Tunnel excavation support management method and system based on advanced geological prediction information

Abstract

The present invention relates to the technical field of tunnel excavation support, and provides a tunnel excavation support management method and system based on advanced geological prediction information, the method comprising: obtaining geological prediction information of tunnel excavation space, and extracting geological parameter data of tunnel face; optimizing tunnel excavation construction parameter data in combination with the geological parameter data of tunnel face; and implementing tunnel excavation support according to the optimized tunnel excavation construction parameter data. The present invention optimizes tunnel excavation construction parameter data based on the geological prediction information of tunnel excavation space and according to the analysis of the extracted geological parameter data of tunnel face, and implements tunnel excavation support according to the optimized tunnel excavation construction parameter data, thereby improving the safety of tunnel excavation support and ensuring the efficiency and quality of tunnel construction.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel excavation support, and in particular to a tunnel excavation support management method and system based on advanced geological prediction information. Background Art

[0002] During the construction of tunnel projects, the geological structure is complex, the excavation depth is large, and the construction speed is fast, making safety monitoring during the construction process a top priority. However, in actual monitoring, the data is updated quickly, the data collection and storage methods are relatively chaotic, the degree of visualization is low, and the utilization efficiency of monitoring data is low. When risks and disasters occur, it is necessary to search on the drawings according to the name and number of the measuring point and then go to the site for further treatment, and the efficiency of collaborative management is relatively low.

[0003] Tunnel construction control is reflected in the process control of geological feedforward and monitoring feedback. Existing tunnel construction requires accurate grasp of the geology in front of the tunnel face, comprehensive identification based on the geological conditions of the face, optimization of construction parameters based on actual geological conditions, and then excavation and support based on the adjusted construction parameters. Finally, combined with monitoring and measurement data, the deformation is analyzed to see if it is stable and controllable, and support parameters are evaluated, adjusted, and optimized based on the situation, so as to achieve safe and orderly tunnel construction. However, in the current construction, there are problems such as delayed forecasting, untimely warning and handling of geological anomalies, untimely monitoring, and data falsification, which lead to process out of control and hidden dangers to tunnel safety.

[0004] Therefore, it is necessary to provide a tunnel excavation support management method and system based on advanced geological prediction information. Summary of the invention

[0005] The present invention provides a tunnel excavation support management method and system based on advanced geological prediction information. By optimizing tunnel excavation construction parameter data based on geological prediction information of the tunnel excavation space and according to the analysis of extracted tunnel face geological parameter data, tunnel excavation support is implemented according to the optimized tunnel excavation construction parameter data, thereby improving the safety of tunnel excavation support and ensuring the efficiency and quality of tunnel construction.

[0006] The present invention provides a tunnel excavation support management method based on advanced geological prediction information, comprising:

[0007] Obtain geological prediction information of the tunnel excavation space and extract geological parameter data of the tunnel face;

[0008] Combined with the geological parameter data of the tunnel face, the tunnel excavation construction parameter data is optimized;

[0009] Tunnel excavation support is implemented according to the optimized tunnel excavation construction parameter data.

[0010] Furthermore, geological prediction information of the tunnel excavation space is obtained, including:

[0011] Collect and obtain real-time geological status data based on the deployed geological forecast information collection device;

[0012] Based on the tunnel geological structure model constructed by BIM technology, the tunnel geological parameter data is obtained;

[0013] Based on digital twin technology, a digital twin platform for tunnel excavation space is constructed according to real-time geological status data and tunnel geological parameter data;

[0014] Based on the digital twin platform of tunnel excavation space, the geological prediction information of tunnel excavation space is obtained.

[0015] Furthermore, the real-time geological status data is collected and acquired based on the geological prediction information collection device deployed, including:

[0016] Performing data collection of manual collection items based on the deployed distributed collection devices to obtain first category item data; the distributed collection devices include but are not limited to point load meters and sonic wave testers;

[0017] Performing automatic data collection at a set period based on the deployed automatic collection device to obtain the second category item data; the automatic collection device includes but is not limited to a thermometer, a hygrometer, a pressure sensor and a displacement sensor;

[0018] The real-time geological status data is composed based on the first category item data and the second category item data.

