Shield tunneling parameter optimization method and system based on vibration perception
Through a shield tunneling parameter optimization method based on vibration perception, machine learning and multi-objective optimization algorithms are used to dynamically adjust the tunneling parameters, solving the problems of strong parameter dependence and high energy consumption in shield construction, and improving construction safety and efficiency.
Patent Information
- Application Number
- CN202510769988.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-10
- Publication Date
- 2025-09-19
AI Technical Summary
The existing shield tunneling parameter optimization relies on manual experience, resulting in inconsistent parameter settings, difficulty in adapting to complex strata, prone to accidents, high energy consumption and large carbon emissions.
By acquiring vibration data during shield tunneling, using machine learning algorithms to identify geological conditions, and combining multi-objective optimization algorithms to optimize tunneling parameters, including dynamic adjustment of cutterhead speed, tunneling speed, thrust, and torque.
It realizes automatic optimization of shield tunneling parameters, reduces energy consumption, improves construction safety and efficiency, and adapts to complex stratum changes.
Smart Images
Figure CN120671243A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of tunnel construction, and in particular to a shield tunneling parameter optimization method and system based on vibration sensing. Background Art
[0002] In shield tunnel construction, the precise setting of excavation parameters is crucial to project quality and progress. Traditionally, excavation parameter determination relies primarily on operator experience, adjusted based on preliminary geological survey data and on-site slag discharge conditions. However, differences in operator experience lead to inconsistent parameter settings and make it difficult to quickly and accurately adapt to complex and changing ground conditions. For example, when traversing areas alternating between single and complex strata, fixed parameter settings can easily lead to serious engineering accidents such as ground subsidence, tool damage, and shield machine jamming. Furthermore, these settings fail to meet the requirements for energy conservation, emission reduction, and power consumption reduction.
[0003] The vibration signals generated by the shield machine during the tunneling process are correlated with the stratum characteristics. Although there have been some developments in shield construction technology, the real-time vibration signals during the operation of the shield machine have not been effectively utilized in the dynamic optimization of tunneling parameters, resulting in poor optimization effects of existing shield tunneling parameters. Summary of the Invention
[0004] In order to solve the above problems, the present disclosure proposes a shield tunneling parameter optimization method and system based on vibration perception, which solves the problems of existing shield tunneling parameter optimization such as strong dependence on manual experience, high carbon emissions, and poor timeliness, thereby reducing construction energy consumption and improving construction safety and tunneling efficiency.
[0005] According to some embodiments, the present disclosure adopts the following technical solutions:
[0006] A shield tunneling parameter optimization method based on vibration sensing, comprising:
[0007] Obtain vibration data and tunneling parameter data during shield tunneling;
[0008] Extract vibration signal features from vibration data, combine them with a pre-established stratum feature model library, and use machine learning algorithms to identify current geological conditions and determine the excavability of the surrounding rock.
[0009] Based on the tunneling parameter data, the tunneling power consumption and tunneling efficiency are calculated. According to the tunneling efficiency grade evaluation system, the tunneling efficiency under different surrounding rock excavability is evaluated to obtain the tunneling efficiency grade evaluation results.
[0010] Based on the evaluation results of surrounding rock excavability and excavation efficiency level, a multi-objective optimization algorithm is used in combination with the weighted sum method to construct an excavation parameter optimization model. By solving the excavation parameter optimization model, the optimal excavation parameters are obtained.
[0011] Among them, the tunneling parameter optimization model takes maximum tunneling efficiency and minimum tunneling power consumption as optimization goals, cutterhead rotation speed and tunneling speed as design variables, and at the same time constrains the shield thrust and cutterhead torque. According to the statistical distribution laws of various variables under different geological conditions and the actual constraints of the project, the constraint range of the variables and the weight ratio of maximum tunneling efficiency and minimum tunneling power consumption are determined.
[0012] According to some embodiments, the present disclosure adopts the following technical solutions:
[0013] A shield tunneling parameter optimization system based on vibration sensing, comprising:
[0014] The acquisition module is configured to: obtain vibration data and excavation parameter data during the shield tunneling process;
[0015] The recognition module is configured to extract vibration signal features from the vibration data, combine them with a pre-established stratum feature model library, and use machine learning algorithms to identify the current geological conditions and determine the excavability of the surrounding rock.
