Road surface frozen soil foundation temperature field change influence evaluation method and device and storage medium

Abstract

The invention relates to the technical field of infrastructure operation and maintenance, in particular to a pavement frozen soil foundation temperature field change influence evaluation method and device and a storage medium. According to meteorological, engineering and geological frozen soil data obtained in advance, a three-dimensional unsteady-state finite element model containing pavement plates and joints is established; setting a joint hot infiltration effect mathematical model; the unfrozen water content parameters are verified by using monitoring data, and the model simulation is sufficient. Setting a future climate boundary condition according to the climate change scene; setting pavement dynamic boundary conditions according to the pavement operation and maintenance data; respectively inputting the boundary conditions into the calibrated model, carrying out long-term simulation, and predicting the evolution of a foundation temperature field; calculating a frozen soil thermal stability index; finally, a mechanical model is coupled, pavement risk early warning is achieved, and the accuracy of the prediction result is high. The method realizes full-chain, refined and quantitative evaluation of the thermal stability of the frozen soil foundation under the pavement from mechanism to decision, and has practicability.

Description

Technical Field

[0001] This invention relates to the field of infrastructure operation and maintenance technology, specifically to a method, equipment, and storage medium for evaluating the impact of changes in the temperature field of permafrost foundations. Background Technology

[0002] Northeast my country is characterized by widespread high-temperature, high-ice-content permafrost. Airports built in this region, such as Mohe Airport, have runways submerged in highly unstable, high-temperature permafrost. Their operational safety is heavily constrained by the thermal stability of the underlying permafrost foundation. After several years of operation, varying degrees of uneven settlement and tectonic plate misalignment have emerged, threatening flight safety. Cement concrete pavements are widely used due to their high strength and durability, but their wide, low-resistance, and heat-absorbing structure and joint characteristics also bring unique thermophysical problems. These significantly alter the energy exchange balance between the Earth's surface and the atmosphere, leading to drastic changes in the temperature field of the underlying permafrost foundation. This results in a series of engineering geological problems such as thawing settlement, frost heave deformation, and more. The pavement acts like a giant "thermal blanket" absorbing solar energy, while the joints become "shortcuts" for the rapid infiltration of moisture and heat. This causes uneven thermal disturbance in the underlying permafrost foundation, forming localized "thawing pads," which in turn lead to uneven settlement, tectonic plate warping, and misalignment, seriously threatening the long-term safety and serviceability of airport runways.

[0003] However, current evaluation methods for this problem mostly treat the pavement as a homogeneous continuum, resulting in insufficient simulation of the local thermal state below the joints; the determination of model parameters largely depends on indoor tests or empirical values, leading to significant uncertainty in the prediction results; the generated evaluations mostly focus on the analysis of a single temperature index or melting depth, and the evaluation results are not strongly correlated with the specific mechanical response of the pavement structure and maintenance decisions, thus lacking practicality. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, equipment and storage medium for evaluating the impact of changes in the temperature field of permafrost foundation on road surfaces, so as to solve the problems of insufficient simulation of actual conditions, insufficient accuracy of prediction results and insufficient practicality of the evaluation methods in the prior art.

[0005] According to a first aspect of the present invention, a method for evaluating the impact of changes in the temperature field of permafrost foundation is provided, comprising: Based on the pre-acquired meteorological data, engineering data, and geological permafrost data, a three-dimensional unsteady finite element model containing the geometric shape of pavement panels and joints is established, and a mathematical model of joint thermal wetting effect is set in the three-dimensional unsteady finite element model. Based on the collected historical geothermal monitoring data, a Bayesian-based inverse analysis was performed on the three-dimensional unsteady finite element model to obtain the unfrozen water content parameters of the key soil layers of the foundation. The uncertainty of the unfrozen water content parameters was quantified to calibrate the three-dimensional unsteady finite element model. Climate change scenarios related to the pavement area were extracted from the IPCC CMIP6 database, and future climate boundary conditions were set based on these scenarios. Dynamic boundary conditions for the pavement were also set based on pavement operation and maintenance data. The future climate boundary conditions and pavement dynamic boundary conditions are respectively input into the calibrated three-dimensional unsteady finite element model to perform transient coupled simulation of the future prediction period and obtain the spatiotemporal data of the temperature field. Multiple key evaluation indicators are extracted from the spatiotemporal data of the temperature field, and the thermal stability index of frozen soil is calculated based on the preset weight coefficients and multiple key evaluation indicators. The spatiotemporal data of the temperature field and the thermal stability index of the frozen soil are input into the pre-constructed pavement structural mechanical response model to calculate the uneven settlement and stress redistribution of the pavement, thereby obtaining a risk matrix. Based on the risk matrix, the pavement is divided into different risk warning levels.

