TBM cutterhead jamming prediction method and system based on surrounding rock collapse height estimation

Abstract

The application discloses a TBM cutter disc jam prediction method and system based on surrounding rock collapse height estimation, comprising: obtaining geophysical data and interpreted three-dimensional images of advanced geological prediction; predicting the height h1 of the plastic zone in front of the working face based on the abnormal body information in the three-dimensional image; calculating the compressive strength and shear strength, and then calculating the surrounding rock plastic zone height h2 of the current construction section through numerical simulation; inverting the surrounding rock stress distribution data to obtain the plastic zone height h3 in front of the working face; multi-modal data fusion is performed on the height h1, h2 and h3 data to obtain the final value h of the surrounding rock plastic zone height of the current construction section; and the final value h is used for predicting the cutter jam of the current TBM construction section. The application calculates the plastic zone height from different angles by different methods, and then calculates the final value h of the plastic zone height in front of the working face through multi-modal data fusion, so that the accuracy of predicting the cutter jam risk can be improved.

Description

Technical Field

[0001] This invention relates to the field of TBM cutterhead jamming prediction technology, and in particular to a TBM cutterhead jamming prediction method and system based on surrounding rock collapse height estimation. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] TBM construction offers advantages such as high tunnel forming quality, fast excavation speed, minimal disturbance to the surrounding rock, and environmental friendliness. Therefore, TBMs are frequently used in deep and long tunnels. TBMs are generally used for long tunnels with relatively good geological conditions. However, in the construction of long and deep tunnels using TBMs, the unpredictable geological conditions ahead of the tunnel can lead to serious threats such as large deformation of soft rock, collapse, and rock bursts.

[0004] If the TBM encounters the aforementioned adverse geological conditions during construction, it can easily lead to rock collapse and deformation, resulting in low TBM tunneling efficiency, slow tunneling speed, and even serious accidents such as machine jamming and entrapment. In more severe cases, it can lead to the scrapping of the entire machine and casualties. Therefore, predicting the deformation height of the surrounding rock in front of the tunnel face during the construction period is of great significance for ensuring the safe construction of TBMs.

[0005] Existing technologies for predicting cutterhead jamming during TBM construction mostly rely on mechanical models to determine whether jamming will occur. However, the tunnel environment during TBM construction is complex, and relying solely on mechanical parameters to predict cutterhead jamming has low accuracy, ignoring the influence of surrounding rock parameters and geophysical parameters in the TBM tunnel on cutterhead jamming. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a TBM cutterhead jamming prediction method and system based on surrounding rock collapse height estimation. This method can detect the height of deformation in the plastic zone of the surrounding rock ahead of the tunnel in advance, thereby determining the probability of cutterhead jamming, reducing the risk of cutterhead jamming during TBM construction, and ensuring safe and efficient TBM construction.

[0007] In some implementations, the following technical solutions are adopted:

[0008] A method for predicting TBM cutterhead jamming based on surrounding rock collapse height estimation includes:

[0009] Conduct advanced geological forecasting at the TBM construction site of the current construction section, and obtain geophysical data and interpreted 3D images for the advanced geological forecasting;

[0010] Based on the information of anomalous bodies in the three-dimensional image, predict the height h1 of the plastic zone in front of the face of the machine tool;

[0011] Based on the relationship model between geophysical data and surrounding rock parameters, the compressive strength and shear strength are calculated, and then the height h2 of the plastic zone of the surrounding rock in the current construction section is obtained through numerical simulation.

[0012] The stress distribution of the surrounding rock in the current construction section is obtained by monitoring and measuring the stress behind the working face of the current construction section. The height h3 of the plastic zone in front of the working face is obtained by inverting the stress distribution data of the surrounding rock.

[0013] Multimodal data fusion is performed on the heights h1, h2, and h3 to obtain the final value h of the plastic zone height of the surrounding rock in the current construction section; based on the final value h, the machine jamming prediction of the current TBM construction section is performed.

[0014] Furthermore, based on the final value h, the jamming prediction of the current TBM construction section is made. Specifically, the final value h is compared with the critical criterion of the collapse height H when the TBM jams. If h > H, it indicates that there is a risk of the TBM cutterhead jamming. The critical criterion of the collapse height H is obtained by summarizing historical collapse heights.

[0015] Furthermore, the geophysical data for the advanced geological prediction includes resistivity and wave velocity.

