A cold region tunnel supporting structure life prediction method, system, device and medium

Abstract

The present application relates to a cold region tunnel supporting structure life prediction method, system, device and medium, which is based on a preset life evaluation criterion, a finite element analysis is carried out on the target tunnel by simulating the frost heaving pressure of the temperature field, the predicted carbonization life and the predicted failure life are obtained, then the correlation model of the carbonization life and the failure life is obtained, then the target carbonization life is obtained according to the preset empirical formula, and the target failure life is obtained based on the correlation model, and then the target complete life is obtained. Compared with the prior art, the correlation model is obtained by taking finite element analysis as a prior condition, and the correlation model can truly reflect the situation under the cold region condition because the frost heaving pressure is simulated during the finite element analysis. At this time, according to the correlation model, the failure life calculated based on the carbonization life obtained by the preset empirical formula can eliminate errors to the greatest extent, and then the life of the cold region tunnel supporting structure can be accurately predicted.

Description

Technical Field

[0001] This invention relates to the field of tunnel engineering technology, and in particular to a method, system, equipment and medium for predicting the life of tunnel support structures in cold regions. Background Technology

[0002] In the process of predicting the life of concrete structures in tunnels, the entire lifespan of concrete is divided into different life stages based on different life evaluation criteria. For example, in the durability-based life criterion, the lifespan of concrete is divided into the carbonation life, the protective layer cracking life, and the crack limit life, while in the safety-based life criterion, the lifespan of concrete is divided into the carbonation life and the safety factor limit life.

[0003] While the life stages vary among different life assessment criteria, the initial stage in most of them is the carbonation life. Carbonation life refers to the time it takes for concrete to carbonate from the start of carbonation to the formation of the protective layer. At this stage, the reinforcing steel in the concrete begins to show significant corrosion; therefore, carbonation life is also called depassivation time. Carbonation is the process by which carbonates in the concrete structure react with atmospheric carbon dioxide to form carbonates, which further exacerbates the corrosion of the reinforcing steel inside the concrete.

[0004] Generally, calculating the carbonation life allows us to calculate the duration of other life stages of concrete. However, for the support structure of tunnels in cold regions, in addition to conventional aging processes, the concrete structure is also affected by frost damage throughout its lifespan, such as frost heave and freeze-thaw cycles. This causes existing life prediction methods to produce errors when calculating the duration of other life stages based on the carbonation life, thus making it impossible to accurately predict the lifespan of tunnel support structures in cold regions. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, system, equipment and medium for predicting the life of tunnel support structures in cold regions, so as to solve the problem that the existing life prediction methods will produce errors when calculating the duration of other life stages based on carbonization life due to the influence of freezing damage in cold regions, and thus cannot accurately predict the life of tunnel support structures in cold regions.

[0006] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for predicting the lifespan of tunnel support structures in cold regions, comprising:

[0008] Based on the preset life evaluation criteria, the target tunnel is subjected to finite element analysis by simulating the freeze expansion pressure in the temperature field to obtain the predicted carbonization life and predicted failure life of the target tunnel.

[0009] Based on the predicted carbonization lifetime and the predicted failure lifetime, a correlation model is obtained to characterize the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel.

[0010] The target carbonization lifetime of the target tunnel is obtained according to a preset empirical formula, and the target failure lifetime of the target tunnel is obtained by calculating the target carbonization lifetime based on the correlation model.

[0011] The target complete lifespan of the target tunnel is obtained by summing the target carbonization lifespan and the target failure lifespan.

[0012] Furthermore, based on preset life evaluation criteria, the target tunnel is subjected to finite element analysis by simulating frost expansion pressure in a temperature field to obtain the predicted carbonization life and predicted failure life of the target tunnel, including:

[0013] Based on multiple preset life evaluation criteria, the target tunnel was subjected to multiple finite element analyses by simulating the freezing expansion pressure in the temperature field, resulting in multiple predicted carbonization lifetimes and predicted failure lifetimes corresponding to the predicted carbonization lifetimes of the target tunnel.

[0014] Furthermore, the association model obtained based on the predicted carbonization lifetime and the predicted failure lifetime to characterize the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel includes:

[0015] Regression analysis was performed on various predicted carbonization lifetimes and various predicted failure lifetimes to obtain the first correlation model;

[0016] The second correlation model is obtained based on the ratio of each predicted carbonization lifetime to the corresponding predicted failure lifetime.

