Tunnel structure safety state reasoning method based on data driving

Abstract

The embodiment of the invention provides a tunnel structure safety state reasoning method based on data driving. The method is applied to tunnel engineering safety evaluation. The method comprises the following steps: establishing a crack description feature set; calculating lining residual bearing capacity corresponding to different crack types in the crack description feature set by utilizing an extended finite element model, and obtaining a lining residual bearing capacity result set; the crack description feature set is used as a training sample, and the lining residual bearing capacity result set is used as a label; training the lining residual bearing capacity prediction model according to the training sample; inputting the to-be-predicted lining residual bearing capacity corresponding crack description feature set into the trained lining residual bearing capacity prediction model to obtain predicted lining residual bearing capacity; the surrounding rock pressure is obtained by testing the supporting force of the tunnel support, the ratio of the surrounding rock pressure to the predicted residual bearing capacity of the lining is calculated, and the safety level of the lining and the safety level of the tunnel structure are obtained. Therefore, the comprehensiveness and accuracy of safety evaluation are improved, and the evaluation result is more objective and real.

Description

Technical Field

[0001] The present disclosure relates to the field of tunnel engineering safety assessment, and in particular to a data-driven tunnel structure safety status reasoning method. Background Art

[0002] Cracks have a great impact on the bearing capacity of the original structure of the tunnel. It is of certain practical significance to conduct a safety assessment of the main cracks in the tunnel and give a safety level to infer the safety status of the tunnel structure. At present, the reasoning method for the safety status of the tunnel structure mainly relies on traditional structural detection methods and finite element simulation analysis. Although these methods can evaluate the safety of the lining to a certain extent, they have disadvantages such as long detection cycle, high cost, and limited detection accuracy. Moreover, these methods often cannot reflect the impact of cracks on the overall safety of the lining, making it difficult to make an accurate assessment of the safety of the tunnel structure. Summary of the invention

[0003] The present disclosure provides a data-driven tunnel structure safety status reasoning method, device, equipment and storage medium.

[0004] According to a first aspect of the present disclosure, a data-driven tunnel structure safety state reasoning method is provided. The method comprises: Establish a fracture description feature set; Using the extended finite element model to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set, and obtaining a lining residual bearing capacity result set; Using the crack description feature set as training samples and the lining residual bearing capacity result set as annotations; training a lining residual bearing capacity prediction model according to the training samples; Input the crack description feature set corresponding to the lining residual bearing capacity to be predicted into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity; The surrounding rock pressure is obtained by testing the tunnel support force, and the ratio of the surrounding rock pressure to the predicted residual bearing capacity of the lining is calculated to obtain the lining safety level; the tunnel structure safety level is obtained based on the lining safety level.

[0005] In some possible implementations of the first aspect, using an extended finite element model to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set includes: The structural bearing capacity function and the crack bearing capacity functions corresponding to different crack types are determined by extending the finite element model, and the lining residual bearing capacity corresponding to different crack types is calculated based on the structural bearing capacity function and the crack bearing capacity functions corresponding to different crack types.

[0006] In some implementations of the first aspect, the fracture description feature set includes: Crack spatial location, crack direction, crack width, crack depth, crack side position, reinforced concrete strength, protective layer thickness, lining thickness.

[0007] In some implementations of the first aspect, the crack type is determined based on the crack spatial location, crack direction, crack width, crack depth, and crack side position, including transverse cracks, longitudinal cracks, and oblique cracks.

[0008] In some implementations of the first aspect, the structural bearing capacity function is a function of the relationship between the interface bearing capacity, the lining thickness, the protective layer thickness and the reinforced concrete strength; The crack bearing capacity function is a function of the spatial position of the crack, the crack direction, the crack width, the crack depth and the relationship between the crack lateral position.

[0009] In some implementations of the first aspect, the interface bearing capacity is calculated by the following formula: Nu = φ(fA+fsAs) / γ Among them, Nu represents the interface bearing capacity, φ represents the structural stability coefficient, γ represents the safety factor, f represents the tensile strength value of the structure, A represents the cross-sectional area of ​​the structure, fs represents the tensile strength value of the stirrup, and As represents the cross-sectional area of ​​the stirrup.