[0019] Furthermore, the geological parameter data of the tunnel face are extracted, including:

[0020] According to the tunnel geological parameter data, the geological characteristic data of the tunnel face to be excavated are obtained; the geological characteristic data include but are not limited to the joints, cracks, degree of fragmentation, degree of roughness, degree of smoothness, mud-rock and water gushing data of the surrounding rock;

[0021] Using the set data extraction template, extract the surrounding rock strength data and rock integrity data of several tunnel faces to be excavated;

[0022] The geological parameter data of the tunnel face are composed based on the geological characteristic data, surrounding rock strength data and rock integrity data.

[0023] Furthermore, combined with the geological parameter data of the tunnel face, the tunnel excavation construction parameter data is optimized, including:

[0024] Build a CNN model, add LSTM layer and attention mechanism to the CNN model to get the CNN-LSTM model;

[0025] Based on the CNN-LSTM model, feature extraction and classification are performed on the geological parameter data of the tunnel face to obtain classified data;

[0026] Based on the classified data, the RBF neural network model is used to predict the changes in the geological parameters of the tunnel face and obtain the prediction results of the changes in the geological parameters of the tunnel face;

[0027] According to the prediction results of the geological parameter changes of the tunnel face, the current construction parameters of the tunnel excavation are optimized to obtain the optimized construction parameter data.

[0028] Furthermore, according to the prediction results of the geological parameter changes of the tunnel face, the construction parameters of the tunnel excavation are optimized to obtain the optimized construction parameter data, including:

[0029] Based on the tunnel excavation space digital twin platform, the current construction parameters of the tunnel excavation are obtained;

[0030] Based on the current construction parameters, the corresponding geological parameters of the first tunnel face are obtained according to the correspondence relationship library between the set construction parameters and the geological parameters of the tunnel face;

[0031] The geological parameters of the first tunnel face are compared and analyzed with the prediction results of the changes in the geological parameters of the tunnel face to obtain the change difference of the geological parameters of the tunnel face. If the change difference is greater than the set difference threshold, the first construction parameters are matched based on the geological parameters of the tunnel face in the prediction results of the geological parameters of the tunnel face and relying on the matching correspondence library between the geological parameters of the tunnel face and the construction parameters. The current construction parameters are optimized according to the first construction parameters to obtain the optimized tunnel excavation construction parameter data.

[0032] Further, tunnel excavation support is implemented according to the optimized tunnel excavation construction parameter data, including:

[0033] Carry out tunnel excavation operations according to the optimized tunnel excavation construction parameter data;

[0034] Based on the set tunnel structure deformation monitoring model, the tunnel structure deformation generated during the tunnel excavation operation is monitored to obtain the monitoring results;

[0035] The monitoring results are evaluated to see whether the risks are controllable. If the evaluation result shows that the risks are uncontrollable, the set support parameters are adjusted and the adjusted support parameters are used for support during tunnel excavation operations.

[0036] Furthermore, based on the set tunnel structure deformation monitoring model, the tunnel structure deformation generated during the tunnel excavation operation is monitored to obtain monitoring results, including:

[0037] Configure monitoring devices, including but not limited to ultrasonic flaw detectors and pressure gauges;

[0038] The monitoring device is used to obtain the tunnel structure change data, and based on the set wireless signal transmission network, the tunnel structure change data is sent to the tunnel excavation space digital twin platform;

[0039] Based on the normal deformation data set of the tunnel structure set in the tunnel excavation space digital twin platform, deformation deduction is performed using the set tunnel structure deformation deduction model to obtain deformation deduction data;

[0040] Compare and analyze the deformation deduction data with the tunnel structure change data to obtain deformation change difference data, deformation rate difference data, and influence degree data on deformation associated data; the deformation associated data includes tunnel structure parameter data associated with the deformation;

[0041] The deformation change difference data and the deformation rate difference data are weighted and summed to obtain the deformation comprehensive change data;

[0042] The tunnel structure deformation monitoring model is used for monitoring to obtain monitoring results on the comprehensive change data of deformation and the degree of impact data.

[0043] Furthermore, the monitoring results are evaluated to see whether the risk is controllable. If the evaluation result shows that the risk is uncontrollable, the set support parameters are adjusted and the adjusted support parameters are used for support during the tunnel excavation operation, including:

[0044] Based on the set first risk prediction model and the comprehensive deformation change data, a basic risk prediction is performed on the tunnel support risk to obtain a basic risk prediction value. If the basic risk prediction value is greater than the set first risk prediction threshold, the risk is assessed to be uncontrollable.