[0016] The evaluation module is configured to calculate the excavation power consumption and excavation efficiency based on the excavation parameter data, evaluate the excavation efficiency under different surrounding rock excavability according to the excavation efficiency grade evaluation system, and obtain the excavation efficiency grade evaluation result;
[0017] The solution module is configured to: construct an excavation parameter optimization model based on the surrounding rock excavability and excavation efficiency level evaluation results using a multi-objective optimization algorithm combined with a weighted sum method, and obtain the optimal excavation parameters by solving the excavation parameter optimization model;
[0018] Among them, the tunneling parameter optimization model takes maximum tunneling efficiency and minimum tunneling power consumption as optimization goals, cutterhead rotation speed and tunneling speed as design variables, and at the same time constrains the shield thrust and cutterhead torque. According to the statistical distribution laws of various variables under different geological conditions and the actual constraints of the project, the constraint range of the variables and the weight ratio of maximum tunneling efficiency and minimum tunneling power consumption are determined.
[0019] According to some embodiments, the present disclosure adopts the following technical solutions:
[0020] A computer program product includes a computer program, which implements a shield tunneling parameter optimization method based on vibration perception when executed by a processor.
[0021] According to some embodiments, the present disclosure adopts the following technical solutions:
[0022] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, a shield tunneling parameter optimization method based on vibration perception is implemented.
[0023] According to some embodiments, the present disclosure adopts the following technical solutions:
[0024] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device implements the shield tunneling parameter optimization method based on vibration perception.
[0025] Compared with the prior art, the present invention has the following beneficial effects:
[0026] The present invention proposes a shield tunneling parameter optimization method and system based on vibration perception. Based on the vibration data and tunneling parameter data during the shield tunneling process, the excavationability of the surrounding rock and the tunneling efficiency are judged, and a multi-objective optimization algorithm is adopted to solve the optimal tunneling parameters. The problems existing in the existing shield tunneling parameter optimization, such as strong dependence on manual experience, high carbon emissions, and poor timeliness, are solved, thereby reducing construction energy consumption and improving construction safety and tunneling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.
[0028] Figure 1 This is a flow chart of the method of Example 1. DETAILED DESCRIPTION
[0029] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.
[0030] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.
[0031] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "comprising" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.
[0032] Example 1
[0033] In one embodiment of the present disclosure, a shield tunneling parameter optimization method based on vibration sensing is provided to solve the problems of existing shield tunneling parameter optimization, such as strong dependence on manual experience, high carbon emissions, and poor timeliness, thereby reducing construction energy consumption and improving construction safety and tunneling efficiency. Figure 1 As shown, the specific steps are:
[0034] Step S1: Obtain vibration data and tunneling parameter data during shield tunneling.
[0035] High-sensitivity sensors are rationally arranged at key locations of the shield machine, and appropriate sampling frequencies are set to obtain vibration data and tunneling parameter data during the shield tunneling process.
[0036] Specifically, vibration, pressure, displacement and other sensors are arranged in key positions such as the shield machine's soil bin wall, thrust cylinder, cutterhead drive system, cutterhead rotating shaft or near the drive motor output shaft, and shield machine body.
[0037] Vibration sensors can accurately sense the vibrations generated by the interaction between the shield machine and the stratum. At the same time, pressure sensors, displacement sensors, etc. are used to collect the tunneling parameter data of the shield machine, providing more comprehensive data support for subsequent analysis.
[0038] Specifically, the accuracy of vibration, pressure and displacement sensors is better than 0.1m 2 / s, 0.01MPa, 0.1mm, and a sampling frequency of 800Hz to ensure accurate monitoring of parameter changes during shield tunneling; the selection of sensors should meet the requirements of the harsh environment of shield construction, such as resistance to high temperature, high pressure, and vibration interference.
[0039] The collected raw data is pre-processed by denoising, filtering, etc. to eliminate environmental interference and remove invalid data and abnormal data in non-excavation sections.
