Shield tunnel excavation face instability disaster risk quantitative evaluation method and system

Abstract

The invention belongs to the technical field of tunnel construction risk assessment, and provides a shield tunnel excavation face instability disaster risk quantitative assessment method and system. The evaluation method comprises the following steps: constructing a shield tunnel excavation face instability disaster risk evaluation system and converting the system into a Bayesian network topological structure; calculating the membership degree of each disaster evaluation index according to the digital feature calculation of the forward cloud generator and the to-be-evaluated data, and converting the membership degree into a Bayesian network prior probability; according to the historical data of the instability disaster of the excavation face of the shield tunnel and the Bayesian network topological structure, parameter learning is conducted on the historical data of the instability disaster of the excavation face of the shield tunnel, and the conditional probability of a Bayesian network model is calculated; according to the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topological structure, calculating a quantitative evaluation result of the instability disaster risk of the excavation face of the shield tunnel; and according to the quantitative evaluation result of the instability disaster risk of the excavation face of the shield tunnel, performing adjustment decision on shield tunneling control parameters.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel construction risk assessment, and in particular relates to a method and system for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces. Background Art

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

[0003] Common accidents and disasters in shield tunneling construction include excavation face instability leading to ground collapse and sudden water inrush in the shield tunnel. These can impact construction progress and cause economic losses, while serious consequences can result in casualties. Therefore, conducting excavation face instability risk assessments during the construction phase of shield tunneling projects is highly necessary and practically significant, laying the technical foundation for ensuring project quality, safety, and progress.

[0004] In the existing technology, the risk assessment method of shield excavation face instability disaster mostly adopts theoretical analysis methods based on describing the excavation face instability process, numerical analysis methods and hierarchical analysis expert evaluation methods. Among them, the theoretical analysis method based on describing the excavation face instability process is to analyze the problem by establishing mathematical models, assumptions and theoretical frameworks, which usually requires simplifying the complex geological environment. The excavation face instability of shield tunnels often occurs in complex soil layers or geological structures, such as the presence of weak interlayers, faults, groundwater flow and other special situations. Theoretical analysis methods usually rely on idealized geological information expression and it is difficult to fully consider the various changes under complex geological conditions; numerical analysis methods have complex modeling and long calculation time, and the accuracy and results of numerical analysis methods are highly dependent on the accuracy of input parameters. In actual engineering, geological parameters vary greatly and are often not completely known. , the boundary conditions of the interaction between soil and shield machine during excavation are difficult to determine, and it is not suitable for the assessment of the risk of instability of shield excavation face; the hierarchical analysis expert evaluation method determines the weight and priority of each risk factor through expert evaluation and hierarchical analysis, but the expert evaluation method relies on the subjective judgment of experts. In practical applications, the risk assessment of instability of shield excavation face often involves multiple goals and multiple factors. Although the hierarchical analysis method can handle multi-level factors and provide a comprehensive score, it is difficult to effectively solve the problem when the evaluation indicators are interrelated. In addition, the hierarchical analysis method is a static indicator structure and lacks a dynamic update mechanism. It is difficult to accurately evaluate when the engineering conditions change significantly. Summary of the Invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method and system for quantitatively assessing the risk of instability disasters in the excavation face of a shield tunnel, which can improve the processing and reasoning capabilities of the quantitative assessment method for the risk of instability disasters in the excavation face under the complex geological and engineering environment of shield tunneling.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A first aspect of the present invention provides a method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces.

[0007] A method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces includes: From the historical data of shield tunnel excavation face instability disasters, the shield tunnel excavation face instability disaster evaluation indicators were extracted, the risk level standards of each evaluation indicator were divided, and the shield tunnel excavation face instability disaster risk assessment system was constructed and converted into a Bayesian network topology structure. Based on the digital feature calculation of the forward cloud generator and the data to be evaluated, the membership degree of each disaster assessment index is calculated and converted into a Bayesian network prior probability; Based on the historical data of shield tunnel excavation face instability disasters and the Bayesian network topology structure, parameter learning is performed on the historical data of shield tunnel excavation face instability disasters, and the conditional probability of the Bayesian network model is calculated; Based on the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topology, the quantitative assessment results of the shield tunnel excavation face instability disaster risk are calculated; Based on the quantitative assessment results of the instability disaster risk of the shield tunnel excavation face, adjustments to the shield tunneling control parameters are made.