[0017] The first association model and the second association model are combined to obtain the association model.

[0018] Furthermore, the association model includes:

[0019] t s (t c )=min(t s1 (t c ), t s2 (t c ))

[0020] Among them, t c For carbonization lifetime, t s (t c ) represents the carbonization lifetime t c Corresponding failure life t s , t s1 (t c) represents the carbonization lifetime t obtained through the first correlation model. c Corresponding failure life t s1 , t s2 (t c ) represents the carbonization lifetime t obtained through the second correlation model. c Corresponding failure life t s2 .

[0021] Furthermore, the first association model includes:

[0022]

[0023] Where, α i is the coefficient in the i-th power term of carbonization lifetime in the first correlation model, n is the highest power in the first correlation model, and β is the adjustment constant.

[0024] Furthermore, the second association model includes:

[0025] t s2 (t c )=K×t c

[0026] Where K is a proportionality coefficient, which is obtained by the ratio of each predicted carbonization lifetime to the corresponding predicted failure lifetime.

[0027] Furthermore, obtaining the target complete lifespan of the target tunnel based on the sum of the target carbonization lifetime and the target failure lifetime includes:

[0028] The mean of multiple predicted carbonization lifetimes and the sum of the mean of multiple predicted failure lifetimes are calculated to obtain the predicted full lifetime.

[0029] Compare the sum of the target carbonization lifetime and the target failure lifetime with the predicted full lifetime, and take the smaller one as the target full lifetime.

[0030] Secondly, the present invention also provides a life prediction system for tunnel support structures in cold regions, comprising:

[0031] The finite element prediction unit is used to perform finite element analysis on the target tunnel based on a preset life evaluation criterion by simulating the freezing expansion pressure in the temperature field, and to obtain the predicted carbonization life and predicted failure life of the target tunnel.

[0032] The correlation analysis unit is used to obtain a correlation model characterizing the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel based on the predicted carbonization lifetime and the predicted failure lifetime.

[0033] The first lifetime calculation unit is used to obtain the target carbonization lifetime of the target tunnel according to a preset empirical formula, and to calculate the target carbonization lifetime based on the correlation model to obtain the target failure lifetime of the target tunnel.

[0034] The second lifetime calculation unit is used to obtain the target complete lifetime of the target tunnel based on the sum of the target carbonization lifetime and the target failure lifetime.

[0035] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein,

[0036] Memory, used to store programs;

[0037] The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the cold-region tunnel support structure life prediction method in any of the above implementations.

[0038] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the cold-region tunnel support structure life prediction method in any of the above implementations.

[0039] This invention provides a method for predicting the lifespan of tunnel support structures in cold regions. First, based on preset lifespan evaluation criteria, a finite element analysis is performed on the target tunnel using a temperature field to simulate frost expansion pressure, yielding the predicted carbonization lifespan and predicted failure lifespan. The sum of the carbonization lifespan and failure lifespan constitutes the tunnel's complete lifespan. Then, based on the predicted carbonization lifespan and predicted failure lifespan, a correlation model is obtained to characterize the duration relationship between the carbonization lifespan and failure lifespan of the target tunnel. Next, a target carbonization lifespan is obtained based on a preset empirical formula, and the target failure lifespan is calculated using the correlation model. Finally, the target complete lifespan of the target tunnel is obtained based on the sum of the target carbonization lifespan and the target failure lifespan. Compared to existing technologies, this invention uses finite element analysis as a priori condition to obtain a correlation model that can characterize the relationship between carbonization lifespan and failure lifespan. Because the finite element analysis incorporates frost expansion pressure simulation, the correlation model includes the condition of frost damage, realistically reflecting the conditions in cold regions. Therefore, based on the correlation model and the failure life calculated using the carbonization life obtained through a preset empirical formula, the error can be eliminated to the greatest extent, thereby accurately predicting the life of the tunnel support structure in cold regions. Attached Figure Description

[0040] Figure 1 A flowchart of an embodiment of the method for predicting the life of tunnel support structures in cold regions provided by the present invention;

[0041] Figure 2 for Figure 1 A flowchart of a method according to an embodiment of step S102;