[0010] In some implementations of the first aspect, the ratio of the surrounding rock pressure to the predicted residual bearing capacity of the lining is negatively correlated with the lining safety level.

[0011] According to a second aspect of the present disclosure, a data-driven tunnel structure safety state reasoning device is provided. The device comprises: A fracture description feature set establishment module, used to establish a fracture description feature set; A lining residual bearing capacity result set acquisition module is used to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set by using an extended finite element model, and acquire a lining residual bearing capacity result set; A lining residual bearing capacity prediction model acquisition module is used to use the crack description feature set as a training sample and the lining residual bearing capacity result set as a label; and train the lining residual bearing capacity prediction model according to the training sample; A module for obtaining the predicted lining residual bearing capacity is used to input the crack description feature set corresponding to the lining residual bearing capacity to be predicted into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity; The lining safety level acquisition module is used to obtain the surrounding rock pressure by testing the tunnel support force, calculate the ratio of the surrounding rock pressure to the predicted lining residual bearing capacity, and obtain the lining safety level; and obtain the tunnel structure safety level according to the lining safety level.

[0012] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method described above.

[0013] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method as described above.

[0014] In the present disclosure, a crack description feature set is established; an extended finite element model is used to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set, and a lining residual bearing capacity result set is obtained; the crack description feature set is used as a training sample, and the lining residual bearing capacity result set is used as a label; a lining residual bearing capacity prediction model is trained according to the training sample; the crack description feature set corresponding to the lining residual bearing capacity to be predicted is input into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity; the surrounding rock pressure is obtained by testing the tunnel support force, and the ratio of the surrounding rock pressure to the predicted lining residual bearing capacity is calculated to obtain the lining safety level; the tunnel structure safety level is obtained according to the lining safety level. In this way, factors such as crack geometry, lining structure type and surrounding rock pressure are comprehensively considered, the comprehensiveness and accuracy of the safety assessment are improved, the influence of human factors is reduced, and the evaluation results are made more objective and true.

[0015] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which: Figure 1 A flow chart of a data-driven tunnel structure safety state reasoning method provided by an embodiment of the present disclosure is shown; Figure 2 A structural diagram of a tunnel structure safety state reasoning device based on data driving provided by an embodiment of the present disclosure is shown; Figure 3 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0018] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0019] In response to the problems in the background technology, the embodiments of the present disclosure provide a data-driven reasoning method and device for the safety status of a tunnel structure. Specifically, a crack description feature set is established; an extended finite element model is used to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set, and a lining residual bearing capacity result set is obtained; the crack description feature set is used as a training sample, and the lining residual bearing capacity result set is used as a label; a lining residual bearing capacity prediction model is trained according to the training sample; the crack description feature set corresponding to the lining residual bearing capacity to be predicted is input into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity; the surrounding rock pressure is obtained by testing the tunnel support force, the ratio of the surrounding rock pressure to the predicted lining residual bearing capacity is calculated, and the lining safety level is obtained; the tunnel structure safety level is obtained according to the lining safety level. In this way, factors such as crack geometry, lining structure type and surrounding rock pressure are comprehensively considered, the comprehensiveness and accuracy of the safety assessment are improved, the influence of human factors is reduced, and the evaluation results are made more objective and true.

[0020] The data-driven tunnel structure safety status reasoning method and device provided by the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings through specific embodiments.

[0021] Figure 1 A flow chart of a data-driven tunnel structure safety state reasoning method provided by an embodiment of the present disclosure is shown. The method 100 includes the following steps: S110, establishing a crack description feature set.

[0022] In some embodiments, the fracture description feature set includes: Crack spatial position f, crack direction θ, crack width i, crack depth k, crack side position g, reinforced concrete strength σ, protective layer thickness d, lining thickness h.

[0023] In some embodiments, the fracture description feature set further includes: Main reinforcement model, main reinforcement spacing, stirrup model, stirrup spacing; The strength of reinforced concrete can be determined based on the main reinforcement model, main reinforcement spacing, stirrup model, and stirrup spacing.