[0008] As an implementation method, the process of dividing the risk level standards of each evaluation indicator is as follows: hierarchically classifying the selected risk assessment indicators and dividing them into indicator layer, criterion layer and target layer according to the characteristics of the indicators; The target layer is the instability disaster risk of the shield tunnel excavation face; The criterion layers are excavation parameters and geological parameters; The index layer includes indicators of excavation parameters and geological parameters; the indicators of the excavation parameters include cutterhead speed, shield excavation speed, and excavation face support pressure; the indicators of the geological parameters include uniaxial compressive strength, rock integrity coefficient, ground stress level and tunnel burial depth.

[0009] As an implementation method, the Bayesian network prior probability is: ; ; ; in, is the consistency parameter, ranging from (0, 1], is the prior probability of the i-th indicator, is the membership degree of the i-th indicator, is the membership degree of the j-th state; is the degree of membership; Ex is the expected value, En is entropy, He is super entropy, C max is the upper limit of the interval, C min is the lower limit of the interval, k is a constant.

[0010] As an implementation method, based on the historical data of instability disasters in shield tunnel excavation faces and the topological relationship between the Bayesian network, the maximum likelihood estimation method is used to learn the parameters of the historical data, and combined with expert knowledge, the conditional probability table of the Bayesian network model is calculated.

[0011] As an implementation method, the conditional probability obtained by parameter learning and the Bayesian network topology are used, and the Bayesian reasoning method is used to combine the current shield tunnel engineering conditions and geological data to forward infer the probability distribution of the current shield tunnel excavation face instability disaster risk level. The probability of shield excavation face instability disaster occurring is expressed as follows:

[0012] in, is the probability of occurrence of shield excavation face instability disaster risk level Ti, is the probability of occurrence of tunneling parameter level C1 when shield excavation face instability disaster risk level Ti occurs, is the probability of occurrence of geological parameter level C2 when the shield excavation face instability disaster risk level Ti occurs, is the probability of occurrence of tunneling parameter level C1, is the probability of occurrence of geological parameter level C1.

[0013] As an implementation method, based on the quantitative assessment results of the instability disaster risk of the shield tunnel excavation face, the decision to adjust the shield tunneling control parameters includes: Taking into account the geological conditions and risk assessment results, the shield tunneling speed, cutterhead speed, soil bin / mud pressure, synchronous grouting pressure and flow, and mud properties are dynamically controlled.

[0014] A second aspect of the present invention provides a shield tunnel excavation face instability disaster risk quantitative assessment system.

[0015] A shield tunnel excavation face instability disaster risk quantitative assessment system, comprising: A Bayesian network topology structure construction module is used to extract shield tunnel excavation face instability disaster evaluation indicators from historical data on shield tunnel excavation face instability disasters, classify the risk level standards for each evaluation indicator, construct a shield tunnel excavation face instability disaster risk assessment system, and convert it into a Bayesian network topology structure; A Bayesian network prior probability calculation module is used to calculate the membership of each disaster assessment indicator based on the digital feature calculation of the forward cloud generator and the data to be evaluated and convert it into a Bayesian network prior probability; A Bayesian network model conditional probability calculation module is used to perform parameter learning on the historical data of shield tunnel excavation face instability disasters and calculate the Bayesian network model conditional probability based on the historical data of shield tunnel excavation face instability disasters and the Bayesian network topology structure; A risk quantitative assessment result calculation module is used to calculate the quantitative assessment results of shield tunnel excavation face instability disaster risk based on the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topology structure; The shield tunneling control parameter decision module is used to make adjustment decisions on the shield tunneling control parameters based on the quantitative assessment results of the instability disaster risk of the shield tunnel excavation face.

[0016] A third aspect of the present invention provides a computer-readable storage medium.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces as described above.