[0042] Figure 3 A system architecture diagram of an embodiment of the cold-region tunnel support structure life prediction system provided by the present invention;

[0043] Figure 4 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0044] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0045] Before describing specific implementation methods, some concepts in this document will be explained:

[0046] Life assessment criteria: Life assessment criteria are a set of standards or guidelines used to evaluate and determine the expected lifespan of structures, equipment, or materials. They provide a series of guiding principles and elements for judging and predicting the length of time a system or component can operate normally under specific service conditions. The following criteria are typically used when calculating the lifespan of concrete:

[0047] 1. Structural Safety Criteria: These criteria consider the safety performance of concrete structures, including load-bearing capacity and seismic performance. The lifespan of a concrete structure is directly related to its safety.

[0048] 2. Service Life Criterion: This criterion considers the service life of a concrete structure, that is, the time during which it can meet design requirements and expected service life. Service life may involve factors such as structural availability, maintenance, and repair.

[0049] 3. Durability Criteria: These criteria focus on the durability of concrete structures under long-term exposure to environmental conditions, such as resistance to freeze-thaw cycles, sulfide corrosion, and chloride ion corrosion. The durability criteria can be used to develop corresponding design requirements and life assessment methods based on specific circumstances.

[0050] Lifespan Stages: Based on different lifespan assessment criteria, the entire lifespan of concrete can be divided into different lifespan stages. For example, the lifespan of concrete can be divided into three stages in chronological order:

[0051] 1. Carbonation life: Carbonation is the process by which carbonates in concrete structures react with carbon dioxide in the atmosphere to form carbonates, which further exacerbates the corrosion of the steel reinforcement inside the concrete. Carbonation life refers to the time it takes for the steel reinforcement in the concrete to begin to show obvious corrosion.

[0052] 2. Crack life of the protective layer: The protective layer in a concrete structure is used to prevent the reinforcing steel from coming into contact with the external environment and to prevent corrosion. The crack life of the protective layer refers to the time it takes for cracks to appear in the protective layer. The appearance of cracks accelerates the oxidation of the reinforcing steel inside the concrete.

[0053] 3. Crack limit life: Crack limit life refers to the limitation on structural safety and service function when cracks in a concrete structure reach a certain width or degree of deformation. If this limit is exceeded, the structure may lose stability or fail to meet the service requirements.

[0054] In this invention, the life cycle of concrete is divided into two stages in chronological order: carbonation life and failure life. The definition of carbonation life is the same as above. The remaining lifespan in the entire lifespan is the failure life.

[0055] It is understood that other technical terms, English abbreviations, etc. appearing in the following text are all existing technologies, and those skilled in the art can understand their meaning based on the context. Due to space limitations, they will not be explained in detail in this article.

[0056] In the description of this application, "multiple" means two or more, unless otherwise expressly and specifically defined.

[0057] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0058] This invention provides a method, system, device, and storage medium for predicting the lifespan of tunnel support structures in cold regions, which will be described below.

[0059] Combination Figure 1 As shown, a specific embodiment of the present invention discloses a method for predicting the life of tunnel support structures in cold regions, comprising:

[0060] S101. Based on the preset life evaluation criteria, the target tunnel is subjected to finite element analysis by simulating the freezing expansion pressure in the temperature field to obtain the predicted carbonization life and predicted failure life of the target tunnel. The sum of the carbonization life and the failure life constitutes the complete life of the tunnel.

[0061] S102. Based on the predicted carbonization lifetime and the predicted failure lifetime, obtain a correlation model to characterize the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel.

[0062] S103. According to the preset empirical formula, the target carbonization lifetime of the target tunnel is obtained, and the target carbonization lifetime is calculated based on the correlation model to obtain the target failure lifetime of the target tunnel.

[0063] S104. The target complete lifespan of the target tunnel is obtained by summing the target carbonization lifespan and the target failure lifespan.

[0064] Compared to existing technologies, this invention uses finite element analysis as a priori condition to obtain a correlation model that characterizes the relationship between carbonization life and failure life. Because the finite element analysis incorporates frost expansion pressure simulation, the correlation model includes the condition of frost damage, thus realistically reflecting the situation under cold-region conditions. Therefore, based on the correlation model and the failure life calculated using the carbonization life obtained through a preset empirical formula, errors can be eliminated to the greatest extent, thereby accurately predicting the life of tunnel support structures in cold regions.