[0006] Preferably, a mathematical model of joint thermal wetting effect is set in the three-dimensional unsteady finite element model, including: In the three-dimensional unsteady finite element model, the seam element is treated as a material with equivalent thermophysical parameters, which are dynamically calculated by the following formula:

[0007] Among them, C eff For equivalent volumetric heat capacity, C dry C is the volumetric heat capacity of the joint material in its dry state. wet λ represents the volumetric heat capacity of the joint material in its wet state; eff λ is the thermal conductivity. dry Let λ be the thermal conductivity of the joint material in its dry state. wet The thermal conductivity of the joint material in a wet state; The porosity of the joint material; For time The equivalent saturation of the change.

[0008] Preferably, the governing equations of the three-dimensional unsteady finite element model include: Transient heat conduction equation considering phase change and water convection, water migration equation based on unsaturated seepage theory, and elastoplastic constitutive relation considering freeze-thaw retraction; The transient heat conduction equation considering phase change and moisture convection is specifically as follows:

[0009] Where T is temperature and t is time; The density of water, This is the specific heat capacity of water; is the velocity vector of water seepage; L is the latent heat of phase change of water; The density of ice; This represents the volumetric ice content.

[0010] The preferred, extracted key evaluation indicators include: Average annual ground temperature, upper limit depth of permafrost, thickness of active layer and thaw plate development index; The annual average ground temperature refers to the annual average ground temperature at different depths and its variation over time. The upper limit of the permafrost depth is the depth at which the highest annual ground temperature is 0°C; The active layer thickness is the difference between the depth at which the annual minimum ground temperature is 0°C and the upper limit depth of permafrost. The melting plate development index is a comprehensive indicator that quantifies the scale of melting soil under the pavement.

[0011] Preferably, the method further includes: The thermal stability index of the frozen soil is calculated using the following formula:

[0012] in, To predict the average ground temperature of the frozen soil at the end of the period, T0 is its initial value; ΔH 50 H0 represents the initial depth of the predicted upper limit of permafrost during the forecast period; TDI 50 To predict the melting plate development index at the end of the period; TDI ref This serves as a reference value for the melting plate development index; , , These are the weighting coefficients, and ; To avoid small quantities with a denominator of zero.

[0013] Preferably, historical ground temperature monitoring data is collected, including: Historical ground temperature monitoring data is collected by a distributed optical fiber sensor array deployed along the centerline of the road surface and the soil surface areas on both sides; the monitoring depth of the distributed optical fiber sensor array is from 0.5 meters below the road surface to at least 5 meters below the top surface of the stable permafrost layer, and the total depth is not less than 20 meters.

[0014] Preferably, dynamic boundary conditions for the pavement are set based on pavement operation and maintenance data, including: Based on the long-term effects of vehicle loads on the pavement, the equivalent thermal effects of chemical erosion by de-icing fluid, and the periodic changes in dynamic albedo, dynamic boundary conditions for the pavement are set.

[0015] Preferably, the pavement is divided into different risk warning levels, including: Based on the upper limit of permafrost descent, the extent of thaw plate development, and the estimated pavement settlement, the pavement is divided into low-risk, medium-risk, and high-risk areas. Based on the pavement risk zone division, a pavement risk zoning plan is generated.

[0016] According to a second aspect of the present invention, an evaluation device for the impact of changes in the temperature field of permafrost foundation is provided, comprising: The main controller and the memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0017] According to a third aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the method described in any of the preceding embodiments.