[0024] In some embodiments, the construction limit is determined according to the tunnel driving conditions, the intersection of the central axis of the construction limit and the line connecting the arch waists on both sides is taken as the origin O, the line connecting the origin O and the arch top is taken as the longitudinal axis, the tunnel direction from small mileage to large mileage is taken as the positive direction, and the line which is perpendicular to the origin O and rotated 90 degrees clockwise from the positive direction is taken as the transverse axis; The spatial position of the crack f represents the angle between the line connecting the crack tail end and the origin O and the horizontal axis; The crack strike θ represents the angle between the crack tip direction vector and the direction vector of the line connecting the origin O and the arch waist; The crack lateral position g indicates the location of the crack tail end, including the lining near rock side and the lining far rock side.

[0025] S120, using an extended finite element model to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set, and obtaining a lining residual bearing capacity result set.

[0026] In some embodiments, the crack type is determined based on the crack spatial location, crack direction, crack width, crack depth, and crack side position, including transverse cracks, longitudinal cracks, and oblique cracks.

[0027] In some embodiments, the extended finite element model is used to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set, including: Determine the structural bearing capacity function and the crack bearing capacity functions corresponding to different crack types by extending the finite element model, and calculate the lining residual bearing capacity corresponding to different crack types according to the structural bearing capacity function and the crack bearing capacity functions corresponding to different crack types; Specifically, the structural bearing capacity function is a function of the relationship between the interface bearing capacity, lining thickness, cover thickness and reinforced concrete strength, that is, the structural bearing capacity function is expressed as f (Nu,h,d,σ), Nu is the interface bearing capacity; The crack bearing capacity function is a function of the relationship between the crack spatial position, crack direction, crack width, crack depth and crack lateral position, that is, the crack bearing capacity function is expressed as f (f,θ,i,k,g); The residual bearing capacity N of the lining corresponding to different crack types is calculated by the following formula: N= f (Nu,h,d,σ)- f (f,θ,i,k,g) Among them, the extended finite element model can use different crack bearing capacity functions according to different crack types. f (f,θ,i,k,g), that is, the crack type corresponds to the crack bearing capacity function one by one.

[0028] In some embodiments, the interface bearing capacity Nu is calculated by the following formula: Nu = φ(fA+fsAs) / γ Among them, φ represents the structural stability coefficient, γ represents the safety factor, f represents the tensile strength value of the structure, A represents the cross-sectional area of ​​the structure, fs represents the tensile strength value of the stirrups, and As represents the cross-sectional area of ​​the stirrups.

[0029] In some embodiments, the extended finite element model uses extremely refined meshing technology to ensure that stress concentration phenomena are captured at key locations such as crack tips, thereby improving the calculation accuracy of the residual bearing capacity of the lining, enabling the evaluation method to more realistically simulate the impact of cracks on the lining structure, and providing reliable data for subsequent safety assessments.

[0030] S130, using the crack description feature set as training samples and the lining residual bearing capacity result set as annotations; and training a lining residual bearing capacity prediction model according to the training samples.

[0031] In some embodiments, a BP neural network is used to train a lining residual bearing capacity prediction model; During the training process of the lining residual bearing capacity prediction model, the gradient descent algorithm is used to optimize the parameters of the lining residual bearing capacity prediction model so that the model can better fit the actual data and improve the accuracy of the evaluation results. At the same time, cross-validation and regularization methods are used to effectively avoid model overfitting and improve the generalization ability of the model.

[0032] S140, inputting the crack description feature set corresponding to the lining residual bearing capacity to be predicted into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity.

[0033] S150, obtaining surrounding rock pressure by testing the tunnel support force, calculating the ratio of surrounding rock pressure to the predicted residual bearing capacity of the lining, and obtaining the lining safety level; and obtaining the tunnel structure safety level according to the lining safety level.

[0034] In some embodiments, the ratio of surrounding rock pressure to predicted lining residual bearing capacity is negatively correlated with the lining safety level; Specifically, the smaller the ratio of surrounding rock pressure to predicted lining residual bearing capacity, the higher the lining safety level, indicating that the lining structure is safer; Furthermore, the lining safety level corresponding to the ratio of surrounding rock pressure to predicted lining residual bearing capacity is less than or equal to 0.35 is the fourth safety level, indicating that the lining structure is safe; When the ratio of surrounding rock pressure to predicted lining residual bearing capacity is greater than or equal to 0.35 and less than 0.65, the corresponding lining safety level is the third safety level, indicating that the lining structure is relatively safe; When the ratio of surrounding rock pressure to predicted lining residual bearing capacity is greater than or equal to 0.65 and less than 0.85, the corresponding lining safety level is the second safety level, indicating that the lining structure is relatively dangerous; When the ratio of surrounding rock pressure to predicted lining residual bearing capacity is greater than or equal to 0.85, the corresponding lining safety level is the first safety level, indicating that the lining structure is dangerous.