[0040] Step S2: Extract vibration signal features from the vibration data, combine them with the pre-established stratum feature model library, identify the current geological conditions through machine learning algorithms, and determine the excavability of the surrounding rock.
[0041] The key characteristic parameters of the time domain, frequency domain and time-frequency domain of the vibration signal are extracted and input into the pre-established formation identification model to identify the current formation type (single formation, composite formation, etc.) and its key geological parameters (soft and hard ratio, surrounding rock strength, surrounding rock grade), etc. The formation type and geological parameters are collectively referred to as geological conditions.
[0042] Specifically, time domain signals include indicators such as peak value, effective value, and margin; frequency domain signals include frequency domain characteristics such as main frequency and spectrum bandwidth; time-frequency analysis includes characteristics such as instantaneous frequency and local frequency band.
[0043] The stratum identification model is an intelligent model of geological type, surrounding rock strength, surrounding rock grade, and soft and hard ratio that is pre-established based on historical data and artificial intelligence algorithms (such as support vector machines, deep learning neural networks, etc.). It is used to predict geological conditions based on vibration signal characteristics.
[0044] Based on the geological conditions, the excavability of the surrounding rock is determined. The specific criteria are shown in Table 1:
[0045] Table 1 Criteria for determining the excavability of surrounding rock
[0046]
[0047] Step S3: Based on the tunneling parameter data, the tunneling power consumption and tunneling efficiency are calculated. According to the tunneling efficiency grade evaluation system, the tunneling efficiency under different surrounding rock excavability is evaluated to obtain the tunneling efficiency grade evaluation result.
[0048] Based on the excavation parameters, the excavation power consumption and excavation efficiency are calculated to evaluate the energy utilization efficiency.
[0049] Specifically, the excavation power consumption (P = Fv + 2πTn, where v is the excavation speed and n is the cutterhead speed) and the excavation efficiency (η = 1 / SE, where SE is the excavation specific energy and SE = Pt / V, where V is the volume of surrounding rock crushed by the cutter within time t) respectively evaluate the energy consumed per unit time and the amount of rock and soil excavation that can be completed per unit energy.
[0050] Based on the tunneling efficiency rating evaluation system, the shield's construction performance and energy efficiency under current ground conditions are comprehensively evaluated to obtain the tunneling efficiency rating, as shown in Table 2:
[0051] Table 2 Criteria for determining tunneling efficiency level
[0052]
[0053] Specifically, as shown in Table 2, the excavability of surrounding rock is divided into five levels: very good, good, medium, poor, and very poor. The surrounding rock conditions include surrounding rock strength, surrounding rock grade, and the ratio of soft to hard rock. The range of excavation efficiency is obtained based on historical data statistics. The excavation efficiency levels under different stratum conditions are divided into three levels: I, II, and III.
[0054] Step S4: Based on the geological conditions and evaluation results, a multi-objective optimization algorithm is used in combination with the weighted sum method to construct a tunneling parameter optimization model. By solving the tunneling parameter optimization model, the optimal tunneling parameters are obtained.
[0055] Specifically, the NSGA-II multi-objective optimization algorithm combined with the weighted sum method (WSM) is used to construct an optimization model for tunneling parameters. The maximum tunneling efficiency (η) and the minimum tunneling power consumption (P) are set as optimization objectives, and the cutterhead speed (n) and tunneling speed (v) are used as design variables. At the same time, the shield thrust F and cutterhead torque T are constrained. According to the statistical distribution law of each parameter under different formation conditions and the actual constraints of the project, the constraint range of the design variables and the weight ratio of the maximum tunneling efficiency (η) and the minimum tunneling power consumption (P) are determined. The optimization objective function is:
[0056] y=(maxη,minP)
[0057] In strata with low surrounding rock strength, the shield tunneling state is usually "low thrust, low torque, high rotation speed, and high tunneling speed". The lower and upper limits of the design variables are set accordingly to construct constraint conditions.
