Intelligent design method for support scheme of rock burst section in TBM tunnel

Abstract

An intelligent design method for a support scheme of a rock burst section in a TBM tunnel, relating to the field of tunnel engineering. The intelligent design method for the support scheme of the rock burst section in the TBM tunnel comprises: determining a surrounding rock class on the basis of TBM mainframe operation data, surrounding rock feedback evaluation, and a tunnel engineering geological survey report, and using a rock burst intensity assessment method to predict a rock burst grade; and on the basis of the surrounding rock class and the rock burst grade, using a computer deep learning technology to determine an initial surrounding rock support method. The method effectively avoids the impact of subjective factors, actively utilizes TBM mainframe operation data and surrounding rock feedback evaluation, utilizes big data processing and an artificial intelligence technology to propose an intelligently designed support scheme for a rock burst section in a TBM tunnel, and can provide an efficient support scheme for field construction workers for a rock burst section in a tunnel.

Description

An intelligent design method for support schemes in rockburst sections of TBM tunnels Technical Field

[0001] The present application relates to the field of tunnel engineering, and specifically to an intelligent design method for a support scheme for a rockburst section of a TBM tunnel. Background Art

[0002] Tunnels, as engineering structures along transportation routes, offer significant social and economic benefits. Tunnel construction is a crucial component of every country's development. With the rapid development of the national economy, my country's underground resource development and infrastructure development are rapidly extending to deeper locations. Rockbursts occur occasionally in tunnels such as the Erlangshan Tunnel on the Sichuan-Tibet Highway, the Qinling Railway Tunnel, and the Sichuan-Tibet Railway. This is because the geological environment in which the rock mass resides becomes more complex with increasing burial depth and ground stress levels, making rockburst hazards caused by excavation or mining more prominent and severe. This presents unprecedented challenges to the design, construction, and production of deep underground projects. Timely and effective tunnel support solutions can prevent and control rockbursts, playing a decisive role in tunnel safety and long-term stability.

[0003] TBM (Transport-Borne Machine) tunneling is widely used in deep hard rock tunneling. However, the selection of TBM tunnel support schemes is primarily based on the experience of designers and construction technicians and engineering analogies. This leads to problems such as severe subjective influence and low design efficiency. With increasing demands for efficiency, quality, and safety, and the need for effective rockburst prevention and control, manual experience and engineering analogies are no longer sufficient for the development of tunnel support. Therefore, an intelligent design method for rockburst section support schemes in TBM tunnels is urgently needed.

[0004] Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an intelligent design method for a support scheme for a rock burst section of a TBM tunnel, which can provide on-site construction personnel with an efficient support scheme for the rock burst section of the tunnel.

[0006] An intelligent design method for a rockburst support scheme in a TBM tunnel comprises the following steps:

[0007] Step 1: Based on TBM operating data, surrounding rock feedback evaluation, tunnel engineering geological survey reports, and tunnel area topographic and geological maps, basic surrounding rock parameters are obtained to determine the surrounding rock classification for the initial support tunnel section and the stress distribution of the surrounding rock during tunnel excavation. Rockburst intensity assessment methods are then used to predict rockburst levels at different radial locations along the tunnel's trajectory.

[0008] Step 1.1: Based on TBM host operation data, surrounding rock feedback evaluation, tunnel engineering geological survey report, and tunnel area topography and geological map, obtain basic surrounding rock parameters. Based on these basic surrounding rock parameters, determine the surrounding rock classification for the tunnel section.

[0009] The basic parameters of surrounding rock mainly include burial depth, stratum lithology, surrounding rock classification, hydrogeological conditions, uniaxial compressive strength of rock, tunnel cross-section shape, and distribution of surrounding rock structural surfaces near the tunneling face;

[0010] Step 1.2: Based on the tunnel engineering geological survey report and the topographic and geological map of the tunnel area, numerical simulation is used to invert the initial in-situ stress of the tunnel rock and the stress field distribution caused by excavation, thereby determining the stress distribution of the surrounding rock during tunnel excavation.

[0011] Step 1.3: Based on the basic parameters of the surrounding rock and the stress distribution of the surrounding rock during tunnel excavation, a comprehensive judgment table of rockburst intensity at different radial positions along the tunnel direction is drawn to comprehensively analyze and predict the rockburst level.

[0012] The rockburst levels specifically include no rockburst, slight rockburst, moderate rockburst and severe rockburst;

[0013] Step 2: Based on the surrounding rock classification and rockburst level, use computer deep learning technology to determine the initial support plan for the surrounding rock of the TBM tunnel;

[0014] Step 2.1: Construct a sample library that includes surrounding rock classification, rockburst grade, and corresponding initial support schemes for surrounding rock of completed or under-construction TBM tunnels.