[0018] A fourth aspect of the present invention provides a computer program product.

[0019] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps in the above-mentioned method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces.

[0020] A fifth aspect of the present invention provides an electronic device.

[0021] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for quantitatively assessing the risk of instability disasters in the excavation face of a shield tunnel as described above are implemented.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The present invention constructs a shield tunnel excavation face instability disaster risk assessment system and converts it into a Bayesian network topology structure; based on the historical data of shield tunnel excavation face instability disasters and the Bayesian network topology structure, parameter learning is performed on the historical data of shield tunnel excavation face instability disasters, and the conditional probability of the Bayesian network model is calculated. Then, combined with the Bayesian network prior probability, the conditional probability of the Bayesian network model and the Bayesian network topology structure, the quantitative assessment result of the shield tunnel excavation face instability disaster risk is calculated to achieve adjustment decisions on shield tunneling control parameters, effectively avoiding the subjectivity of the expert knowledge evaluation method, and utilizing the advantages of the Bayesian network in processing complex relationships of multiple factors, thereby improving the processing and reasoning capabilities of the quantitative assessment method of the excavation face instability disaster risk in the complex geological and engineering environment of shield tunneling.

[0023] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0025] Figure 1 It is a schematic diagram of the quantitative assessment process of the instability disaster risk of the shield tunnel excavation face implemented by the present invention; Figure 2 Schematic diagram of the risk index of instability disaster of shield tunnel excavation face according to an embodiment of the present invention; Figure 3 This is a flow chart of a method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces according to an embodiment of the present invention; Figure 4 2. It is a schematic structural diagram of a shield tunnel excavation face instability disaster risk quantitative assessment system according to an embodiment of the present invention; Figure 5 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0027] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. 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 invention belongs.

[0028] 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 invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0029] Example 1 Combine Figure 1 and Figure 3 The embodiment of the present invention provides a method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces, which includes: S1: Extract shield tunnel excavation face instability disaster evaluation indicators from historical data of shield tunnel excavation face instability disasters, classify the risk level standards of each evaluation indicator, construct a shield tunnel excavation face instability disaster risk assessment system and convert it into a Bayesian network topology structure; S2: Calculate the membership of each disaster assessment index based on the digital feature calculation of the forward cloud generator and the data to be evaluated and convert it into a Bayesian network prior probability; S3: Based on the historical data of shield tunnel excavation face instability disasters and the Bayesian network topology structure, parameter learning is performed on the historical data of shield tunnel excavation face instability disasters, and the conditional probability of the Bayesian network model is calculated; S4: Calculate the quantitative assessment results of shield tunnel excavation face instability disaster risk based on the Bayesian network prior probability, Bayesian network model conditional probability and Bayesian network topology structure; S5: Based on the quantitative assessment results of the instability disaster risk of the shield tunnel excavation face, make adjustments to the shield tunneling control parameters.

[0030] In step S1, the process of dividing the risk level standards of each evaluation indicator is as follows: hierarchically classifying the selected risk assessment indicators and dividing them into indicator layer, criterion layer and target layer according to the characteristics of the indicators; The target layer is the instability disaster risk of the shield tunnel excavation face; The criterion layers are excavation parameters and geological parameters; The index layer includes indicators of excavation parameters and geological parameters; Figure 2 As shown, the indicators of the excavation parameters include cutterhead speed, shield excavation speed, and excavation face support pressure; the indicators of the geological parameters include uniaxial compressive strength, rock integrity coefficient, ground stress level and tunnel burial depth.

[0031] In this example, the criterion layer extracts shield tunneling parameters and geological parameters, while the indicator layer extracts cutterhead speed, shield tunneling speed, excavation face support pressure, uniaxial compressive strength, rock mass integrity coefficient, ground stress level, and tunnel depth. To improve inference and calculation efficiency, the risk level of each factor is divided into four levels. This achieves the establishment of risk assessment indicators and risk assessment levels.