[0035] In some embodiments, the lining safety level is positively correlated with the tunnel structure safety level, that is, the higher the lining safety level, the higher the tunnel structure safety level.

[0036] According to the embodiments of the present disclosure, a crack description feature set is established; the extended finite element model is used to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set, and the lining residual bearing capacity result set is obtained; the crack description feature set is used as a training sample, and the lining residual bearing capacity result set is used as a label; the lining residual bearing capacity prediction model is trained according to the training sample; the crack description feature set corresponding to the lining residual bearing capacity to be predicted is input into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity; the surrounding rock pressure is obtained by testing the tunnel support force, and the ratio of the surrounding rock pressure to the predicted lining residual bearing capacity is calculated to obtain the lining safety level; the tunnel structure safety level is obtained according to the lining safety level. In this way, factors such as crack geometry, lining structure type and surrounding rock pressure are comprehensively considered, the comprehensiveness and accuracy of the safety assessment are improved, the influence of human factors is reduced, and the evaluation results are made more objective and true.

[0037] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0038] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0039] Figure 2 The structure diagram of a device for evaluating the safety of a reinforced concrete lining of a tunnel with cracks provided by an embodiment of the present disclosure is shown. The device 200 comprises: The fracture description feature set establishing module 210 is used to establish a fracture description feature set.

[0040] In some embodiments, module 210 is specifically configured to: Fracture description feature set, including: Crack spatial location, crack direction, crack width, crack depth, crack side position, reinforced concrete strength, protective layer thickness, lining thickness.

[0041] The lining residual bearing capacity result set acquisition module 220 is used to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set using the extended finite element model, and acquire the lining residual bearing capacity result set.

[0042] In some embodiments, module 220 is specifically configured to: The type of crack is determined based on the spatial location of the crack, the direction of the crack, the width of the crack, the depth of the crack, and the side position of the crack, including transverse cracks, longitudinal cracks, and oblique cracks.

[0043] In some embodiments, module 220 is further configured to: The extended finite element model is used to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set, including: The structural bearing capacity function and the crack bearing capacity functions corresponding to different crack types are determined by extending the finite element model, and the lining residual bearing capacity corresponding to different crack types is calculated based on the structural bearing capacity function and the crack bearing capacity functions corresponding to different crack types.

[0044] In some embodiments, module 220 is further configured to: The structural bearing capacity function is a function of the relationship between the interface bearing capacity, lining thickness, cover thickness and reinforced concrete strength; The crack bearing capacity function is a function of the relationship between the crack spatial position, crack direction, crack width, crack depth and crack lateral position; among them, The interface bearing capacity is calculated by the following formula: Nu = φ(fA+fsAs) / γ Among them, Nu represents the interface bearing capacity, φ represents the structural stability coefficient, γ represents the safety factor, f represents the tensile strength value of the structure, A represents the cross-sectional area of ​​the structure, fs represents the tensile strength value of the stirrup, and As represents the cross-sectional area of ​​the stirrup.

[0045] The lining residual bearing capacity prediction model acquisition module 230 is used to use the crack description feature set as training samples and the lining residual bearing capacity result set as annotations; and train the lining residual bearing capacity prediction model according to the training samples.

[0046] The predicted lining residual bearing capacity acquisition module 240 is used to input the crack description feature set corresponding to the lining residual bearing capacity to be predicted into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity.

[0047] The lining safety level acquisition module 250 is used to obtain the surrounding rock pressure by testing the tunnel support force, calculate the ratio of the surrounding rock pressure to the predicted lining residual bearing capacity, and obtain the lining safety level; and obtain the tunnel structure safety level according to the lining safety level.

[0048] In some embodiments, module 250 is specifically configured to: The ratio of surrounding rock pressure to predicted lining residual bearing capacity is negatively correlated with the lining safety level.