[0015] Step 2.2: Construct a TBM tunnel initial support scheme selection model based on a neural network, and substitute the sample library into the TBM tunnel initial support scheme selection model for training;

[0016] Step 2.3: Input the surrounding rock classification and rockburst grade of the tunnel to be built into the trained TBM tunnel initial support scheme selection model to obtain the corresponding TBM tunnel initial support scheme.

[0017] The useful effects of the present invention are:

[0018] The present invention provides an intelligent design method for a support scheme for a rockburst section of a TBM tunnel. The intelligent design method for a support scheme for a rockburst section of a TBM tunnel is based on TBM host operation data, surrounding rock feedback evaluation, and a tunnel engineering geological survey report to determine the surrounding rock classification, and use a rockburst intensity assessment method to predict the rockburst grade. Based on the surrounding rock classification and rockburst grade, the method uses computer deep learning technology to determine the initial surrounding rock support method. The method effectively avoids the influence of subjective factors, actively utilizes TBM host operation data and surrounding rock feedback evaluation, combines big data processing and artificial intelligence technology, and proposes an intelligent design support scheme for the rockburst section of a TBM tunnel. The method can provide on-site construction personnel with an efficient support scheme for the rockburst section of the tunnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG1 is a flow chart of an intelligent design method for a rockburst support scheme for a TBM tunnel provided by an embodiment of the present invention;

[0020] FIG2 is a schematic diagram of a monitoring page of a TBM host at a certain excavation moment of a TBM tunnel provided by an embodiment of the present invention;

[0021] FIG3 is a schematic diagram of a TBM tunnel initial support scheme selection model based on an existing TBM tunnel initial support scheme library using deep neural network technology, provided by an embodiment of the present invention;

[0022] FIG4 is a schematic diagram of a plane expansion of a rockburst level prediction for a TBM tunnel provided by an embodiment of the present invention;

[0023] FIG5 is a flowchart of the selection of the initial support scheme for the surrounding rock of a TBM tunnel provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments;

[0025] An intelligent design method for a rockburst support scheme in a TBM tunnel, as shown in FIG1 , specifically includes the following steps:

[0026] Step 1: Based on TBM operating data, surrounding rock feedback evaluation, tunnel engineering geological survey reports, and tunnel area topographic and geological maps, basic surrounding rock parameters are obtained to determine the surrounding rock classification for the initial support tunnel section and the stress distribution of the surrounding rock during tunnel excavation. Rockburst intensity assessment methods are then used to predict rockburst levels at different radial locations along the tunnel's trajectory.

[0027] Step 1.1: Based on TBM host operation data, surrounding rock feedback evaluation, tunnel engineering geological survey reports, and tunnel area topographic and geological maps, obtain basic surrounding rock parameters. These parameters mainly include burial depth, stratum lithology, surrounding rock classification, hydrogeological conditions, uniaxial compressive strength of rock, tunnel cross-section shape, and distribution of surrounding rock structural surfaces near the tunnel face. Based on these basic surrounding rock parameters, determine the surrounding rock classification for the tunnel section.

[0028] It should be noted that the TBM host operating data and surrounding rock feedback evaluation reflect the real-time status of the TBM host and surrounding rock during tunnel excavation. Figure 2 shows a schematic diagram of the TBM host monitoring page at a certain moment of tunnel excavation in a TBM tunnel. It reflects the TBM host status (TBM excavation footage data, cutterhead torque, total thrust, penetration, excavation speed, pressure and displacement of the left gripper, gripper, top shield, left shield, and right shield) and the tunnel face rock mass evaluation. The tunnel engineering geological survey report reflects the surrounding rock status during the tunnel design stage. The former results should be given priority, and the latter results should be supplemented.

[0029] Step 1.2: Based on the tunnel engineering geological survey report and the topographic and geological map of the tunnel area, numerical simulation is used to invert the initial in-situ stress of the tunnel rock and the distribution of the excavation disturbance stress field, and determine the stress distribution of the surrounding rock during the tunnel excavation project.

[0030] Step 1.3: Based on the basic parameters of the surrounding rock and the stress distribution of the surrounding rock during tunnel excavation, a comprehensive judgment table of rockburst intensity at different radial positions along the tunnel direction is drawn. The rockburst level is comprehensively analyzed and predicted. The rockburst level includes no rockburst, slight rockburst, moderate rockburst, and severe rockburst.

