Tunnel underbreak rock mass excavation method

Abstract

The application discloses a tunnel underbreak rock mass excavation method, and relates to the technical field of tunnel excavation.The tunnel underbreak rock mass excavation method comprises the following steps: obtaining stratum position information; establishing a digital twin model of the tunnel according to design drawings; establishing a risk index prediction model according to the stratum position information; fitting the risk index prediction model and the digital twin model to determine risk feature points; excavating the tunnel and scanning three-dimensional real scene images in the tunnel in real time during the excavation process; laying monitoring equipment at the risk feature points; establishing a three-dimensional real scene model according to the real-time three-dimensional real scene images, performing geometric intersection on the three-dimensional real scene model and the digital twin model, and obtaining the engineering quantity of underbreak rock mass; marking the position of the underbreak rock mass in the tunnel; predicting the deformation of the underbreak rock mass according to the monitoring result of the monitoring equipment; and excavating the underbreak rock mass.The method is helpful to control the excavation quality and ensure the excavation safety.

Description

Technical Field

[0001] This invention relates to the field of tunnel excavation technology, and in particular to a method for excavating under-excavated rock mass in tunnels. Background Technology

[0002] During the excavation of tunnels with large cross sections, factors such as complex geological conditions of the rock mass or improper control of drill-and-blast parameters may lead to under-excavation of the tunnel. Due to the limited visibility inside the tunnel, it is difficult for surveyors to align their foresight and backsight points when conducting measurements inside the tunnel. This makes it difficult to accurately determine parameters such as the amount of under-excavation work and the location of under-excavation in the under-excavated rock mass. The secondary excavation of the under-excavated rock mass is more difficult, and for tunnels with complex geological conditions, secondary excavation of the under-excavated rock mass can easily cause rock mass instability and pose safety hazards. Summary of the Invention

[0003] The main objective of this invention is to propose a method for excavating under-excavated rock mass in tunnels, aiming to solve the technical problems in the prior art where it is difficult to accurately determine parameters such as the amount of under-excavation work and the location of under-excavation in under-excavated rock mass, the secondary excavation of under-excavated rock mass is difficult, and it is easy to cause rock mass instability and safety hazards.

[0004] To achieve the above objectives, the present invention proposes a method for excavating under-excavated rock mass in tunnels, comprising the following steps: conducting geological surveys of the tunnel rock mass to obtain stratigraphic location information; establishing a digital twin model of the tunnel based on design drawings; establishing a risk index prediction model based on the stratigraphic location information; fitting the risk index prediction model with the digital twin model to determine risk feature points; excavating the tunnel and scanning three-dimensional real-scene images within the tunnel in real time during the excavation process; deploying monitoring equipment at the risk feature points; establishing a three-dimensional real-scene model based on the real-time three-dimensional real-scene images, and geometrically intersecting the three-dimensional real-scene model with the digital twin model to obtain the engineering quantity of the under-excavated rock mass; marking the location of the under-excavated rock mass within the tunnel; predicting the deformation of the under-excavated rock mass based on the monitoring results of the monitoring equipment; and excavating the under-excavated rock mass.

[0005] In one embodiment, the step of deploying the detection device at the risk feature point includes: drilling a mounting hole at the risk feature point; inserting a stress gauge into the mounting hole; and communicating with the stress gauge to an external processor.

[0006] In one embodiment, the step of predicting the deformation of the under-excavated rock mass based on the monitoring results of the monitoring equipment includes: the stress gauge acquiring the stress value of the risk feature point in real time during the excavation process; the processor obtaining the stress change by comparing the difference between the stress values ​​of the risk feature point before and after excavation; the processor determining the excavation disturbance range based on the stress change; and the processor predicting the deformation of the under-excavated rock mass based on the engineering volume of the under-excavated rock mass and the excavation disturbance range.