[0016] Furthermore, based on the information of anomalous bodies in the three-dimensional image, the height h1 of the plastic zone in front of the face of the face is predicted, specifically as follows:

[0017] The interpreted three-dimensional images include three-dimensional images of seismic wave reflection coefficients and three-dimensional images of resistivity;

[0018] Regions with decreased seismic wave velocity and regions with decreased resistivity are selected from the three-dimensional image of seismic wave reflection coefficient and the three-dimensional image of resistivity, respectively, as anomaly regions.

[0019] The height h1 of the plastic zone is determined by calculating the average of the reflection coefficient of the anomalous body region on the Z-axis in the three-dimensional image of seismic wave reflection coefficient and the height of the anomalous body on the Z-axis in the three-dimensional resistivity imaging image.

[0020] Furthermore, based on the relationship model between geophysical data and surrounding rock parameters, the compressive strength and shear strength were calculated, specifically:

[0021] Based on the obtained resistivity and wave velocity geophysical data, the compressive strength was calculated using a model relating wave velocity to compressive strength; the shear strength was calculated using a model relating resistivity to shear strength.

[0022] Furthermore, the height h2 of the plastic zone of the surrounding rock in the current construction section is obtained through numerical simulation calculation, specifically including:

[0023] Compressive strength is related to elastic modulus and Poisson's ratio, while shear strength is related to cohesion and internal friction angle. Based on the obtained compressive and shear strengths of the surrounding rock, four parameters—elastic modulus, Poisson's ratio, cohesion, and internal friction angle—are calculated according to the Mohr-Coulomb criterion.

[0024] A geological model of the construction site is established, and the four parameters obtained are assigned to each cell grid of the geological model. The height h2 of the plastic zone of the surrounding rock is calculated by the finite element method or the discrete element method.

[0025] Furthermore, the height h3 of the plastic zone in front of the tunnel face is obtained by inverting the stress distribution data of the surrounding rock. The specific process includes:

[0026] Based on measured surrounding rock stress data, the displacement distribution in front of the tunnel face was inverted using the least squares method. The contour lines with zero displacement were determined based on the inversion results, thereby determining the deformation height h3 in the plastic zone.

[0027] Furthermore, multimodal data fusion is performed on the heights h1, h2, and h3 to obtain the final value h of the plastic zone height of the surrounding rock in the current construction section, specifically:

[0028] A training dataset is constructed based on the heights h1, h2, and h3 measured from the excavated section, and the actual measured height h of the plastic zone after excavation.

[0029] The multimodal fusion neural network model is trained using the training dataset to obtain a well-trained multimodal fusion neural network model.

[0030] The height values ​​h1, h2, and h3 obtained from the current construction section are input into the trained multimodal fusion neural network model, which outputs the final value h of the plastic zone height of the surrounding rock in the current construction section.

[0031] Furthermore, the final value h of the plastic zone height in front of the tunnel face calculated by the multimodal fusion algorithm of the current TBM construction section is compared with the critical criterion H of the collapse height when the TBM gets stuck. The critical criterion H can be determined when the machine gets stuck during the excavation process.

[0032] In other embodiments, the following technical solutions are adopted:

[0033] A TBM cutterhead jam prediction system based on surrounding rock collapse height estimation includes:

[0034] The advanced forecasting module is used to conduct advanced geological forecasting at the TBM construction site, and to acquire geophysical data and interpreted 3D images for advanced geological forecasting.

[0035] The first height calculation module is used to predict the height h1 of the plastic zone in front of the face of the face based on the information of abnormal bodies in the three-dimensional image;

[0036] The second height calculation module is used to calculate the compressive strength and shear strength based on the relationship model between geophysical data and surrounding rock parameters, and then calculate the height h2 of the plastic zone of the surrounding rock in the current construction section through numerical simulation.

[0037] The third height calculation module is used to obtain the stress distribution of the surrounding rock in the current construction section by monitoring and measuring the stress of the surrounding rock behind the working face, and to invert the stress distribution data of the surrounding rock to obtain the height h3 of the plastic zone in front of the working face.