[0031] Because the stress distribution of the surrounding rock during tunnel excavation is often asymmetric, stress concentration occurs in certain areas of the surrounding rock. Predicted rockbursts often occur in these areas. Therefore, the stress distribution of the surrounding rock during tunnel excavation is used to determine the location of stress concentration, and accordingly, more effective support measures are required at these locations. Therefore, the predicted rockburst level not only indicates the rockburst level of a specific section, but also indicates the stress concentration area at a specific location in the surrounding rock, as shown in Figure 4.

[0032] Based on rockburst prediction theory, domestic and international experts have proposed various rockburst severity prediction methods. Based on the tunnel's characteristics, an appropriate rockburst severity prediction method is selected to predict the tunnel's rockburst severity. Currently, the main rockburst severity prediction methods include engineering geological analysis, rock mechanics criteria, RVI index, neural networks, and rockburst microseismic early warning. In this implementation case, a tunnel was used as an example. Four criteria applicable to the tunnel were selected, as shown below. A comprehensive rockburst severity judgment table was then developed to comprehensively predict the surrounding rockburst severity. If the four rockburst severity prediction results for a particular section of the tunnel disagree, the majority prediction will be used as the final prediction.

[0033] 1) burial depth;

[0034] 2) surrounding rock grade;

[0035] 3) Rock stress intensity ratio method σ θ / σ c , where σ θ Maximum tangential stress around the excavation face (MPa, and saturated uniaxial compressive strength of rock (MPa);

[0036] 4) Rock strength stress ratio method σ c / σ max , where σ c is the saturated uniaxial compressive strength of rock (MPa), σ max is the maximum in-situ stress of the surrounding rock (MPa);

[0037] Step 2: Based on the surrounding rock classification and rockburst level, use computer deep learning technology to determine the initial support plan for the surrounding rock of the TBM tunnel.

[0038] Neural networks are a newly emerging discipline, inspired by the widespread adoption of computers. They are a complex computational method that simulates the structure of neurons and their connections, inspired by the human nervous system. Neural network technology is primarily designed based on the human neural workflow, utilizing the way humans process information. Neural networks do not require a predefined mathematical equation for the mapping between input and output. Instead, they train themselves to learn certain rules and generate the closest possible output for a given input.

[0039] A classic neural network model usually consists of an input layer, a hidden layer, and an output layer. The layers are fully interconnected, and the nodes in each layer are not connected. There can be multiple hidden layers. The neural network is reshaped by continuous self-deduction and then the final result is obtained. With the research and application of neural networks by experts and scholars at home and abroad, there are neural network models with various structures, such as BP neural network, convolutional neural network, recurrent neural network, RBF neural network, etc. In order to achieve the purpose of intelligent design of the support scheme for the rock burst section of TBM tunnel, a neural network structure suitable for the on-site tunnel can be selected. This application takes the following neural network structure as an example.

[0040] Step 2.1: Construct a sample library, which includes surrounding rock classification, rockburst grade, and corresponding surrounding rock initial support schemes for completed or under-construction TBM tunnels.

[0041] The corresponding characteristics of a tunnel are shown in the following table:

[0042] Step 2.2: Construct a TBM tunnel initial support scheme selection model based on a neural network, and substitute the sample library into the TBM tunnel initial support scheme selection model for training.

[0043] The deep neural network used in the model for selecting the initial support scheme for TBM tunnels is set to five layers: an input layer, three hidden layers, and an output layer. The input layer corresponds to two parameters: surrounding rock classification and rockburst severity. These parameters are selected based on their characteristics. For example, there are six types of surrounding rock classification: I, II, III, IV, V, and VI, and four types of rockburst: no rockburst, mild rockburst, moderate rockburst, and severe rockburst, resulting in two nodes. The output layer corresponds to the initial support scheme for TBM tunnels, resulting in one node. The number of nodes in the hidden layer is determined through research; it is generally recommended that the number of nodes be less than twice the number of characteristic items (the number of input layer nodes). A tangent function or a logarithmic function is used as the transfer function (activation function of the hidden layer), as shown in Figure 3.

[0044] After the neural network is established, a large number of sample libraries are input to train the deep learning artificial network selection model until convergence and the set error standard are met. The trained deep learning artificial network selection model is obtained, which is a usable TBM tunnel initial support scheme selection model.

[0045] The TBM tunnel initial support scheme selection model is trained with extensive sample library data and features self-learning capabilities. As more sample library data is generated during construction, its accuracy continues to improve, resulting in a high degree of accuracy and intelligence. During implementation, the sample library will contain no fewer than 100 sets of data.