[0007] In one embodiment, prior to the step of identifying the location of the under-excavated rock mass within the tunnel, the tunnel under-excavated rock mass excavation method further includes: using a convolutional neural network model to identify the location of the under-excavated rock mass.

[0008] In one embodiment, the step of identifying the location of the under-excavated rock mass within the tunnel includes: installing a laser emitting device within the tunnel; the laser emitting device emitting a laser beam toward the location of the under-excavated rock mass to identify its location.

[0009] In one embodiment, after the step of excavating the under-excavated rock mass, the tunnel under-excavated rock mass excavation method further includes: constructing a lining structure.

[0010] In one embodiment, after the step of constructing the lining structure, the tunnel under-excavation rock mass excavation method further includes: establishing a risk index actual model based on the deformation prediction results; fitting the risk index actual model with the digital twin model to determine high-risk points; and constructing a reinforcing support structure on the lining structure at the high-risk points.

[0011] In one embodiment, the step of excavating the tunnel and scanning the three-dimensional real-scene image inside the tunnel in real time during the excavation process includes: excavating the tunnel and cleaning the surface of the tunnel; setting up a three-dimensional laser scanner inside the tunnel to scan the three-dimensional real-scene image inside the tunnel to obtain the three-dimensional real-scene image.

[0012] In one embodiment, the step of establishing a three-dimensional real-scene model based on the real-time three-dimensional real-scene image, and geometrically intersecting the three-dimensional real-scene model and the digital twin model to obtain the engineering quantity of the under-excavated rock mass includes: obtaining point cloud data within the tunnel based on the three-dimensional real-scene image; organizing and registering the point cloud data; using three-dimensional modeling software to establish the three-dimensional real-scene model based on the organized and registered point cloud data; and geometrically intersecting the three-dimensional real-scene model and the digital twin model to obtain the engineering quantity of the under-excavated rock mass.

[0013] In one embodiment, after the step of excavating the under-excavated rock mass, the tunnel under-excavated rock mass excavation method further includes: repeatedly excavating the tunnel and scanning a three-dimensional real-scene image of the tunnel in real time during the excavation process, until the amount of work of the under-excavated rock mass is less than the allowable value.

[0014] The proposed method for excavating under-excavated rock mass in tunnels involves real-time scanning of 3D real-scene images within the tunnel during excavation. A 3D real-scene model is then built based on these images. By geometrically intersecting the 3D real-scene model with a digital twin model, the amount and location of the under-excavated rock mass within the tunnel under the current excavation state are obtained. The location of the under-excavated rock mass is then marked within the tunnel, facilitating secondary excavation and helping to precisely control excavation quality, ensuring the tunnel structure meets design requirements. Before tunnel excavation, a risk index prediction model is established by acquiring the geological location information of the tunnel rock mass. This model is then fitted with the digital twin model to predict risk characteristic points during tunnel excavation. Monitoring equipment is deployed at these risk characteristic points, and this equipment predicts deformation of the under-excavated rock mass during secondary excavation, thereby preventing safety hazards during secondary excavation and ensuring the safety of the under-excavated rock mass excavation construction. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0016] Figure 1 This is a schematic flowchart of an embodiment of the tunnel under-excavation rock mass excavation method provided by the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0020] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0021] During the excavation of tunnels with large cross sections, factors such as complex geological conditions of the rock mass or improper control of drill-and-blast parameters may lead to under-excavation of the tunnel. Due to the limited visibility inside the tunnel, it is difficult for surveyors to align their foresight and backsight points when conducting measurements inside the tunnel. This makes it difficult to accurately determine parameters such as the amount of under-excavation work and the location of under-excavation in the under-excavated rock mass. The secondary excavation of the under-excavated rock mass is more difficult, and for tunnels with complex geological conditions, secondary excavation of the under-excavated rock mass can easily cause rock mass instability and pose safety hazards.