[0065] Furthermore, in a preferred embodiment, step S101 above, based on a preset life evaluation criterion, involves performing finite element analysis on the target tunnel using a temperature field to simulate frost expansion pressure, to obtain the predicted carbonization life and predicted failure life of the target tunnel. Specifically, this includes:

[0066] Based on multiple preset life evaluation criteria, the target tunnel was subjected to multiple finite element analyses by simulating the freezing expansion pressure in the temperature field, resulting in multiple predicted carbonization lifetimes and predicted failure lifetimes corresponding to the predicted carbonization lifetimes of the target tunnel.

[0067] This embodiment obtains multiple sets of predicted carbonization lifetime and predicted failure lifetime through various different lifetime evaluation criteria, in order to better explore the relationship between the two in the target environment, making the final correlation model more objective and accurate.

[0068] The present invention also provides a more detailed embodiment to more clearly illustrate step S101 above:

[0069] In this embodiment, two life evaluation criteria based on reliability and safety factor are used for prediction. Reliability can represent the crack level of the tunnel support structure, while safety factor represents the load-bearing capacity of the tunnel support structure.

[0070] Taking the reliability criterion as an example, the frost expansion pressure and freeze-thaw cycle effects on the support structure are simulated through a cyclic temperature field. Based on the similarity rule, a finite element model of the target tunnel is established and analyzed using the Monte Carlo sampling method to obtain the reliability-time curve. Then, the time corresponding to the reliability decreasing below a certain set threshold is selected as the carbonization life. Subsequently, the time corresponding to the reliability decreasing below another set threshold is further selected to obtain the failure life. Life prediction based on the safety factor criterion is similar.

[0071] In the finite element modeling and analysis described above, the temperature field was established by incorporating frost heave force. Therefore, the predicted carbonization life and predicted failure indicate the extent to which frost damage in cold regions affects the lifespan. The specific implementation details of the above process are existing technologies that can be understood by those skilled in the art, and therefore will not be elaborated upon in this paper.

[0072] Understandably, in practice, it is also possible to use only one preset life evaluation criterion for prediction, and finally obtain a predicted carbonization life value and a predicted failure life value. In this case, the ratio between the two can be directly used as a correlation model, and then the failure life can be directly calculated using this ratio after the carbonization life has been calculated.

[0073] Furthermore, in combination Figure 2 As shown, in a preferred embodiment, step S102, obtaining a correlation model characterizing the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel based on the predicted carbonization lifetime and the predicted failure lifetime, specifically includes:

[0074] S201. Regression analysis was performed on multiple predicted carbonization lifetimes and multiple predicted failure lifetimes to obtain the first correlation model;

[0075] S202. Based on the ratio of each predicted carbonization lifetime to the corresponding predicted failure lifetime, a second correlation model is obtained;

[0076] S203. Combining the first association model and the second association model, the association model is obtained.

[0077] The purpose of the above process is to explore the relationship between carbonization lifetime and failure lifetime in a variety of different ways in order to achieve objective and accurate results.

[0078] Specifically, in a preferred embodiment, the association model includes:

[0079] t s (t c )=min(t s1 (t c ), t s2 (tc ))

[0080] Among them, t c For carbonization lifetime, t s (t c ) represents the carbonization lifetime t c Corresponding failure life t s , t s1 (t c ) represents the carbonization lifetime t obtained through the first correlation model. c Corresponding failure life t s1 , t s2 (t c ) represents the carbonization lifetime t obtained through the second correlation model. c Corresponding failure life t s2 .

[0081] The significance of the aforementioned correlation model lies in calculating two failure lifetimes using the first and second correlation models respectively, and then selecting the smaller failure lifetime value as the final desired failure lifetime to achieve a more conservative estimate. Understandably, the method for determining the final failure lifetime based on the two failure lifetimes can be flexibly set according to the actual situation, such as taking the average.

[0082] Furthermore, in a preferred embodiment, in step S201, a multinomial regression method is used to analyze the association between the two lifespans, and the first association model includes:

[0083]

[0084] Where, α i is the coefficient in the i-th power term of carbonization lifetime in the first correlation model, n is the highest power in the first correlation model, and β is the adjustment constant.