[0045] If the basic risk prediction value is less than the set first risk prediction threshold, then based on the set second risk prediction model, according to the deformation comprehensive change data and the impact degree data, an additional risk prediction is performed on the tunnel support risk to obtain an additional risk prediction value. If the additional risk prediction value is greater than the set second risk prediction threshold, the risk is assessed to be uncontrollable.

[0046] According to the assessment results of uncontrollable risks, the set support parameters are adjusted and the adjusted support parameters are used for support during tunnel excavation operations.

[0047] Tunnel excavation support management system based on advanced geological prediction information includes:

[0048] The tunnel face geological parameter data acquisition module is used to obtain geological prediction information of the tunnel excavation space and extract the tunnel face geological parameter data;

[0049] The construction parameter data optimization module is used to optimize the tunnel excavation construction parameter data by combining the tunnel face geological parameter data;

[0050] The tunnel excavation support implementation module is used to implement tunnel excavation support according to the optimized tunnel excavation construction parameter data.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects: by optimizing tunnel excavation construction parameter data based on geological prediction information of tunnel excavation space and according to the analysis of extracted tunnel face geological parameter data, and implementing tunnel excavation support according to the optimized tunnel excavation construction parameter data, the safety of tunnel excavation support can be improved, and the efficiency and quality of tunnel construction can be ensured.

[0052] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0053] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0055] Figure 1 A schematic diagram of the steps of the tunnel excavation support management method based on advanced geological prediction information;

[0056] Figure 2 A schematic diagram of the method steps for obtaining geological prediction information of tunnel excavation space;

[0057] Figure 3 This is a schematic diagram of the structure of the tunnel excavation support management system based on advanced geological prediction information. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0059] The present invention provides a tunnel excavation support management method based on advanced geological prediction information, such as Figure 1 As shown, including:

[0060] Obtain geological prediction information of the tunnel excavation space and extract geological parameter data of the tunnel face;

[0061] Combined with the geological parameter data of the tunnel face, the tunnel excavation construction parameter data is optimized;

[0062] Tunnel excavation support is implemented according to the optimized tunnel excavation construction parameter data.

[0063] The working principle of the above technical solution is: in order to realize the tunnel excavation support management method based on advanced geological prediction information, the present invention first obtains the geological prediction information of the tunnel excavation space and extracts the geological parameter data of the tunnel face; then combines the geological parameter data of the tunnel face to optimize the tunnel excavation construction parameter data; finally, implements tunnel excavation support according to the optimized tunnel excavation construction parameter data.

[0064] The beneficial effect of the above technical scheme is: by adopting the scheme provided in this embodiment, by optimizing the tunnel excavation construction parameter data based on the geological prediction information of the tunnel excavation space and according to the analysis of the extracted tunnel face geological parameter data, and implementing tunnel excavation support according to the optimized tunnel excavation construction parameter data, the safety of tunnel excavation support can be improved, and the efficiency and quality of tunnel construction can be ensured.

[0065] In one embodiment, Figure 2 As shown, the geological prediction information of the tunnel excavation space is obtained, including:

[0066] Collect and obtain real-time geological status data based on the deployed geological forecast information collection device;

[0067] Based on the tunnel geological structure model constructed by BIM technology, the tunnel geological parameter data is obtained;

[0068] Based on digital twin technology, a digital twin platform for tunnel excavation space is constructed according to real-time geological status data and tunnel geological parameter data;

[0069] Based on the digital twin platform of tunnel excavation space, the geological prediction information of tunnel excavation space is obtained.

[0070] The working principle of the above technical solution is: in order to obtain the geological prediction information of the tunnel excavation space more accurately, the present invention first collects and obtains real-time geological status data based on the deployed geological prediction information acquisition device; then obtains tunnel geological parameter data based on the tunnel geological structure model constructed based on BIM technology; finally, based on the digital twin technology, according to the real-time geological status data and tunnel geological parameter data, a tunnel excavation space digital twin platform is constructed; based on the tunnel excavation space digital twin platform, the geological prediction information of the tunnel excavation space is obtained.

[0071] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, by building a digital twin platform of the tunnel excavation space based on real-time geological status data and tunnel geological parameter data, the accuracy of geological prediction information acquisition of the tunnel excavation space can be guaranteed.