[0022] This invention proposes a method for excavating under-excavated rock mass in tunnels, comprising the following steps:

[0023] S10: Conduct geological surveys on the tunnel rock mass to obtain stratigraphic location information;

[0024] S20: Establish a digital twin model of the tunnel based on the design drawings;

[0025] S30: Establish a risk index prediction model based on the stratigraphic location information;

[0026] S40: Fit the risk index prediction model to the digital twin model to determine risk characteristic points;

[0027] S50: Excavate the tunnel and scan the three-dimensional real-scene image inside the tunnel in real time during the excavation process;

[0028] S60: Deploy monitoring equipment at the risk characteristic points;

[0029] S70: Based on the real-time three-dimensional real-scene image, establish a three-dimensional real-scene model, and perform geometric intersection between the three-dimensional real-scene model and the digital twin model to obtain the engineering quantity of the under-excavated rock mass;

[0030] S80: Mark the location of the under-excavated rock mass within the tunnel;

[0031] S90: Based on the monitoring results of the monitoring equipment, the deformation of the under-excavated rock mass is predicted;

[0032] S100: Excavate the under-excavated rock mass.

[0033] Please see Figure 1 Before tunnel excavation, a geological survey is conducted on the rock mass within the excavation area to obtain stratigraphic location information. A digital twin model of the tunnel excavation is established, and a risk index prediction model is built based on the stratigraphic location information. The digital twin model and the risk index prediction model are then fitted together. Based on the characteristics of different geological conditions and the location information under multiple stratigraphic combinations, the location with the greatest risk during tunnel excavation through the rock mass can be predicted. Risk characteristic points are identified in advance before excavation, facilitating focused monitoring of these points during the excavation process. During tunnel excavation, after completing a section of the tunnel, a 3D real-scene image of the tunnel is scanned promptly, and a 3D real-scene model of the current state is built based on the 3D real-scene image. A 3D reality model can reflect the current excavation status within the tunnel in real time. By geometrically intersecting the 3D reality model with the digital twin model, the discrepancy between the current excavation status and the tunnel's design status can be determined. The difference between the current excavation status and the tunnel's design status represents the amount of rock mass that is under-excavated. The locations where the discrepancies exist between the 3D reality model and the digital twin model indicate the location of the under-excavated rock mass. Finally, by marking the location of the under-excavated rock mass within the tunnel, secondary excavation of the under-excavated rock mass is facilitated. Before secondary excavation of the under-excavated rock mass, monitoring equipment is deployed at risk feature points. During the under-excavation process, the structural and geometric parameters of these risk feature points are monitored in real time to predict deformation during secondary excavation, thereby ensuring the safety of the under-excavated rock mass excavation.

[0034] The proposed method for excavating under-excavated rock mass in tunnels involves real-time scanning of 3D real-scene images within the tunnel during excavation. A 3D real-scene model is then built based on these images. By geometrically intersecting the 3D real-scene model with a digital twin model, the amount and location of the under-excavated rock mass within the tunnel under the current excavation state are obtained. The location of the under-excavated rock mass is then marked within the tunnel, facilitating secondary excavation and helping to precisely control excavation quality, ensuring the tunnel structure meets design requirements. Before tunnel excavation, a risk index prediction model is established by acquiring the geological location information of the tunnel rock mass. This model is then fitted with the digital twin model to predict risk characteristic points during tunnel excavation. Monitoring equipment is deployed at these risk characteristic points, and this equipment predicts deformation of the under-excavated rock mass during secondary excavation, thereby preventing safety hazards during secondary excavation and ensuring the safety of the under-excavated rock mass excavation construction.

[0035] In one embodiment, step S60 includes:

[0036] S61: Drill mounting holes at the risk feature points;

[0037] S62: Insert a stress gauge into the mounting hole;

[0038] S63: Connect the stress gauge to an external processor for communication.