[0085] Multinomial regression is a regression analysis method used to fit the nonlinear relationship between independent and dependent variables. In multinomial regression, the powers of the independent variables are introduced as predictor variables, forming multiple polynomial terms. The advantages of multinomial regression include its ability to fit nonlinear data, its good flexibility, and its ability to interpret parameters. In this embodiment, multinomial regression can be considered as nonlinear regression.

[0086] Furthermore, in a preferred embodiment, the second association model includes:

[0087] t s2 (t c )=K×t c

[0088] Where K is a proportionality coefficient, obtained based on the ratio of each predicted carbonization lifetime to the corresponding predicted failure lifetime. In this embodiment, the proportionality coefficient is the mean of the ratios of each predicted carbonization lifetime to the corresponding predicted failure lifetime. It is understood that in practice, the proportionality coefficient can also be obtained using other standards based on multiple ratios, such as selecting the maximum or minimum value. The second correlation model in this embodiment can be regarded as linear regression.

[0089] This embodiment combines linear regression and nonlinear regression to analyze the prediction of carbonization life and failure life, combining the advantages of both analyses. Finally, a correlation model is established by jointly using the first correlation model and the second correlation model to achieve the goal of accurately calculating the failure life.

[0090] Furthermore, in a preferred embodiment, step S103, obtaining the target carbonization lifetime of the target tunnel according to a preset empirical formula, and calculating the target carbonization lifetime based on the correlation model to obtain the target failure lifetime of the target tunnel, specifically includes:

[0091] The target carbonization lifetime is calculated using the following formula;

[0092] t c =min(t1, t2)

[0093]

[0094]

[0095] Where t1 is the depassivation time of the reinforcing steel under carbonization conditions, i.e., the time when the inner reinforcing steel of the tunnel lining structure begins to corrode due to carbonization; t2 is the depassivation time of the reinforcing steel under chloride ion erosion conditions, i.e., the time when the outer reinforcing steel of the tunnel lining structure begins to corrode due to chloride ion intrusion; c is the thickness of the protective layer of the target tunnel; k is the carbonization coefficient; and C s () represents the surface chloride ion concentration value of the target tunnel, erf() is the error function, and D Cl Let [Cl] be the chloride ion diffusion coefficient. - [ ] represents the critical concentration of chloride ions on the surface of the target tunnel.

[0096] It is understood that the above formula is only a preferred embodiment for illustrative purposes. In practice, the preset empirical formula used to calculate the carbonization lifetime can be flexibly set according to the specific circumstances. After calculating the target carbonization lifetime, the target failure lifetime can be calculated according to the above correlation model. After obtaining the target carbonization lifetime and the target failure lifetime, step S104 can be performed. The sum of the two can be directly used as the final target complete lifetime, or it can be further optimized according to the specific circumstances.

[0097] For example, in a preferred embodiment, step S104, obtaining the target complete lifespan of the target tunnel based on the sum of the target carbonization lifetime and the target failure lifetime, specifically includes:

[0098] The mean of multiple predicted carbonization lifetimes and the sum of the mean of multiple predicted failure lifetimes are calculated to obtain the predicted full lifetime.

[0099] Compare the sum of the target carbonization lifetime and the target failure lifetime with the predicted full lifetime, and take the smaller one as the target full lifetime.

[0100] The above process establishes the predicted full life by taking the average value. The predicted full life is then compared with the calculated target carbonization life and target failure life to determine the final target full life. This makes the final calculated target full life more rigorous and makes the tunnel safer and more reliable when it is put into use.

[0101] To better implement the life prediction method for cold-region tunnel support structures in this invention, based on the existing method, please refer to the relevant documentation. Figure 3 , Figure 3 This is a schematic diagram of an embodiment of the cold-region tunnel support structure life prediction system provided by the present invention. The cold-region tunnel support structure life prediction system 300 provided by this embodiment includes:

[0102] The finite element prediction unit 310 is used to perform finite element analysis on the target tunnel based on a preset life evaluation criterion by simulating the freezing expansion pressure in the temperature field, and to obtain the predicted carbonization life and predicted failure life of the target tunnel, wherein the sum of the carbonization life and the failure life constitutes the complete life of the tunnel.