[0018] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: The technical solution presented in this invention establishes a three-dimensional unsteady finite element model including pavement panels and joints based on pre-acquired meteorological, engineering, and geological permafrost data, and sets up a mathematical model of joint thermal infiltration effect. By introducing a dynamic mathematical model of the "joint thermal infiltration effect," the periodic infiltration of water in the joints and its localized intensified heating effect on the underlying foundation temperature field are realistically reproduced in numerical simulation. Monitoring data is used to verify the unfrozen water content parameter, transforming the evaluation conclusion from "a definite value" to "a probability distribution," resulting in richer and more scientific decision-making information. Future climate boundary conditions are set based on the aforementioned climate change scenario; dynamic boundary conditions for the pavement are set based on pavement operation and maintenance data; these boundary conditions are input into the calibrated model for long-term simulation to predict the evolution of the foundation temperature field; and then the permafrost thermal stability index is calculated. This index integrates multi-dimensional information, providing a unified and quantitative stability benchmark. Finally, a coupled mechanical model is used to achieve pavement risk early warning, with high accuracy in the prediction results. Ultimately, the pavement was divided into different risk warning levels, making the complex permafrost degradation problem measurable, zoned, and controllable, greatly enhancing the engineering practical value of the method.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0021] Figure 1 This is a schematic diagram illustrating the steps of an exemplary method for evaluating the impact of temperature field changes in permafrost foundations on road surfaces. Figure 2 This is a schematic diagram of a refined numerical model of an airport pavement-permafrost foundation that incorporates the joint thermal wetting effect according to an exemplary embodiment. Figure 3 This is a schematic diagram illustrating the Bayesian inversion results of model parameters according to an exemplary embodiment; Figure 4 This is a schematic diagram illustrating the upper limit depth of permafrost and the predicted thaw plate below the future runway centerline, according to an exemplary embodiment. Figure 5 This is a graded zoning map for early warning of service performance risks of pavement structures, illustrated according to an exemplary embodiment. Detailed Implementation

[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0023] In one embodiment, Figure 1 This is a schematic diagram illustrating the steps of a method for evaluating the impact of temperature field changes in permafrost foundations according to an exemplary embodiment. See also... Figure 1 This paper provides a method for evaluating the impact of changes in the temperature field of permafrost foundation on road surfaces, including: Step S11: Based on the pre-acquired meteorological data, engineering data, and geological permafrost data, establish a three-dimensional unsteady finite element model containing the geometric shape of pavement panels and joints, and set a mathematical model of joint thermal wetting effect in the three-dimensional unsteady finite element model.

[0024] The three-dimensional unsteady finite element model is a refined thermal-hydraulic-mechanical coupling model of pavement-permafrost foundation, which is a physical and mathematical model that can truly reflect the complex thermophysical processes of the pavement system.

[0025] Taking airport pavement as an example, meteorological data refers to long-term meteorological observation data of the target airport area (temperature, humidity, wind speed, solar radiation, precipitation, snow thickness, etc.) and future climate scenario data (such as SSP path data from the IPCC CMIP6 series). Engineering data refers to the structural design drawings of the airport pavement (materials and thicknesses of the surface layer, base layer, and subbase layer), the thermophysical parameters of the pavement materials (thermal conductivity, specific heat capacity, density, absorptivity), and the aircraft load spectrum. Geological and permafrost data refers to the engineering geological survey report, the layering information of the foundation soil, the thermophysical parameters of each soil layer (thermal conductivity, volumetric heat capacity, latent heat of phase change, unfrozen water content curve), initial ground temperature field data, and the distribution of underground ice content.

[0026] After acquiring and preprocessing the aforementioned data, geometric modeling was performed using this data. Taking the cement concrete runway of Mohe Airport in a permafrost region as an example, a three-dimensional unsteady finite element model was established. The model precisely includes the geometry of the pavement panels (e.g., a standard 4.5m × 5m panel) and the joints. The joint width is set according to the design drawings (usually 1cm).

[0027] Meanwhile, this embodiment introduces a mathematical model of the seam thermal wetting effect into the three-dimensional unsteady finite element model, while traditional models ignore the seam or treat it as a homogeneous material. This embodiment considers the seam unit as a special porous medium, whose thermophysical parameters (volume heat capacity) are... and thermal conductivity It is dynamic and changes.

[0028] In a preferred embodiment, in the three-dimensional unsteady finite element model, the seam element is treated as a material with equivalent thermophysical parameters, which are dynamically calculated by the following formula:

[0029] Among them, C eff For equivalent volumetric heat capacity, C dry C is the volumetric heat capacity of the joint material in its dry state. wet λ represents the volumetric heat capacity of the joint material in its wet state; eff λ is the thermal conductivity. dry Let λ be the thermal conductivity of the joint material in its dry state. wet The thermal conductivity of the joint material in a wet state; The porosity of the joint material; For time The equivalent saturation of the change.