[0072] In one embodiment, the real-time geological status data is collected and acquired based on the geological prediction information collection device deployed, including:

[0073] Performing data collection of manual collection items based on the deployed distributed collection devices to obtain first category item data; the distributed collection devices include but are not limited to point load meters and sonic wave testers;

[0074] Performing automatic data collection at a set period based on the deployed automatic collection device to obtain the second category item data; the automatic collection device includes but is not limited to a thermometer, a hygrometer, a pressure sensor and a displacement sensor;

[0075] The real-time geological status data is composed based on the first category item data and the second category item data.

[0076] The working principle of the above technical solution is: in order to obtain real-time geological status data, the present invention first performs manual data collection based on the deployed distributed acquisition devices to obtain the first category of data; the distributed acquisition devices include but are not limited to point load meters and acoustic wave testers; then, based on the deployed automatic acquisition devices, automatic data collection of a set period is performed to obtain the second category of data; the automatic acquisition devices include but are not limited to thermometers, hygrometers, pressure sensors and displacement sensors; finally, the real-time geological status data is composed according to the first category of data and the second category of data.

[0077] The beneficial effect of the above technical solution is: by adopting the solution provided by this embodiment, the accuracy of real-time geological status data can be guaranteed by collecting data according to different collection devices.

[0078] In one embodiment, extracting geological parameter data of the tunnel face includes:

[0079] According to the tunnel geological parameter data, the geological characteristic data of the tunnel face to be excavated are obtained; the geological characteristic data include but are not limited to the joints, cracks, degree of fragmentation, degree of roughness, degree of smoothness, mud-rock and water gushing data of the surrounding rock;

[0080] Using the set data extraction template, extract the surrounding rock strength data and rock integrity data of several tunnel faces to be excavated;

[0081] The geological parameter data of the tunnel face are composed based on the geological characteristic data, surrounding rock strength data and rock integrity data.

[0082] The working principle of the above technical solution is as follows: in order to extract the geological parameter data of the tunnel face, the present invention first obtains the geological characteristic data of the tunnel face to be excavated according to the tunnel geological parameter data; the geological characteristic data includes but is not limited to the joints, cracks, degree of fragmentation, roughness, smoothness, mud-rock and water gushing data of the surrounding rock; then, the set data extraction template is used to extract the surrounding rock strength data and rock integrity data of several tunnel faces to be excavated; finally, the geological parameter data of the tunnel face is composed according to the geological characteristic data, surrounding rock strength data and rock integrity data.

[0083] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, by obtaining the geological characteristic data, surrounding rock strength data and rock integrity data of the tunnel face, it is possible to ensure that comprehensive and accurate geological parameter data of the tunnel face are obtained.

[0084] In one embodiment, the tunnel excavation construction parameter data is optimized in combination with the tunnel face geological parameter data, including:

[0085] Build a CNN model, add LSTM layer and attention mechanism to the CNN model to get the CNN-LSTM model;

[0086] Based on the CNN-LSTM model, feature extraction and classification are performed on the geological parameter data of the tunnel face to obtain classified data;

[0087] Based on the classified data, the RBF neural network model is used to predict the changes in the geological parameters of the tunnel face and obtain the prediction results of the changes in the geological parameters of the tunnel face;

[0088] According to the prediction results of the geological parameter changes of the tunnel face, the current construction parameters of the tunnel excavation are optimized to obtain the optimized construction parameter data.

[0089] The working principle of the above technical solution is as follows: in order to combine the geological parameter data of the tunnel face and optimize the tunnel excavation construction parameter data, the present invention first establishes a CNN model, which is a convolutional neural network model, and is a very common model in deep learning. It is widely used in image processing; an LSTM layer and an attention mechanism are added to the CNN model to obtain a CNN-LSTM model; the attention mechanism is based on human visual attention. When observing an object, people tend to focus on some features. The attention mechanism is based on this feature and our goal to give a greater weight to strong features and a smaller weight or even 0 weight to weak features; the CNN-LSTM model is then used to extract and classify the geological parameter data of the tunnel face to obtain classified data; then, based on the classified data, the RBF neural network model is used to predict the changes in the geological parameters of the tunnel face to obtain the prediction results of the changes in the geological parameters of the tunnel face; finally, according to the prediction results of the changes in the geological parameters of the tunnel face, the current construction parameters of the tunnel excavation are optimized to obtain the optimized construction parameter data.