[0032] In this embodiment, the risk assessment system is converted into a Bayesian network topology structure, which is essentially a directed acyclic graph. The parent node is the shield tunnel excavation face instability disaster risk evaluation index, the child node is the disaster risk level, and the conditional probability table is calculated, which can effectively quantitatively evaluate the shield tunnel excavation face instability disaster risk.

[0033] In step S2, based on the theoretical characteristics of the cloud model, the digital characteristic calculation method of the forward cloud generator is determined, and the actual values of the cutterhead speed, shield tunneling speed, excavation face support pressure, uniaxial compressive strength, rock mass integrity coefficient, ground stress level and tunnel depth factors in the index layer are determined according to the on-site conditions. The membership degree of each disaster assessment index factor is calculated, and the Bayesian network prior probability conversion is performed. The Bayesian network prior probability is: ; ; ; in, is the consistency parameter, ranging from (0, 1], is the prior probability of the i-th indicator, is the membership degree of the i-th indicator, is the membership degree of the j-th state; is the degree of membership; Ex is the expected value, En is entropy, He is super entropy, C max is the upper limit of the interval, C min is the lower limit of the interval, k is a constant.

[0034] In step S3, based on the historical data of shield tunnel excavation face instability disasters and the topological relationship between the Bayesian network, the maximum likelihood estimation method is used to learn the parameters of the historical data, and combined with expert knowledge, the conditional probability table of the Bayesian network model is calculated.

[0035] In Bayesian networks, parameter learning involves using collected historical data to calculate the conditional probability distribution of each node in the Bayesian network structure. This parameter learning determines the conditional probability table (CPT) in the Bayesian network, enabling the network to accurately perform inference and prediction.

[0036] Fill the calculated conditional probability values into the conditional probability table. The sum of all target node probability values under each parent node condition should be 1.

[0037] Table 1 is the conditional probability table

[0038] In step S4, the conditional probability obtained by parameter learning and the Bayesian network topology are used, and the Bayesian reasoning method is used to combine the current shield tunnel engineering conditions and geological data to forward infer the probability distribution of the current shield tunnel excavation face instability disaster risk level; the probability of shield excavation face instability disaster occurring is expressed as follows:

[0039] in, is the probability of occurrence of shield excavation face instability disaster risk level Ti, is the probability of occurrence of tunneling parameter level C1 when shield excavation face instability disaster risk level Ti occurs, is the probability of occurrence of geological parameter level C2 when the shield excavation face instability disaster risk level Ti occurs, is the probability of occurrence of tunneling parameter level C1, is the probability of occurrence of geological parameter level C1.

[0040] In step S5, based on the quantitative assessment results of the shield tunnel excavation face instability disaster risk, the shield tunneling control parameters are adjusted and decided, including: Taking into account the geological conditions and risk assessment results, the shield tunneling speed, cutterhead speed, soil bin / mud pressure, synchronous grouting pressure and flow, and mud properties are dynamically controlled.

[0041] For the cases where the quantitative assessment results of excavation face instability disaster risk are level I or II, there is no need to adjust the shield tunneling parameters; For situations where the quantitative assessment result of excavation face instability disaster risk is level III, no targeted adjustment of geological conditions is required. Instead, the soil bin / mud pressure can be appropriately increased, the shield tunneling speed can be reduced, and the disturbance of the stratum caused by shield tunneling construction can be reduced. For the situation where the quantitative assessment result of excavation face instability disaster risk is level IV, adjustments need to be made based on the geological conditions. In high-permeability sand and gravel formations, the shield excavation speed should be reduced, the soil bin / mud pressure, mud viscosity and grouting pressure should be increased, and the properties of the slag should be improved. In low-permeability formations, mud cakes are likely to form, causing excavation face instability disasters. The cutter head speed should be increased and the mud viscosity should be reduced.