[0103] The correlation analysis unit 320 is used to obtain a correlation model that characterizes the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel based on the predicted carbonization lifetime and the predicted failure lifetime.

[0104] The first lifetime calculation unit 330 is used to obtain the target carbonization lifetime of the target tunnel according to a preset empirical formula, and to calculate the target carbonization lifetime based on the correlation model to obtain the target failure lifetime of the target tunnel.

[0105] The second lifetime calculation unit 340 is used to obtain the target complete lifetime of the target tunnel based on the sum of the target carbonization lifetime and the target failure lifetime.

[0106] It should be noted that the system 300 provided in the above embodiments can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding content in the above method embodiments, and will not be repeated here.

[0107] Please see Figure 4 , Figure 4 This is a schematic diagram of the electronic device provided in an embodiment of the present invention. Based on the above-described method for predicting the lifespan of tunnel support structures in cold regions, the present invention also provides a corresponding device 400 for predicting the lifespan of tunnel support structures in cold regions, i.e., the aforementioned electronic device. The device 400 can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, or server. The device 400 includes a processor 410, a memory 420, and a display 430. Figure 4 Only some components of the cold-region tunnel support structure life prediction device are shown. However, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0108] In some embodiments, the memory 420 may be an internal storage unit of the cold-region tunnel support structure life prediction device 400, such as a hard disk or memory of the cold-region tunnel support structure life prediction device 400. In other embodiments, the memory 420 may be an external storage device of the cold-region tunnel support structure life prediction device 400, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the cold-region tunnel support structure life prediction device 400. Furthermore, the memory 420 may include both internal storage units and external storage devices of the cold-region tunnel support structure life prediction device 400. The memory 420 is used to store application software and various types of data installed on the cold-region tunnel support structure life prediction device 400, such as the program code for installing the cold-region tunnel support structure life prediction device 400. The memory 420 may also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 420 stores a life prediction program 440 for cold-region tunnel support structures, which can be executed by the processor 410 to implement the life prediction method for cold-region tunnel support structures according to various embodiments of this application.

[0109] In some embodiments, processor 410 may be a central processing unit (CPU), microprocessor or other data processing chip, used to run program code stored in memory 420 or process data, such as executing a method for predicting the life of tunnel support structures in cold regions.

[0110] In some embodiments, display 430 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 430 is used to display information from the cold-region tunnel support structure life prediction device 400 and to display a user interface for visualization. Components 410-430 of the cold-region tunnel support structure life prediction device 400 communicate with each other via a system bus.

[0111] In one embodiment, when the processor 410 executes the cold-region tunnel support structure life prediction program 440 in the memory 420, the steps in the cold-region tunnel support structure life prediction method described above are implemented.

[0112] This embodiment also provides a computer-readable storage medium storing a life prediction program for cold-region tunnel support structures, which, when executed by a processor, can implement the steps in the above embodiments.

[0113] This invention provides a method for predicting the lifespan of tunnel support structures in cold regions. First, based on preset lifespan evaluation criteria, a finite element analysis is performed on the target tunnel using a temperature field to simulate frost expansion pressure, yielding the predicted carbonization lifespan and predicted failure lifespan. The sum of the carbonization lifespan and failure lifespan constitutes the tunnel's complete lifespan. Then, based on the predicted carbonization lifespan and predicted failure lifespan, a correlation model is obtained to characterize the duration relationship between the carbonization lifespan and failure lifespan of the target tunnel. Next, a target carbonization lifespan is obtained based on a preset empirical formula, and the target failure lifespan is calculated using the correlation model. Finally, the target complete lifespan of the target tunnel is obtained based on the sum of the target carbonization lifespan and the target failure lifespan. Compared to existing technologies, this invention uses finite element analysis as a priori condition to obtain a correlation model that can characterize the relationship between carbonization lifespan and failure lifespan. Because the finite element analysis incorporates frost expansion pressure simulation, the correlation model includes the condition of frost damage, thus realistically reflecting the conditions in cold regions. Therefore, based on the correlation model and the failure life calculated using the carbonization life obtained through a preset empirical formula, the error can be eliminated to the greatest extent, thereby accurately predicting the life of the tunnel support structure in cold regions.