[0039] It should be noted that risk characteristic points are identified within the excavated tunnel cross-section, and installation holes for stress gauges are drilled within the rock mass at these risk characteristic points. Once the stress gauges are inserted into the installation holes, they monitor the stress state within the rock mass by measuring stress changes. Each stress gauge is connected to an external processor to transmit real-time stress data from the rock mass. By installing stress gauges at risk characteristic points, the locations with the highest risk during tunnel excavation are monitored, and the deformation of the rock mass is predicted during secondary excavation of under-excavated rock masses, thus providing early warnings of potential risks during the excavation process.

[0040] In one embodiment, step S90 includes:

[0041] S91: The stress gauge acquires the stress value of the risk feature point in real time during the excavation process;

[0042] S92: The processor obtains the stress change by comparing the difference in stress values ​​of the risk feature points before and after excavation;

[0043] S93: The processor determines the excavation disturbance range based on the stress change;

[0044] S94: The processor predicts the deformation of the under-excavated rock mass based on the amount of work done and the range of excavation disturbance.

[0045] Furthermore, the stress gauge provides raw data for subsequent stress analysis and deformation prediction by monitoring stress changes at risk characteristic points in real time. The external processor calculates the difference (stress change) by comparing stress values ​​before and after excavation to determine the range of impact of excavation activities on rock mass stability, i.e., the excavation disturbance range. Finally, the processor combines the amount of work done on the under-excavated rock mass with the excavation disturbance range to predict potential deformations of the under-excavated rock mass. By monitoring stress values ​​in real time and calculating stress changes, the impact of excavation activities on rock mass stability can be assessed more accurately, thereby improving the accuracy of risk assessment. By predicting potential deformations of the under-excavated rock mass, preventative measures can be taken in advance to reduce safety risks during construction and ensure the safety of construction personnel and equipment.

[0046] In one embodiment, prior to step S80, the tunnel under-excavation rock mass excavation method further includes the following steps:

[0047] S79: Use a convolutional neural network model to identify the location of the under-excavated rock mass.

[0048] Understandably, by inputting 3D real-world images of the tunnel, the convolutional neural network model can automatically identify under-excavated areas, providing accurate information for subsequent identification of under-excavated rock mass locations. The convolutional neural network model can quickly process large amounts of image data, rapidly process real-time 3D real-world images, and improve the automation and accuracy of under-excavated rock mass identification, reducing human error and enhancing work efficiency and data reliability.

[0049] In one embodiment, step S80 includes:

[0050] S81: A laser emitting device is installed inside the tunnel;

[0051] S82: The laser emitting device emits a laser beam toward the location of the under-excavated rock mass to mark the location of the under-excavated rock mass.

[0052] It should be noted that the laser emitter communicates with an external processor. After the external processor obtains the location of the under-excavated rock mass, the laser emitter can emit a laser beam towards that location to mark the position of the under-excavated rock mass in the complex environment of the tunnel. This facilitates secondary excavation of the under-excavated rock mass by construction personnel. The precise location marking provided by the laser emitter reduces errors in locating the under-excavated rock mass by construction personnel, thus helping to improve the accuracy of excavation.

[0053] In one embodiment, after step S100, the tunnel under-excavation rock mass excavation method further includes the following steps:

[0054] S110: Construct lining structure.

[0055] Understandably, after the secondary excavation of the under-excavated rock mass in the tunnel is completed, a lining structure is immediately constructed inside the tunnel. The lining structure reinforces and protects the inner surface of the tunnel, reduces safety hazards caused by prolonged exposure of the rock mass, and provides a safer working environment for construction workers in subsequent construction.

[0056] In one embodiment, after step S110, the tunnel under-excavation rock mass excavation method further includes the following steps:

[0057] S120: Establish a practical model of the risk index based on the deformation prediction results;

[0058] S130: Fit the actual model of the risk index with the digital twin model to determine high-risk points;

[0059] S140: Construct a reinforcing support structure on the lining structure at the high-risk point.