[0058] Specifically, through statistical analysis of a large amount of engineering data, the corresponding variable constraint ranges are shown in Table 3:
[0059] Table 3 Variable constraint range table
[0060]
[0061] Specifically, through statistical analysis of a large amount of engineering data, the shield thrust F and cutterhead torque T, as well as the tunneling speed and cutterhead speed, satisfy the constraints shown in Table 4 below:
[0062] Table 4 Constraint relationship table
[0063]
[0064] Specifically, the weights of tunneling efficiency and tunneling power consumption in a single stratum are both 0.5; the weights of tunneling efficiency and tunneling power consumption in a composite stratum are 0.8 and 0.2 respectively.
[0065] The model is iteratively solved using the NSGA-II algorithm. After multiple iterations, a set of Pareto frontier solutions is generated. Each solution represents a feasible tunneling parameter combination that meets multiple objective requirements.
[0066] Within the Pareto frontier solution set, different weights are assigned to tunneling efficiency and power consumption based on stratum type and project requirements. The WSM method is then used to identify the optimal tunneling parameter combination. These optimized parameters are then transmitted to the shield machine control system in real time, driving the actuators to adjust the tunneling parameters.
[0067] Based on a powerful computing server, running the NSGA-II algorithm and WSM algorithm programs, it can automatically match the best weights and optimization plans according to different formation conditions and engineering requirements; at the same time, it also has a visual interface that can display the optimization process and results, making it convenient for operators to monitor and adjust.
[0068] Step S5: Perform feedback control based on the optimal excavation parameters obtained.
[0069] The optimal tunneling parameters are transmitted to the shield machine control system in real time, driving the actuator to adjust the tunneling parameters. The actuator's feedback signals and the actual operating parameters of the shield machine, such as cutterhead torque, shield thrust, and tunneling speed, are collected in real time. When the tunneling energy consumption calculated based on the current tunneling parameters is Level I (Table 2), the current tunneling parameters are maintained. When the tunneling efficiency is Level II (Table 2), the tunneling parameters are selectively adjusted with reference to the project risk and construction progress. When it is lower than Level III (Table 2), the re-optimization process is immediately triggered, and the newly collected vibration signals and operating parameters are included in the analysis scope. The formation characteristics are re-identified and the optimization calculation is re-performed to achieve dynamic closed-loop optimization control of the tunneling parameters.
[0070] Example 2
[0071] In one embodiment of the present disclosure, a shield tunneling parameter optimization system based on vibration sensing is provided, comprising:
[0072] The acquisition module is configured to: obtain vibration data and excavation parameter data during the shield tunneling process;
[0073] The recognition module is configured to extract vibration signal features from the vibration data, combine them with a pre-established stratum feature model library, and use machine learning algorithms to identify the current geological conditions and determine the excavability of the surrounding rock.
[0074] The evaluation module is configured to calculate the excavation power consumption and excavation efficiency based on the excavation parameter data, evaluate the excavation efficiency under different surrounding rock excavability according to the excavation efficiency grade evaluation system, and obtain the excavation efficiency grade evaluation result;
[0075] The solution module is configured to: construct an excavation parameter optimization model based on the surrounding rock excavability and excavation efficiency level evaluation results using a multi-objective optimization algorithm combined with a weighted sum method, and obtain the optimal excavation parameters by solving the excavation parameter optimization model;
[0076] Among them, the tunneling parameter optimization model takes maximum tunneling efficiency and minimum tunneling power consumption as optimization goals, cutterhead rotation speed and tunneling speed as design variables, and at the same time constrains the shield thrust and cutterhead torque. According to the statistical distribution laws of various variables under different geological conditions and the actual constraints of the project, the constraint range of the variables and the weight ratio of maximum tunneling efficiency and minimum tunneling power consumption are determined.
[0077] Example 3
[0078] In one embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method for optimizing shield tunneling parameters based on vibration perception is implemented.
[0079] Example 4
[0080] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which is used to store computer instructions. When the computer instructions are executed by a processor, the vibration sensing-based shield tunneling parameter optimization method is implemented.
[0081] Example 5
[0082] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the shield tunneling parameter optimization method based on vibration perception.
[0083] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0085] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.