[0090] The beneficial effects of the above technical solution are: by adopting the solution provided in this embodiment, classified data is obtained according to the CNN-LSTM model; and the RBF neural network model is used to predict the changes in geological parameters of the tunnel face, thereby providing a guarantee for the optimization quality of the current construction parameters of tunnel excavation.

[0091] In one embodiment, according to the prediction result of the geological parameter change of the tunnel face, the construction parameters of the tunnel excavation are optimized to obtain the optimized construction parameter data, including:

[0092] Based on the tunnel excavation space digital twin platform, the current construction parameters of the tunnel excavation are obtained;

[0093] Based on the current construction parameters, the corresponding geological parameters of the first tunnel face are obtained according to the correspondence relationship library between the set construction parameters and the geological parameters of the tunnel face;

[0094] The geological parameters of the first tunnel face are compared and analyzed with the prediction results of the changes in the geological parameters of the tunnel face to obtain the change difference of the geological parameters of the tunnel face. If the change difference is greater than the set difference threshold, the first construction parameters are matched based on the geological parameters of the tunnel face in the prediction results of the geological parameters of the tunnel face and relying on the matching correspondence library between the geological parameters of the tunnel face and the construction parameters. The current construction parameters are optimized according to the first construction parameters to obtain the optimized tunnel excavation construction parameter data.

[0095] The working principle of the above technical scheme is: in order to obtain optimized construction parameter data, the present invention first obtains the current construction parameters of tunnel excavation based on the tunnel excavation space digital twin platform; then based on the current construction parameters, according to the set construction parameters and the corresponding relationship library of tunnel face geological parameters, obtain the corresponding first tunnel face geological parameters; compare and analyze the first tunnel face geological parameters with the tunnel face geological parameter change prediction results to obtain the change difference of the tunnel face geological parameters. If the change difference is greater than the set difference threshold, based on the tunnel face geological parameters in the tunnel face geological parameter change prediction results, relying on the matching correspondence library between the tunnel face geological parameters and the construction parameters, the first construction parameters are matched to obtain, and the current construction parameters are optimized according to the first construction parameters to obtain the optimized tunnel excavation construction parameter data.

[0096] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, a correspondence library between the set construction parameters and the tunnel face geological parameters can be used to match the changes in construction parameters and tunnel face geological parameters, so as to better optimize the tunnel excavation construction parameter data.

[0097] In one embodiment, tunnel excavation support is implemented according to the optimized tunnel excavation construction parameter data, including:

[0098] Carry out tunnel excavation operations according to the optimized tunnel excavation construction parameter data;

[0099] Based on the set tunnel structure deformation monitoring model, the tunnel structure deformation generated during the tunnel excavation operation is monitored to obtain the monitoring results;

[0100] The monitoring results are evaluated to see whether the risks are controllable. If the evaluation result shows that the risks are uncontrollable, the set support parameters are adjusted and the adjusted support parameters are used for support during tunnel excavation operations.

[0101] The working principle of the above technical solution is as follows: in order to realize the implementation of tunnel excavation support, the present invention first implements tunnel excavation operations according to the optimized tunnel excavation construction parameter data; then, based on the set tunnel structure deformation monitoring model, the tunnel structure deformation generated during the tunnel excavation operation is monitored to obtain monitoring results; finally, the monitoring results are evaluated to see whether the risks are controllable. If the evaluation result is that the risks are uncontrollable, the set support parameters are adjusted, and the adjusted support parameters are used for support during the tunnel excavation operation.

[0102] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, by monitoring the tunnel structure deformation generated during the excavation operation and evaluating whether the risk of the monitoring results is controllable, the risk of tunnel deformation to tunnel excavation support can be discovered in time, so that support improvement measures can be taken in a timely and targeted manner.