[0060] It should be noted that the risk index prediction model is established based on geological location information and is used to predict risks during the excavation process before tunnel excavation. The actual risk index model is established based on monitoring results obtained from monitoring equipment deployed after tunnel excavation. It is used to conduct a secondary assessment of excavation risks based on the actual excavation conditions of the under-excavated rock mass and to identify high-risk points. By reassessing the risk based on the extent of disturbance to the rock mass during the secondary excavation of the under-excavated rock mass, it is easier to adjust and strengthen the tunnel lining support structure accordingly based on high-risk points, thereby increasing the bearing capacity and stability of high-risk locations to ensure the stability of the tunnel support structure. It can be noted that strengthening the support structure can employ methods such as denser reinforcement and thicker concrete.

[0061] In one embodiment, step S50 includes:

[0062] S51: Excavate the tunnel and clean the surface of the tunnel;

[0063] S52: A 3D laser scanner is installed inside the tunnel to scan the 3D real-world image inside the tunnel in order to obtain the 3D real-world image.

[0064] Understandably, after tunnel excavation, necessary cleaning of the tunnel surface is essential to ensure the cleanliness and safety of the tunnel interior, reduce errors during scanning, and ensure more accurate 3D reality images, providing high-quality foundational data for subsequent data analysis and processing. Installing a 3D laser scanner inside the tunnel allows for precise 3D scanning of the tunnel's interior space, generating 3D reality images of the tunnel. The 3D laser scanner enables rapid, real-time scanning, reducing the time and labor required by traditional measurement methods and improving construction efficiency.

[0065] In one embodiment, step S70 includes:

[0066] S71: Obtain point cloud data within the tunnel based on the three-dimensional real-scene image;

[0067] S72: Organize and register the point cloud data;

[0068] S73: Use 3D modeling software to build the 3D reality model based on the organized and registered point cloud data;

[0069] S74: Perform geometric intersection between the three-dimensional real-scene model and the data twin model to obtain the engineering quantity of the under-excavated rock mass.

[0070] Furthermore, point cloud data within the tunnel is acquired using a 3D laser scanner. This point cloud data contains the spatial coordinates of various points within the tunnel. The point cloud data is then processed through outlier removal, smoothing, data alignment, and correction to organize and register it. A 3D reality model is then built based on this organized and registered data, ensuring that the model closely matches the actual excavation conditions within the tunnel. An accurate 3D reality model can more realistically reflect the actual conditions inside the tunnel. Finally, the established 3D reality model is geometrically intersected with the tunnel's digital twin model to obtain the amount of under-excavated rock mass. This provides a precise basis for subsequent construction decisions, optimizes construction plans, and reduces material waste and additional excavation costs.

[0071] In one embodiment, after step S100, the tunnel under-excavation rock mass excavation method further includes:

[0072] S200: Repeat the steps of excavating the tunnel and scanning the three-dimensional real-scene image inside the tunnel in real time during the excavation process until the amount of the under-excavated rock mass is less than the allowable value.

[0073] To illustrate, the tunnel excavation process employs multiple iterations to ensure that the tunnel excavation meets design requirements. After each excavation, a 3D laser scanner is used to scan the 3D reality image inside the tunnel to obtain the latest point cloud data and 3D reality model. By geometrically intersecting with the digital twin model, the current amount of under-excavated rock mass can be assessed. This process is repeated until the amount of under-excavated rock mass is reduced to below the specified allowable value, ensuring that the tunnel excavation quality meets design and safety standards. Through multiple iterations of excavation and real-time scanning, the amount of under-excavated rock mass can be gradually reduced until the design requirements are met, further improving the accuracy of the excavation.