[0042] The present invention utilizes the advantages of cloud model theory in expressing the fuzziness and uncertainty of risk assessment indicators, and adopts the "50% correlation" method to improve the entropy calculation formula in the digital characteristics of the normal cloud model, thereby realizing the discretization of the prior probability of the Bayesian network assessment model for the stability and instability disaster of the shield tunnel excavation face, better representing the fuzzy random geological environment and engineering conditions during the shield construction process, and combining with the Bayesian network model, the conditional probability is obtained through parameter learning of the collected historical case data, effectively avoiding the subjectivity of the expert knowledge evaluation method, and utilizing the advantages of the Bayesian network in processing complex relationships of multiple factors, thereby improving the processing and reasoning capabilities of the excavation face instability disaster risk quantitative assessment method in the complex geological and engineering environment of shield tunneling.

[0043] Example 2 like Figure 4 As shown, an embodiment of the present invention provides a shield tunnel excavation face instability disaster risk quantitative assessment system, comprising: A Bayesian network topology structure construction module 401 is used to extract shield tunnel excavation face instability disaster evaluation indicators from historical data of shield tunnel excavation face instability disasters, classify the risk level standards of each evaluation indicator, construct a shield tunnel excavation face instability disaster risk assessment system, and convert it into a Bayesian network topology structure; A Bayesian network prior probability calculation module 402 is used to calculate the membership of each disaster assessment index based on the digital feature calculation of the forward cloud generator and the data to be evaluated and convert it into a Bayesian network prior probability; A Bayesian network model conditional probability calculation module 403 is used to perform parameter learning on the historical data of shield tunnel excavation face instability disasters based on the historical data and the Bayesian network topology structure, and calculate the Bayesian network model conditional probability; A risk quantitative assessment result calculation module 404 is used to calculate a shield tunnel excavation face instability disaster risk quantitative assessment result based on the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topology structure; The shield tunneling control parameter decision module 405 is used to make adjustment decisions on the shield tunneling control parameters according to the quantitative assessment results of the instability disaster risk of the shield tunnel excavation face.

[0044] It should be noted here that the various modules in the embodiment of the present invention correspond one-to-one to the various steps in the above embodiment, and their specific implementation processes are the same, which will not be described in detail here.

[0045] Example 3 This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method for quantitatively assessing the risk of instability disasters in the excavation face of a shield tunnel as described above are implemented.

[0046] Example 4 A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps in the above-mentioned method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces.

[0047] Example 5 This embodiment provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps described above in the method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces.

[0048] Reference Figure 5 , a structural diagram of an electronic device in this embodiment. It should be noted that, Figure 5 The electronic device 500 shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0049] like Figure 5 As shown, electronic device 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 502 or programs loaded from a storage unit 508 into a random access memory (RAM) 503. Various programs and data required for system operation are also stored in RAM 503. Central processing unit 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0050] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, mouse, and the like; an output section 507 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 508 including devices such as a hard disk; and a communication section 509 including a network interface card such as a local area network (LAN) card or a modem. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. Removable media 511, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 510 as needed, so that computer programs read from the removable media can be installed in the storage section 508 as needed.

[0051] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509 and / or installed from the removable medium 511. When the computer program is executed by the central processing unit 501, the various functions defined in the apparatus of the present application are performed.

[0052] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams and the combination of processes and / or blocks in the flowcharts and / or block diagrams 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 flowcharts and / or block diagrams. 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.

[0053] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces, characterized in that: include: From the historical data of shield tunnel excavation face instability disasters, the shield tunnel excavation face instability disaster evaluation indicators were extracted, the risk level standards of each evaluation indicator were divided, and the shield tunnel excavation face instability disaster risk assessment system was constructed and converted into a Bayesian network topology structure. Based on the digital feature calculation of the forward cloud generator and the data to be evaluated, the membership degree of each disaster assessment index is calculated and converted into a Bayesian network prior probability; Based on the historical data of shield tunnel excavation face instability disasters and the Bayesian network topology structure, parameter learning is performed on the historical data of shield tunnel excavation face instability disasters, and the conditional probability of the Bayesian network model is calculated; Based on the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topology, the quantitative assessment results of the shield tunnel excavation face instability disaster risk are calculated; Based on the quantitative assessment results of the instability disaster risk of the shield tunnel excavation face, adjustments to the shield tunneling control parameters are made.