[0103] In one embodiment, based on a set tunnel structure deformation monitoring model, the tunnel structure deformation generated during the tunnel excavation operation is monitored to obtain monitoring results, including:

[0104] Configure monitoring devices, including but not limited to ultrasonic flaw detectors and pressure gauges;

[0105] The monitoring device is used to obtain the tunnel structure change data, and based on the set wireless signal transmission network, the tunnel structure change data is sent to the tunnel excavation space digital twin platform;

[0106] Based on the normal deformation data set of the tunnel structure set in the tunnel excavation space digital twin platform, deformation deduction is performed using the set tunnel structure deformation deduction model to obtain deformation deduction data;

[0107] Compare and analyze the deformation deduction data with the tunnel structure change data to obtain deformation change difference data, deformation rate difference data, and influence degree data on deformation associated data; the deformation associated data includes tunnel structure parameter data associated with the deformation;

[0108] The deformation change difference data and the deformation rate difference data are weighted and summed to obtain the deformation comprehensive change data;

[0109] The tunnel structure deformation monitoring model is used for monitoring to obtain monitoring results on the comprehensive change data of deformation and the degree of impact data.

[0110] The working principle of the above technical solution is: in order to monitor the tunnel structure deformation generated during the excavation operation and obtain the monitoring results, the present invention first configures a monitoring device, which includes but is not limited to an ultrasonic flaw detector and a pressure gauge; uses the monitoring device to obtain tunnel structure change data, and based on the set wireless signal transmission network, sends the tunnel structure change data to the tunnel excavation space digital twin platform; then, based on the normal deformation data set of the tunnel structure set in the tunnel excavation space digital twin platform, the set tunnel structure deformation deduction model is used to perform deformation deduction to obtain deformation deduction data; finally, the deformation deduction data is compared and analyzed with the tunnel structure change data to obtain deformation change difference data, deformation rate difference data, and influence degree data on deformation related data; the deformation related data includes tunnel structure parameter data associated with the deformation; the deformation change difference data and the deformation rate difference data are weighted and summed respectively to obtain comprehensive deformation change data; the tunnel structure deformation monitoring model is used for monitoring to obtain monitoring results on the comprehensive deformation change data and the influence degree data.

[0111] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, accurate monitoring results can be guaranteed by monitoring the comprehensive deformation change data and the impact degree data.

[0112] In one embodiment, the monitoring result is evaluated to determine whether the risk is controllable. If the evaluation result shows that the risk is uncontrollable, the set support parameters are adjusted, and the adjusted support parameters are used for support during the tunnel excavation operation, including:

[0113] Based on the set first risk prediction model and the comprehensive deformation change data, a basic risk prediction is performed on the tunnel support risk to obtain a basic risk prediction value. If the basic risk prediction value is greater than the set first risk prediction threshold, the risk is assessed to be uncontrollable.

[0114] If the basic risk prediction value is less than the set first risk prediction threshold, then based on the set second risk prediction model, according to the deformation comprehensive change data and the impact degree data, an additional risk prediction is performed on the tunnel support risk to obtain an additional risk prediction value. If the additional risk prediction value is greater than the set second risk prediction threshold, the risk is assessed to be uncontrollable.

[0115] According to the assessment results of uncontrollable risks, the set support parameters are adjusted and the adjusted support parameters are used for support during tunnel excavation operations.

[0116] The working principle of the above technical scheme is: in order to evaluate whether the risk of the monitoring results is controllable, and adjust the set support parameters according to the evaluation result that the risk is uncontrollable, so that the adjusted support parameters can be used for support in the tunnel excavation process, the present invention first performs a basic risk prediction on the tunnel support risk based on the set first risk prediction model and the comprehensive deformation change data to obtain a basic risk prediction value. If the basic risk prediction value is greater than the set first risk prediction threshold, it is evaluated as the risk is uncontrollable; if the basic risk prediction value is less than the set first risk prediction threshold, then based on the set second risk prediction model, an additional risk prediction is performed on the tunnel support risk according to the comprehensive deformation change data and the impact degree data to obtain an additional risk prediction value. If the additional risk prediction value is greater than the set second risk prediction threshold, it is evaluated as the risk is uncontrollable; finally, according to the evaluation result that the risk is uncontrollable, the set support parameters are adjusted, and the adjusted support parameters are used for support in the tunnel excavation process.

[0117] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, basic risk prediction and additional risk prediction are performed according to the comprehensive deformation change data and the impact degree data, so as to ensure that accurate risk prediction results are obtained.

[0118] Tunnel excavation support management system based on advanced geological prediction information, such as Figure 3 As shown, including:

[0119] The tunnel face geological parameter data acquisition module is used to obtain geological prediction information of the tunnel excavation space and extract the tunnel face geological parameter data;

[0120] The construction parameter data optimization module is used to optimize the tunnel excavation construction parameter data by combining the tunnel face geological parameter data;

[0121] The tunnel excavation support implementation module is used to implement tunnel excavation support according to the optimized tunnel excavation construction parameter data.