[0074] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for excavating under-excavated rock mass in tunnels, characterized in that, Including the following steps: Geological surveys were conducted on the tunnel rock mass to obtain stratigraphic location information; A digital twin model of the tunnel was created based on the design drawings; A risk index prediction model is established based on the aforementioned stratigraphic location information; By fitting the risk index prediction model with the digital twin model, risk characteristic points are determined; The tunnel is excavated, and three-dimensional real-scene images of the tunnel are scanned in real time during the excavation process; Monitoring equipment was deployed at the risk characteristic points; A three-dimensional real-scene model is established based on the real-time three-dimensional real-scene images. The three-dimensional real-scene model and the digital twin model are geometrically intersected to obtain the engineering quantity of the under-excavated rock mass. Mark the location of the under-excavated rock mass within the tunnel; Deformation prediction of the under-excavated rock mass is performed based on the monitoring results of the monitoring equipment. Establish a practical model for the risk index based on the deformation prediction results; By fitting the actual model of the risk index with the digital twin model, high-risk areas can be identified. Excavate the under-excavated rock mass.

2. The tunnel under-excavation rock mass excavation method as described in claim 1, characterized in that, The step of deploying detection equipment at the risk feature points includes: Drill mounting holes at the risk feature points; A stress gauge is inserted into the mounting hole; The stress gauge is connected to an external processor for communication.

3. The tunnel under-excavation rock mass excavation method as described in claim 2, characterized in that, The step of predicting the deformation of the under-excavated rock mass based on the monitoring results of the monitoring equipment includes: The stress gauge acquires the stress value of the risk feature point in real time during the excavation process; The processor obtains the stress change by comparing the difference in stress values ​​at the risk feature points before and after excavation. The processor determines the excavation disturbance range based on the stress change. The processor predicts the deformation of the under-excavated rock mass based on the amount of work done and the range of excavation disturbance.

4. The method for excavating under-excavated rock mass in tunnels as described in claim 1, characterized in that, Prior to the step of identifying the location of the under-excavated rock mass within the tunnel, the tunnel under-excavated rock mass excavation method further includes: The location of the under-excavated rock mass was identified using a convolutional neural network model.

5. The tunnel under-excavation rock mass excavation method as described in claim 4, characterized in that, The step of identifying the location of the under-excavated rock mass within the tunnel includes: A laser emitting device is installed inside the tunnel; The laser emitting device emits a laser beam toward the location of the under-excavated rock mass to mark its position.

6. The method for tunnel under-excavation rock mass excavation as described in any one of claims 1 to 5, characterized in that, Following the step of excavating the under-excavated rock mass, the tunnel under-excavated rock mass excavation method further includes: Construct the lining structure.

7. The tunnel under-excavation rock mass excavation method as described in claim 6, characterized in that, Following the step of constructing the lining structure, the tunnel under-excavation rock mass excavation method further includes: A reinforced support structure is constructed on the lining structure at the high-risk points.

8. The method for tunnel under-excavation rock mass excavation as described in any one of claims 1 to 5, characterized in that, The steps of excavating the tunnel and scanning the three-dimensional real-scene image inside the tunnel in real time during the excavation process include: Excavate the tunnel and clean its surface; A 3D laser scanner is installed inside the tunnel to scan the 3D real-world image inside the tunnel in order to obtain a 3D real-world image.

9. The method for excavating under-excavated rock mass in tunnels as described in claim 8, characterized in that, The steps of establishing a 3D real-scene model based on the real-time 3D real-scene image, and geometrically intersecting the 3D real-scene model and the digital twin model to obtain the engineering quantity of the under-excavated rock mass include: The point cloud data inside the tunnel is obtained based on the three-dimensional real-scene image; The point cloud data is organized and registered; The three-dimensional reality model is created using 3D modeling software based on the organized and registered point cloud data. The engineering quantity of the under-excavated rock mass is obtained by geometrically intersecting the three-dimensional real-scene model and the data twin model.

10. The method for tunnel under-excavation rock mass excavation as described in any one of claims 1 to 5, characterized in that, Following the step of excavating the under-excavated rock mass, the tunnel under-excavated rock mass excavation method further includes: The process of repeatedly excavating the tunnel and scanning the three-dimensional real-world image inside the tunnel in real time during the excavation is continued until the amount of the under-excavated rock mass is less than the allowable value.

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