[0114] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the lifespan of tunnel support structures in cold regions, characterized in that, include: Based on the preset life evaluation criteria, the target tunnel is subjected to finite element analysis by simulating the freeze expansion pressure in the temperature field to obtain the predicted carbonization life and predicted failure life of the target tunnel. Based on the predicted carbonization lifetime and the predicted failure lifetime, a correlation model is obtained to characterize the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel. The target carbonization lifetime of the target tunnel is obtained according to a preset empirical formula, and the target failure lifetime of the target tunnel is obtained by calculating the target carbonization lifetime based on the correlation model. The target complete lifespan of the target tunnel is obtained by summing the target carbonization lifespan and the target failure lifespan.

2. The method for predicting the lifespan of tunnel support structures in cold regions according to claim 1, characterized in that, The method, based on a preset life evaluation criterion, involves performing finite element analysis on the target tunnel using a temperature field to simulate frost expansion pressure, thereby obtaining the predicted carbonization life and predicted failure life of the target tunnel. This includes: Based on multiple preset life evaluation criteria, the target tunnel was subjected to multiple finite element analyses by simulating the freeze-thaw pressure in the temperature field, resulting in multiple predicted carbonization lifetimes and predicted failure lifetimes corresponding to the predicted carbonization lifetimes of the target tunnel.

3. The method for predicting the lifespan of tunnel support structures in cold regions according to claim 2, characterized in that, The association model obtained based on the predicted carbonization lifetime and the predicted failure lifetime to characterize the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel includes: Regression analysis was performed on multiple predicted carbonization lifetimes and multiple predicted failure lifetimes to obtain the first correlation model; The second correlation model is obtained based on the ratio of each predicted carbonization lifetime to the corresponding predicted failure lifetime. The first association model and the second association model are combined to obtain the association model.

4. The method for predicting the lifespan of tunnel support structures in cold regions according to claim 3, characterized in that, The association model includes: t s (t C )=min(t s1 (t c ),t s2 (t c )) Among them, t c For carbonization lifetime, t s (t c ) represents the carbonization lifetime t c Corresponding failure life t s , t s1 (t c ) represents the carbonization lifetime t obtained through the first correlation model. c Corresponding failure life t s1 , t s2 (t c ) represents the carbonization lifetime t obtained through the second correlation model. c Corresponding failure life t s2 .

5. The method for predicting the lifespan of tunnel support structures in cold regions according to claim 4, characterized in that, The first association model includes: Where, α i is the coefficient in the i-th power term of carbonization lifetime in the first correlation model, n is the highest power in the first correlation model, and β is the adjustment constant.

6. The method for predicting the lifespan of tunnel support structures in cold regions according to claim 5, characterized in that, The second association model includes: t s2 (t c )=K×t c Where K is a proportionality coefficient, which is obtained by the ratio of each predicted carbonization lifetime to the corresponding predicted failure lifetime.

7. The method for predicting the lifespan of tunnel support structures in cold regions according to claim 2, characterized in that, The step of obtaining the target complete lifespan of the target tunnel based on the sum of the target carbonization lifespan and the target failure lifespan includes: The mean of multiple predicted carbonization lifetimes and the sum of the mean of multiple predicted failure lifetimes are calculated to obtain the predicted full lifetime. Compare the sum of the target carbonization lifetime and the target failure lifetime with the predicted full lifetime, and take the smaller one as the target full lifetime.

8. A life prediction system for tunnel support structures in cold regions, characterized in that, include: The finite element prediction unit is used to perform finite element analysis on the target tunnel based on a preset life evaluation criterion by simulating the frost expansion pressure in the temperature field, and to obtain the predicted carbonization life and predicted failure life of the target tunnel. The correlation analysis unit is used to obtain a correlation model characterizing the duration relationship between the carbonization lifetime and the failure lifetime of the target tunnel based on the predicted carbonization lifetime and the predicted failure lifetime. The first lifetime calculation unit is used to obtain the target carbonization lifetime of the target tunnel according to a preset empirical formula, and to calculate the target carbonization lifetime based on the correlation model to obtain the target failure lifetime of the target tunnel. The second lifetime calculation unit is used to obtain the target complete lifetime of the target tunnel based on the sum of the target carbonization lifetime and the target failure lifetime.

9. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the cold region tunnel support structure life prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, are capable of implementing the steps in the cold-region tunnel support structure life prediction method according to any one of claims 1 to 7.

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