[0046] Step 2.3: Input the surrounding rock classification and rockburst level of the tunnel to be built into the trained TBM tunnel initial support scheme selection model to obtain the corresponding TBM tunnel initial support scheme, as shown in Figure 5.

[0047] The initial support scheme for the surrounding rock stress concentration area in the initial support scheme of TBM tunnels needs to be optimized to a more effective support method, such as shortening the spacing between steel bars and arranging anchor bolts densely, so as to better prevent the occurrence of rock bursts. In the initial support scheme selection model for TBM tunnels, it can be understood that the surrounding rock classification, rock burst level, and initial support method of the surrounding rock stress concentration area of ​​the tunnel section are used as inputs, and the initial support scheme for the surrounding rock stress concentration area is used as the output node. The initial support scheme for the surrounding rock stress concentration area is obtained through training as the output node. For example, the initial support measures adopted in a tunnel section with a surrounding rock of medium rock burst level III are as follows:

[0048] The initial support measures for the surrounding rock stress concentration area of ​​this tunnel section should be optimized as shown in the following table:

Claims

1. An intelligent design method for the support scheme of the rock burst section of a TBM tunnel, characterized in that, Specifically, it includes the following steps: Step 1: Based on the TBM main machine operation data, surrounding rock feedback evaluation, tunnel engineering geological investigation report, and tunnel area topographic geological map, obtain the basic parameters of the surrounding rock, determine the surrounding rock classification of the initial support tunnel section and the stress distribution of the surrounding rock during tunnel excavation, and then use the rockburst intensity evaluation method to predict the rockburst grades at different radial positions in different positions along the tunnel alignment; Step 2: Based on the surrounding rock classification and rockburst grades, use computer deep learning technology to determine the initial support scheme for the TBM tunnel surrounding rock.

2. The intelligent design method for the support scheme of the rock burst section of the TBM tunnel according to claim 1, wherein Specifically, Step 1 is as follows: Step 1.1: Based on the TBM main machine operation data, surrounding rock feedback evaluation, tunnel engineering geological investigation report, and tunnel area topographic geological map, obtain the basic parameters of the surrounding rock, and determine the surrounding rock classification of this tunnel section according to the basic parameters of the surrounding rock; Step 1.2: Based on the tunnel engineering geological investigation report and the tunnel area topographic geological map, inversely calculate the initial in-situ stress of the tunnel's original rock and the distribution of the excavation disturbance stress field through numerical simulation methods to determine the stress distribution of the surrounding rock during tunnel excavation; Step 1.3: According to the basic parameters of the surrounding rock and the stress distribution of the surrounding rock during tunnel excavation, draw a comprehensive judgment table of the tunnel rockburst intensity at different radial positions in different positions along the tunnel alignment, and comprehensively analyze and predict the rockburst grades.

3. The intelligent design method for the support scheme of the TBM tunnel rock burst section according to claim 2, characterized in that, The basic parameters of the surrounding rock mainly include burial depth, formation lithology, surrounding rock classification, hydrogeological conditions, uniaxial compressive strength of rock, tunnel cross-section shape, and the distribution of surrounding rock structural planes near the tunneling face.

4. The intelligent design method for the support plan of the TBM tunnel rockburst section according to claim 2, wherein, The specific rockburst grades include no rockburst, slight rockburst, moderate rockburst, and intense rockburst.

5. The intelligent design method for the support scheme of the rock burst section of the TBM tunnel according to claim 1, characterized in that, Specifically, Step 2 is as follows: Step 2.1: Construct a sample library; Step 2.2: Based on a neural network, construct a model for selecting the initial support scheme for the TBM tunnel, and substitute the sample library into the model for selecting the initial support scheme for the TBM tunnel for training; Step 2.3: Obtain the corresponding initial support scheme for the TBM tunnel.

6. The intelligent design method for the support scheme of the TBM tunnel rock burst section according to claim 5, characterized in that, The sample library includes the surrounding rock classification, rockburst grades, and the corresponding initial support schemes for the surrounding rock of the completed or under-construction TBM tunnels.

7. An intelligent design method for the support scheme of the rock burst section of a TBM tunnel according to claim 5, characterized in that, Specifically, Step 2.3 is as follows: Input the surrounding rock classification and rockburst grades of the tunnel to be built into the trained model for selecting the initial support scheme for the TBM tunnel to obtain the corresponding initial support scheme for the TBM tunnel.

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