[0122] The working principle of the above technical solution is: in order to realize a tunnel excavation support management system based on advanced geological prediction information, the present invention proposes a face geological parameter data acquisition module for acquiring geological prediction information of the tunnel excavation space and extracting tunnel face geological parameter data; proposes a construction parameter data optimization module for optimizing tunnel excavation construction parameter data in combination with tunnel face geological parameter data; proposes a tunnel excavation support implementation module for implementing tunnel excavation support according to the optimized tunnel excavation construction parameter data.

[0123] The beneficial effect of the above technical scheme is: by adopting the scheme provided in this embodiment, by optimizing the tunnel excavation construction parameter data based on the geological prediction information of the tunnel excavation space and according to the analysis of the extracted tunnel face geological parameter data, and implementing tunnel excavation support according to the optimized tunnel excavation construction parameter data, the safety of tunnel excavation support can be improved, and the efficiency and quality of tunnel construction can be ensured.

[0124] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A tunnel excavation support management method based on advanced geological prediction information, characterized in that: include: Obtain geological prediction information of the tunnel excavation space and extract geological parameter data of the tunnel face; Combined with the geological parameter data of the tunnel face, the tunnel excavation construction parameter data is optimized; Combined with the geological parameter data of the tunnel face, the tunnel excavation construction parameter data is optimized, including: Build a CNN model, add LSTM layer and attention mechanism to the CNN model to get the CNN-LSTM model; Based on the CNN-LSTM model, feature extraction and classification are performed on the geological parameter data of the tunnel face to obtain classified data; Based on the classified data, the RBF neural network model is used to predict the changes in the geological parameters of the tunnel face and obtain the prediction results of the changes in the geological parameters of the tunnel face; According to the prediction results of the geological parameters change of the tunnel face, the current construction parameters of the tunnel excavation are optimized to obtain the optimized construction parameter data; Tunnel excavation support is implemented according to the optimized tunnel excavation construction parameter data.

2. The tunnel excavation support management method based on advanced geological prediction information according to claim 1 is characterized in that: Obtain geological prediction information for tunnel excavation space, including: Collect and obtain real-time geological status data based on the deployed geological forecast information collection device; The tunnel geological structure model constructed based on BIM technology can obtain tunnel geological parameter data; Based on digital twin technology, a digital twin platform for tunnel excavation space is constructed according to real-time geological status data and tunnel geological parameter data; Based on the digital twin platform of tunnel excavation space, the geological prediction information of tunnel excavation space is obtained.

3. The tunnel excavation support management method based on advanced geological prediction information according to claim 2 is characterized in that: The geological forecast information collection device is used to collect and obtain real-time geological status data, including: Performing data collection of manual collection items based on the deployed distributed collection devices to obtain first category item data; the distributed collection devices include but are not limited to point load meters and sonic wave testers; Performing automatic data collection at a set period based on the deployed automatic collection device to obtain the second category item data; the automatic collection device includes but is not limited to a thermometer, a hygrometer, a pressure sensor and a displacement sensor; The real-time geological status data is composed based on the first category item data and the second category item data.

4. The tunnel excavation support management method based on advanced geological prediction information according to claim 2 is characterized in that: Extract geological parameter data of tunnel face, including: According to the tunnel geological parameter data, the geological characteristic data of the tunnel face to be excavated are obtained; the geological characteristic data include but are not limited to the joints, cracks, degree of fragmentation, degree of roughness, degree of smoothness, mud-rock and water gushing data of the surrounding rock; Using the set data extraction template, extract the surrounding rock strength data and rock integrity data of several tunnel faces to be excavated; The geological parameter data of the tunnel face are composed based on the geological characteristic data, surrounding rock strength data and rock integrity data.