[0038] The high-level fusion module is used to perform multimodal data fusion on the heights h1, h2 and h3 to obtain the final value h of the plastic zone height of the surrounding rock in the current construction section; and to predict the machine jamming of the current TBM construction section based on the final value h.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] (1) This invention analyzes the range of anomalies in the three-dimensional images of advanced forecast data interpretation, and more intuitively shows the height h1 of the plastic zone in front of the face of the tunnel from the images;

[0041] This invention obtains geophysical parameters such as wave velocity and resistivity through advanced geological prediction methods, establishes the relationship between geophysical parameters such as wave velocity and resistivity and compressive strength and shear strength through existing model relationships, and predicts the collapse height h2 by introducing the relationship model between existing geophysical parameters and surrounding rock parameters.

[0042] This invention obtains the height h3 of the plastic zone in front of the tunnel face by inverting the stress distribution data of the surrounding rock;

[0043] This invention utilizes different methods to calculate the height of the plastic zone from different angles, and then uses a multimodal data fusion algorithm to calculate the final value h of the height of the plastic zone in front of the face of the cutting head, which can improve the accuracy of predicting the risk of the cutterhead jamming.

[0044] Other features and advantages 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. Attached Figure Description

[0045] Figure 1 This is a flowchart of the TBM cutterhead jam prediction method based on surrounding rock collapse height estimation in an embodiment of the present invention. Detailed Implementation

[0046] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0048] Example 1

[0049] In one or more embodiments, a TBM cutterhead jamming prediction method based on surrounding rock collapse height estimation is disclosed, combined with... Figure 1 This includes the following processes:

[0050] (1) Conduct advanced geological forecasting at the TBM construction site and obtain geophysical data and interpreted three-dimensional images for advanced geological forecasting;

[0051] In this embodiment, geophysical data includes resistivity and wave velocity, which can be obtained through induced polarization.

[0052] The interpreted three-dimensional images include three-dimensional images of seismic wave reflection coefficients and three-dimensional images of resistivity, which were obtained through seismic wave inversion and resistivity inversion, respectively.

[0053] (2) Predict the height h1 of the plastic zone in front of the face of the face based on the information of anomalous bodies in the three-dimensional image;

[0054] Information about anomalies in 3D images includes the extent and height of the anomaly. Regions exhibiting decreased seismic wave velocity and decreased resistivity are selected from the 3D seismic wave reflection coefficient image and the 3D resistivity image, respectively, as anomaly regions.

[0055] The height h1 of the plastic zone is determined by averaging the height of the reflection coefficient on the Z-axis in the three-dimensional image of seismic wave reflection coefficient and the height of the anomaly on the Z-axis in the three-dimensional image of resistivity.

[0056] (3) Based on the relationship model between geophysical data and surrounding rock parameters, the compressive strength and shear strength are calculated, and then the height h2 of the plastic zone of the surrounding rock in the current construction section is obtained through numerical simulation.

[0057] In this embodiment, the relationship model between geophysical data and surrounding rock parameters includes: the relationship model σ between wave velocity and compressive strength. c=f(V p ), and the model relating resistivity and shear strength τ f =f(ρ).

[0058] Where, σ c τ represents compressive strength. f V represents shear strength. p ρ represents wave speed, and ρ represents resistivity.

[0059] The compressive strength and shear strength of the surrounding rock can be obtained by the above method. Since the compressive strength is related to the elastic modulus and Poisson's ratio, and the shear strength is related to the cohesion and internal friction angle, the four parameters of elastic modulus, Poisson's ratio, cohesion and internal friction angle can be calculated according to the Mohr-Coulomb criterion.

[0060] A geological model is established. To make the numerical model closer to the actual model, the four parameters calculated above are assigned to each cell grid of the geological model. The height h2 of the plastic zone of the surrounding rock is calculated by the finite element method or the discrete element method.

[0061] (4) The stress distribution of the surrounding rock in the current construction section is obtained by monitoring and measuring the stress of the surrounding rock behind the working face, and the height h3 of the plastic zone in front of the working face is obtained by inverting the stress distribution data of the surrounding rock.

[0062] Specifically, on-site monitoring is carried out in the TBM construction section, and the stress distribution of the surrounding rock in the current construction section is obtained by monitoring and measuring the stress of the surrounding rock behind the tunnel face.

[0063] Based on measured surrounding rock stress data, the displacement distribution in front of the tunnel face was inverted using the least squares method. The contour lines with zero displacement were determined based on the inversion results, thereby determining the deformation height h3 in the plastic zone.

