM tunneling parameters and the rock mass mechanics parameters.
4. A tunneling control method under extremely complex geological conditions of a TBM as claimed in claim 3, characterized in that the acquisition process of the rock mechanics parameters is:
obtaining rock mass mechanics parameters through a rock mass database corresponding to the TBM tunneling parameters; carrying out statistical analysis on the scale distribution of the diameters of the muck particles through image acquisition to obtain rock mechanical parameters; and performing weighted fusion on the rock mass mechanical parameters obtained by the two methods through an artificial neural network to obtain a real solution of the distribution of the rock mass mechanical parameters of the tunnel face.
5. The tunneling control method under extremely complex geological conditions of a TBM according to claim 1, characterized in that the TBM operating state information includes cutterhead thrust, cutterhead torque, penetration, and propulsion speed.
6. The tunneling control method under the extremely complex geological condition of the TBM according to claim 1, wherein the evaluation grades of the health state of the TBM are divided into excellent, good, medium and poor:
the evaluation of the TBM health state is carried out according to a set parameter threshold interval, the TBM health state is in a first set threshold interval, and the evaluation grade is excellent; within a second set threshold interval, the evaluation grade is good; within a third set threshold interval, the evaluation grade is medium; within the fourth set threshold interval, the evaluation level is poor.
7. The tunneling control method under extremely complex geological conditions of the TBM according to claim 6, wherein if the health state of the TBM is good, the unit with the TBM having problems is determined according to the working state information of the TBM, and the corresponding tunneling parameters are adjusted to obtain the optimal solution for adjusting the current tunneling parameters and the TBM state of the TBM.
8. The tunneling control method under the extremely complex geological condition of the TBM according to claim 6, wherein if the health state of the TBM is medium or poor, the TBM is controlled to stop tunneling, various information is analyzed, and tunneling is continued after the problem is solved.
9. The tunneling control method under the extremely complex geological condition of the TBM according to claim 6, wherein the tunneling information of the TBM, the information of rock mechanical parameters, the grouting information and the muck image information are collected, a TBM construction information base is established, deep learning training is performed on the information that the TBM is not excellent in health state, the optimal solution of the relation between the tunneling parameters of the TBM and the rock mechanical parameters is fitted again, and the tunneling parameters of the TBM under the same condition are adjusted.
10. A tunneling control system under extremely complex geological conditions of TBM is characterized by comprising:
the model building module is used for building a TBM tunneling sample database and building a TBM control decision model according to the dynamic interaction rule of the TBM tunneling parameters and the rock mass mechanical parameters;
the state evaluation module is used for acquiring the working state information of the TBM and evaluating the health state of the TBM; and if the health state of the TBM is not optimal, the TBM controls the decision model to adjust the tunneling parameters and state so that the TBM tunnels in the optimal health state.
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