[0049] Understandably, Figure 2 Each module / unit in the detection device 200 shown has the function of implementing each step in the detection method 100 provided in the embodiment of the present disclosure and can achieve its corresponding technical effect. For the sake of brevity, it will not be repeated here.

[0050] Figure 3 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. The electronic device 300 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 300 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0051] like Figure 3As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0052] A number of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0053] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0054] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0055] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0056] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0057] It should be noted that the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute method 100 and achieve the corresponding technical effect achieved by executing the method in the embodiment of the present disclosure. For the sake of concise description, they are not repeated here.

[0058] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0059] The systems and techniques described herein can be implemented in a computing system that includes a backend component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with the systems and techniques described herein). The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0060] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0061] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0062] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data-driven tunnel structure safety state reasoning method, characterized in that: include: Establish a fracture description feature set; Using the extended finite element model to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set, and obtaining a lining residual bearing capacity result set; Using the crack description feature set as training samples and the lining residual bearing capacity result set as annotations; training a lining residual bearing capacity prediction model according to the training samples; Input the crack description feature set corresponding to the lining residual bearing capacity to be predicted into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity; The surrounding rock pressure is obtained by testing the tunnel support force, and the ratio of the surrounding rock pressure to the predicted residual bearing capacity of the lining is calculated to obtain the lining safety level; The tunnel structure safety level is obtained according to the lining safety level.

2. The method according to claim 1, characterized in that The method of calculating the lining residual bearing capacity corresponding to different crack types in the crack description feature set by using the extended finite element model includes: The structural bearing capacity function and the crack bearing capacity functions corresponding to different crack types are determined by extending the finite element model, and the lining residual bearing capacity corresponding to different crack types is calculated based on the structural bearing capacity function and the crack bearing capacity functions corresponding to different crack types.

3. The method according to claim 2, characterized in that The fracture description feature set includes: Crack spatial location, crack direction, crack width, crack depth, crack side position, reinforced concrete strength, protective layer thickness, lining thickness.

4. The method according to claim 3, characterized in that The crack type is determined according to the crack spatial location, crack direction, crack width, crack depth, and crack side position, including transverse cracks, longitudinal cracks, and oblique cracks.

5. The method according to claim 3, characterized in that: The structural bearing capacity function is a function of the relationship between the interface bearing capacity, lining thickness, protective layer thickness and reinforced concrete strength; The crack bearing capacity function is a function of the relationship between the crack spatial position, crack direction, crack width, crack depth and crack lateral position.

6. The method according to claim 5, characterized in that The interface bearing capacity is calculated by the following formula: Nu = φ(fA+fsAs) / γ Among them, Nu represents the interface bearing capacity, φ represents the structural stability coefficient, γ represents the safety factor, f represents the tensile strength value of the structure, A represents the cross-sectional area of ​​the structure, fs represents the tensile strength value of the stirrup, and As represents the cross-sectional area of ​​the stirrup.

7. The method according to claim 1, characterized in that The ratio of the surrounding rock pressure to the predicted residual bearing capacity of the lining is negatively correlated with the lining safety level.

8. A device for evaluating the safety of a tunnel reinforced concrete lining containing cracks, characterized in that: include: A fracture description feature set establishment module, used to establish a fracture description feature set; A lining residual bearing capacity result set acquisition module is used to calculate the lining residual bearing capacity corresponding to different crack types in the crack description feature set by using an extended finite element model, and acquire a lining residual bearing capacity result set; A lining residual bearing capacity prediction model acquisition module is used to use the crack description feature set as a training sample and the lining residual bearing capacity result set as a label; and train the lining residual bearing capacity prediction model according to the training sample; A module for obtaining the predicted lining residual bearing capacity is used to input the crack description feature set corresponding to the lining residual bearing capacity to be predicted into the trained lining residual bearing capacity prediction model to obtain the predicted lining residual bearing capacity; The lining safety level acquisition module is used to obtain the surrounding rock pressure by testing the tunnel support force, calculate the ratio of the surrounding rock pressure to the predicted lining residual bearing capacity, and obtain the lining safety level; and obtain the tunnel structure safety level according to the lining safety level.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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