[0064] The model parameters of the initial inversion model are the deformation height of the plastic zone; the input parameters during the inversion process are the stress distribution data of the surrounding rock, and the output result is the deformation height h3 of the plastic zone.

[0065] (5) Perform multimodal data fusion on the height h1, h2 and h3 data to obtain the final value h of the plastic zone height of the surrounding rock in the current construction section; predict the machine jamming of the current TBM construction section based on the final value h.

[0066] In this embodiment, a training dataset is constructed based on the heights h1, h2, and h3 measured from the excavated section, and the actual measured height h of the plastic zone after excavation.

[0067] The multimodal fusion neural network model is trained using the training dataset to obtain a well-trained multimodal fusion neural network model.

[0068] Inputting the values of heights h1, h2 and h3 obtained from the current construction section into the trained multimodal fusion neural network model, and outputting the final value h of the surrounding rock plastic zone height of the current construction section.

[0069] As one embodiment, artificial neural network models (ANNs) can be used for the multimodal fusion neural network model; of course, those skilled in the art can select the specific type according to needs.

[0070] (6) Comparing the final value h of the plastic zone height in front of the tunnel face calculated by the multimodal fusion algorithm of the current construction section of the TBM with the critical criterion of collapse height H when the TBM is jammed, so as to judge the jamming risk of the current TBM construction section, thereby realizing the prediction of TBM cutterhead jamming. If h<H, it indicates that the collapse height h obtained for the TBM construction section does not reach the collapse height that causes TBM jamming, therefore the jamming risk of the current TBM construction section is low; if h>H, it indicates that the predicted collapse height of the TBM construction section has exceeded the collapse height that causes TBM jamming, therefore the jamming risk of the current TBM construction section is high.

[0071] Embodiment II

[0072] In one or more embodiments, a TBM cutterhead jamming prediction system based on surrounding rock collapse height estimation is disclosed, comprising:

[0073] An advanced prediction module, configured to perform advanced geological prediction at the TBM construction site, and obtain geophysical data and interpreted three-dimensional images of the advanced geological prediction;

[0074] A first height calculation module, configured to predict the plastic zone height h1 in front of the tunnel face based on abnormal body information in the three-dimensional image;

[0075] A second height calculation module, configured to calculate compressive strength and shear strength based on a relationship model between geophysical data and surrounding rock parameters, and further obtain the surrounding rock plastic zone height h2 of the current construction section through numerical simulation calculation;

[0076] A third height calculation module, configured to obtain the surrounding rock stress distribution of the current construction section through monitoring and measurement of surrounding rock stress behind the tunnel face, and invert the surrounding rock stress distribution data to obtain the plastic zone height h3 in front of the tunnel face;

[0077] A height fusion module, configured to perform multimodal data fusion on the data of heights h1, h2 and h3 to obtain the final value h of the surrounding rock plastic zone height of the current construction section; and perform jamming prediction for the current TBM construction section based on the final value h.

[0078] The specific implementation manners of the above modules have been described in Embodiment I, and will not be elaborated herein.