Claims
1. A shield tunneling parameter optimization method based on vibration sensing, characterized in that: include: Obtain vibration data and tunneling parameter data during shield tunneling; Extract vibration signal features from vibration data, combine them with a pre-established stratum feature model library, and use machine learning algorithms to identify current geological conditions and determine the excavability of the surrounding rock. Based on the tunneling parameter data, the tunneling power consumption and tunneling efficiency are calculated. According to the tunneling efficiency grade evaluation system, the tunneling efficiency under different surrounding rock excavability is evaluated to obtain the tunneling efficiency grade evaluation results. Based on the evaluation results of surrounding rock excavability and excavation efficiency level, a multi-objective optimization algorithm is used in combination with the weighted sum method to construct an excavation parameter optimization model. By solving the excavation parameter optimization model, the optimal excavation parameters are obtained. Among them, the tunneling parameter optimization model takes maximum tunneling efficiency and minimum tunneling power consumption as optimization goals, cutterhead rotation speed and tunneling speed as design variables, and at the same time constrains the shield thrust and cutterhead torque. According to the statistical distribution laws of various variables under different geological conditions and the actual constraints of the project, the constraint range of the variables and the weight ratio of maximum tunneling efficiency and minimum tunneling power consumption are determined.
2. A shield tunneling parameter optimization method based on vibration sensing according to claim 1, characterized in that: The vibration data is collected by a vibration sensor arranged on the shield machine; The tunneling parameter data is collected by pressure sensors and displacement sensors installed on the shield machine.
3. The shield tunneling parameter optimization method based on vibration sensing according to claim 1, characterized in that: The method extracts vibration signal features from the vibration data, and extracts characteristic parameters of the vibration data in the time domain, frequency domain and time-frequency domain.
4. The shield tunneling parameter optimization method based on vibration sensing according to claim 1, characterized in that: The geological conditions include geological types and geological parameters, and the geological parameters include surrounding rock strength, surrounding rock grade, and soft-hard ratio.
5. The shield tunneling parameter optimization method based on vibration sensing according to claim 1, characterized in that: The tunneling parameter optimization model is solved by iteratively solving the model using the NSGA-II algorithm, and a set of solutions is generated after multiple iterations, each solution representing a feasible tunneling parameter combination that meets multiple objective requirements; In the generated set of solutions, different weights are set for tunneling efficiency and tunneling power consumption based on geological conditions and actual engineering requirements, and the WSM method is used to screen out the optimal tunneling parameter combination.
6. The shield tunneling parameter optimization method based on vibration sensing according to claim 1, characterized in that: It also includes feedback control based on the optimal excavation parameters obtained.
7. A shield tunneling parameter optimization system based on vibration sensing, characterized in that: include: The acquisition module is configured to: obtain vibration data and excavation parameter data during the shield tunneling process; The recognition module is configured to extract vibration signal features from the vibration data, combine them with a pre-established stratum feature model library, and use machine learning algorithms to identify the current geological conditions and determine the excavability of the surrounding rock. The evaluation module is configured to calculate the excavation power consumption and excavation efficiency based on the excavation parameter data, evaluate the excavation efficiency under different surrounding rock excavability according to the excavation efficiency grade evaluation system, and obtain the excavation efficiency grade evaluation result; The solution module is configured to: construct an excavation parameter optimization model based on the surrounding rock excavability and excavation efficiency level evaluation results using a multi-objective optimization algorithm combined with a weighted sum method, and obtain the optimal excavation parameters by solving the excavation parameter optimization model; Among them, the tunneling parameter optimization model takes maximum tunneling efficiency and minimum tunneling power consumption as optimization goals, cutterhead rotation speed and tunneling speed as design variables, and at the same time constrains the shield thrust and cutterhead torque. According to the statistical distribution laws of various variables under different geological conditions and the actual constraints of the project, the constraint range of the variables and the weight ratio of maximum tunneling efficiency and minimum tunneling power consumption are determined.
8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the shield tunneling parameter optimization method based on vibration perception described in any one of claims 1 to 6 is implemented.
9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the shield tunneling parameter optimization method based on vibration perception as described in any one of claims 1 to 6 is implemented.
10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement a shield tunneling parameter optimization method based on vibration perception as described in any one of claims 1 to 6.