5. The tunnel excavation support management method based on advanced geological prediction information according to claim 1 is characterized in that: According to the prediction results of the geological parameters change of the tunnel face, the construction parameters of the tunnel excavation are optimized to obtain the optimized construction parameter data, including: Based on the tunnel excavation space digital twin platform, the current construction parameters of the tunnel excavation are obtained; Based on the current construction parameters, the corresponding geological parameters of the first tunnel face are obtained according to the correspondence relationship library between the set construction parameters and the geological parameters of the tunnel face; The geological parameters of the first tunnel face are compared and analyzed with the predicted results of the changes in the geological parameters of the tunnel face to obtain the change difference of the geological parameters of the tunnel face. If the change difference is greater than the set difference threshold, the first construction parameters are matched based on the geological parameters of the tunnel face in the predicted results of the geological parameters of the tunnel face and relying on the matching correspondence library between the geological parameters of the tunnel face and the construction parameters. The current construction parameters are optimized according to the first construction parameters to obtain the optimized tunnel excavation construction parameter data.

6. The tunnel excavation support management method based on advanced geological prediction information according to claim 1 is characterized in that: Implement tunnel excavation support according to the optimized tunnel excavation construction parameter data, including: Carry out tunnel excavation operations according to the optimized tunnel excavation construction parameter data; Based on the set tunnel structure deformation monitoring model, the tunnel structure deformation generated during the tunnel excavation operation is monitored to obtain the monitoring results; The monitoring results are evaluated to see whether the risks are controllable. If the evaluation result shows that the risks are uncontrollable, the set support parameters are adjusted and the adjusted support parameters are used for support during tunnel excavation operations.

7. The tunnel excavation support management method based on advanced geological prediction information according to claim 6 is characterized in that: Based on the set tunnel structure deformation monitoring model, the tunnel structure deformation generated during the tunnel excavation operation is monitored to obtain monitoring results, including: Configure monitoring devices, including but not limited to ultrasonic flaw detectors and pressure gauges; The monitoring device is used to obtain the tunnel structure change data, and based on the set wireless signal transmission network, the tunnel structure change data is sent to the tunnel excavation space digital twin platform; Based on the normal deformation data set of the tunnel structure set in the tunnel excavation space digital twin platform, deformation deduction is performed using the set tunnel structure deformation deduction model to obtain deformation deduction data; Compare and analyze the deformation deduction data with the tunnel structure change data to obtain deformation change difference data, deformation rate difference data, and influence degree data on deformation associated data; the deformation associated data includes tunnel structure parameter data associated with the deformation; The deformation change difference data and the deformation rate difference data are weighted and summed to obtain the deformation comprehensive change data; The tunnel structure deformation monitoring model is used for monitoring to obtain monitoring results on the comprehensive change data of deformation and the degree of impact data.

8. The tunnel excavation support management method based on advanced geological prediction information according to claim 7 is characterized in that: The monitoring results are evaluated to see whether the risks are controllable. If the evaluation result shows that the risks are uncontrollable, the set support parameters are adjusted and used for support during tunnel excavation operations, including: Based on the set first risk prediction model and the comprehensive deformation change data, a basic risk prediction is performed on the tunnel support risk to obtain a basic risk prediction value. If the basic risk prediction value is greater than the set first risk prediction threshold, the risk is assessed to be uncontrollable. If the basic risk prediction value is less than the set first risk prediction threshold, then based on the set second risk prediction model, according to the deformation comprehensive change data and the impact degree data, an additional risk prediction is performed on the tunnel support risk to obtain an additional risk prediction value. If the additional risk prediction value is greater than the set second risk prediction threshold, the risk is assessed to be uncontrollable. According to the assessment results of uncontrollable risks, the set support parameters are adjusted and the adjusted support parameters are used for support during tunnel excavation operations.

9. The tunnel excavation support management system based on advanced geological prediction information is characterized by: include: The tunnel face geological parameter data acquisition module is used to obtain geological prediction information of the tunnel excavation space and extract the tunnel face geological parameter data; The construction parameter data optimization module is used to optimize the tunnel excavation construction parameter data by combining the tunnel face geological parameter data; including: Build a CNN model, add LSTM layer and attention mechanism to the CNN model to get the CNN-LSTM model; Based on the CNN-LSTM model, feature extraction and classification are performed on the geological parameter data of the tunnel face to obtain classified data; Based on the classified data, the RBF neural network model is used to predict the changes in the geological parameters of the tunnel face and obtain the prediction results of the changes in the geological parameters of the tunnel face; According to the prediction results of the geological parameters change of the tunnel face, the current construction parameters of the tunnel excavation are optimized to obtain the optimized construction parameter data; The tunnel excavation support implementation module is used to implement tunnel excavation support according to the optimized tunnel excavation construction parameter data.

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