[0079] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for predicting TBM cutterhead jamming based on surrounding rock collapse height estimation, characterized in that, include: Conduct advanced geological forecasting at the TBM construction site of the current construction section, and obtain geophysical data and interpreted 3D images for the advanced geological forecasting; Based on the information of anomalous bodies in the three-dimensional image, predict the height h1 of the plastic zone in front of the face of the machine tool; Based on the relationship model between geophysical data and surrounding rock parameters, the compressive strength and shear strength are calculated, and then the height h2 of the plastic zone of the surrounding rock in the current construction section is obtained through numerical simulation. The stress distribution of the surrounding rock in the current construction section is obtained by monitoring and measuring the stress behind the working face of the current construction section. The height h3 of the plastic zone in front of the working face is obtained by inverting the stress distribution data of the surrounding rock. Multimodal data fusion is performed on the heights h1, h2, and h3 to obtain the final value h of the plastic zone height of the surrounding rock in the current construction section; based on the final value h, the TBM jamming prediction for the current construction section is performed. Specifically, the height h1 of the plastic zone in front of the face of the face is predicted based on the information of abnormal bodies in the three-dimensional image. The interpreted three-dimensional images include three-dimensional images of seismic wave reflection coefficients and three-dimensional images of resistivity; Regions with decreased seismic wave velocity and regions with decreased resistivity are selected from the three-dimensional image of seismic wave reflection coefficient and the three-dimensional image of resistivity, respectively, as anomaly regions. The height h1 of the plastic zone is determined by calculating the average of the reflection coefficient of the anomalous body region on the Z-axis in the three-dimensional image of seismic wave reflection coefficient and the height of the anomalous body on the Z-axis in the three-dimensional resistivity imaging image. The height h2 of the plastic zone of the surrounding rock in the current construction section is obtained through numerical simulation calculation, specifically including: Based on the obtained resistivity and wave velocity geophysical data, the compressive strength was calculated using the relationship model between wave velocity and compressive strength; the shear strength was calculated using the relationship model between resistivity and shear strength. Compressive strength is related to elastic modulus and Poisson's ratio, while shear strength is related to cohesion and internal friction angle. Based on the obtained compressive and shear strengths of the surrounding rock, four parameters—elastic modulus, Poisson's ratio, cohesion, and internal friction angle—are calculated according to the Mohr-Coulomb criterion. A geological model of the construction site is established, and the four parameters obtained are assigned to each cell grid of the geological model. The height h2 of the plastic zone of the surrounding rock is calculated by the finite element method or the discrete element method. The height h3 of the plastic zone in front of the tunnel face is obtained by inverting the surrounding rock stress distribution data. The specific process includes: Based on measured surrounding rock stress data, the displacement distribution in front of the tunnel face was inverted using the least squares method. The contour lines with zero displacement were determined based on the inversion results, thereby determining the deformation height h3 in the plastic zone.

2. The TBM cutterhead jamming prediction method based on surrounding rock collapse height estimation as described in claim 1, characterized in that, Based on the final value h, the jamming prediction of the current TBM construction section is made. Specifically, the final value h is compared with the critical criterion of the collapse height H when the TBM jams. If h > H, it indicates that there is a risk of the TBM cutterhead jamming. The critical criterion of the collapse height H is obtained by summarizing the historical collapse heights.

3. The TBM cutterhead jamming prediction method based on surrounding rock collapse height estimation as described in claim 1, characterized in that, The geophysical data for the advanced geological prediction includes resistivity and wave velocity.

4. The TBM cutterhead jamming prediction method based on surrounding rock collapse height estimation as described in claim 1, characterized in that, Multimodal data fusion is performed on the heights h1, h2, and h3 to obtain the final value h of the plastic zone height of the surrounding rock in the current construction section, specifically: A training dataset is constructed based on the heights h1, h2, and h3 measured from the excavated section, and the actual measured height h of the plastic zone after excavation. The multimodal fusion neural network model is trained using the training dataset to obtain a well-trained multimodal fusion neural network model. The height values ​​h1, h2, and h3 obtained from the current construction section are input into the trained multimodal fusion neural network model, which outputs the final value h of the plastic zone height of the surrounding rock in the current construction section.

5. The TBM cutterhead jamming prediction method based on surrounding rock collapse height estimation as described in claim 1, characterized in that, The final value h of the plastic zone in front of the tunnel face, calculated by the multimodal fusion algorithm of the current TBM construction section, is compared with the critical criterion H of the collapse height when the TBM gets stuck. The critical criterion H can be determined when the machine gets stuck during the excavation process.

6. A system for implementing the TBM cutterhead jam prediction method based on surrounding rock collapse height estimation as described in claim 1, characterized in that, include: The advanced forecasting module is used to conduct advanced geological forecasting at the TBM construction site, and to acquire geophysical data and interpreted 3D images for advanced geological forecasting. The first height calculation module is used to predict the height h1 of the plastic zone in front of the face of the face based on the information of abnormal bodies in the three-dimensional image; The second height calculation module is used to calculate the compressive strength and shear strength based on the relationship model between geophysical data and surrounding rock parameters, and then calculate the height h2 of the plastic zone of the surrounding rock in the current construction section through numerical simulation. The third height calculation module is used to obtain the stress distribution of the surrounding rock in the current construction section by monitoring and measuring the stress of the surrounding rock behind the working face, and to invert the stress distribution data of the surrounding rock to obtain the height h3 of the plastic zone in front of the working face. The high-level fusion module is used to perform multimodal data fusion on the heights h1, h2 and h3 to obtain the final value h of the plastic zone height of the surrounding rock in the current construction section; and to predict the machine jamming of the current TBM construction section based on the final value h.

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