2. The method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces according to claim 1, characterized in that: The process of dividing the risk level standards of each evaluation indicator is as follows: hierarchically classify the selected risk assessment indicators and divide them into indicator layer, criterion layer and target layer according to the characteristics of the indicators; The target layer is the instability disaster risk of the shield tunnel excavation face; The criterion layers are excavation parameters and geological parameters; The index layer includes indicators of excavation parameters and geological parameters; the indicators of the excavation parameters include cutterhead speed, shield excavation speed, and excavation face support pressure; the indicators of the geological parameters include uniaxial compressive strength, rock integrity coefficient, ground stress level and tunnel burial depth.

3. The method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces according to claim 1, characterized in that: The Bayesian network prior probability is: ； ； ； in, is the consistency parameter, ranging from (0, 1], is the prior probability of the i-th indicator, is the membership degree of the i-th indicator, is the membership degree of the j-th state; is the degree of membership; Ex is the expected value, En is entropy, He is super entropy, C max is the upper limit of the interval, C min is the lower limit of the interval, k is a constant.

4. The method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces according to claim 1, wherein: According to the relationship between the historical data of shield tunnel excavation face instability disasters and the Bayesian network topology, the maximum likelihood estimation method is used to learn the parameters of the historical data, and combined with expert knowledge, the conditional probability table of the Bayesian network model is calculated.

5. The method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces according to claim 1, characterized in that: Using the conditional probability obtained through parameter learning and the Bayesian network topology, and the Bayesian inference method, combined with the current shield tunnel engineering conditions and geological data, the probability distribution of the current shield tunnel excavation face instability disaster risk level is forward inferred. The probability of shield excavation face instability disaster occurring is given by: in, is the probability of occurrence of shield excavation face instability disaster risk level Ti, is the probability of occurrence of tunneling parameter level C1 when shield excavation face instability disaster risk level Ti occurs, is the probability of occurrence of geological parameter level C2 when the shield excavation face instability disaster risk level Ti occurs, is the probability of occurrence of tunneling parameter level C1, is the probability of occurrence of geological parameter level C1.

6. The method for quantitatively assessing the risk of instability disasters in shield tunnel excavation faces according to claim 1, characterized in that: Based on the quantitative assessment results of the shield tunnel excavation face instability disaster risk, the following decisions are made to adjust the shield tunneling control parameters: Taking into account the geological conditions and risk assessment results, the shield tunneling speed, cutterhead speed, soil bin / mud pressure, synchronous grouting pressure and flow, and mud properties are dynamically controlled.

7. A shield tunnel excavation face instability disaster risk quantitative assessment system, characterized by: include: A Bayesian network topology structure construction module is used to extract shield tunnel excavation face instability disaster evaluation indicators from historical data on shield tunnel excavation face instability disasters, classify the risk level standards for each evaluation indicator, construct a shield tunnel excavation face instability disaster risk assessment system, and convert it into a Bayesian network topology structure; A Bayesian network prior probability calculation module is used to calculate the membership of each disaster assessment indicator based on the digital feature calculation of the forward cloud generator and the data to be evaluated and convert it into a Bayesian network prior probability; A Bayesian network model conditional probability calculation module is used to perform parameter learning on the historical data of shield tunnel excavation face instability disasters and calculate the Bayesian network model conditional probability based on the historical data of shield tunnel excavation face instability disasters and the Bayesian network topology structure; A risk quantitative assessment result calculation module is used to calculate the quantitative assessment results of shield tunnel excavation face instability disaster risk based on the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topology structure; The shield tunneling control parameter decision module is used to make adjustment decisions on the shield tunneling control parameters based on the quantitative assessment results of the instability disaster risk of the shield tunnel excavation face.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for quantitatively assessing the risk of instability disaster of the excavation face of a shield tunnel as described in any one of claims 1 to 6 are implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method for quantitatively assessing the risk of instability disaster of the excavation face of a shield tunnel as described in any one of claims 1 to 6 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for quantitatively assessing the risk of instability disaster of the excavation face of a shield tunnel as described in any one of claims 1 to 6 are implemented.

Images