[0030] The function is driven by a simplified hydrological model that takes rainfall event data as input and simulates the accumulation, infiltration, and evaporation of rainwater in joints. This allows the model to dynamically simulate how, after summer rainfall, the joints become saturated with water, drastically increasing their thermal conductivity and thus becoming channels for rapid downward conduction of "heat sources."

[0031] It should be noted that the governing equations of the three-dimensional unsteady finite element model include: the transient heat conduction equation considering phase change and water convection, the water migration equation based on unsaturated seepage theory, and the elastoplastic constitutive relation considering freeze-thaw settlement.

[0032] Regarding heat transfer, a transient heat conduction equation considering phase change and moisture convection is adopted, specifically:

[0033] Where T is temperature and t is time; The density of water, This is the specific heat capacity of water; is the velocity vector of water seepage; L is the latent heat of phase change of water; The density of ice; This represents the volumetric ice content.

[0034] This equation not only takes into account the latent heat of phase change The release / absorption also takes into account water migration. The resulting thermal convection effect is crucial for simulating the rapid flow of moisture in seams and the resulting heat transport.

[0035] Regarding water migration, a water migration equation based on the unsaturated seepage theory is adopted, specifically an unsaturated seepage model based on the Richards equation, which describes the migration of liquid water driven by matrix potential and temperature potential gradients.

[0036] In terms of the stress field, an elastoplastic constitutive model that considers frost heave and thaw settlement (such as a combination of the Drucker-Prager model and the expansion model) is adopted, and the phase transition and water content change calculated from the temperature field and the moisture field are used as the body force source terms of the stress field.

[0037] Furthermore, for the boundaries and initial conditions of the three-dimensional unsteady finite element model, the upper boundary of the model is the pavement surface and the natural ground surface, and a comprehensive heat exchange boundary is applied that takes into account solar radiation, long-wave radiation, convective heat transfer, and evaporation and condensation. The lower boundary is set as the geothermal heat flow boundary. The initial conditions are the steady-state or quasi-steady-state geothermal field obtained by interpolation through geothermal monitoring data.

[0038] Step S12: Based on the collected historical geothermal monitoring data, perform a reverse analysis based on Bayesian theory on the three-dimensional unsteady finite element model to obtain the unfrozen water content parameters of the key soil layers of the foundation, quantify the uncertainty of the unfrozen water content parameters, and calibrate the three-dimensional unsteady finite element model.

[0039] This step involves inverse model calibration and uncertainty quantification. In practice, long-term geothermal monitoring data obtained from a distributed temperature sensing system for airport runways in permafrost regions can be used to inversely calibrate the three-dimensional unsteady finite element model, with the aim of ensuring the model's predictive reliability.

[0040] In one embodiment, collecting historical ground temperature monitoring data includes: collecting historical ground temperature monitoring data through a distributed optical fiber sensor array deployed on the centerline of the road surface and the soil surface areas on both sides; the monitoring depth of the distributed optical fiber sensor array is from 0.5 meters below the road surface to at least 5 meters below the top surface of the stable permafrost layer, and the total depth is not less than 20 meters.

[0041] In practice, taking airports as an example, historical ground temperature monitoring data is obtained using a distributed fiber optic temperature measurement system deployed along the runway of Mohe Airport in a permafrost region, representing at least 2-3 complete years of spatiotemporal data. Monitoring points need to cover key locations, such as the pavement slab, directly below joints, and pavement shoulders.

[0042] The inversion method employs Markov Chain Monte Carlo (MCMC) sampling. The key parameters to be inverted are those in the unfrozen water content relationship (e.g., α, b), which are extremely sensitive to the temperature field but difficult to measure accurately. Bayesian inversion not only provides optimal estimates of these parameters... MAP (Maximum A posteriori probability estimation) also quantifies the uncertainty of parameters in the form of a posterior probability distribution. This means that subsequent long-term predictions can be based on this uncertainty propagation analysis to provide confidence intervals for the prediction results, significantly improving the reliability of the evaluation conclusions.

[0043] Step S13: Extract climate change scenarios related to the pavement area from the IPCC CMIP6 database, set future climate boundary conditions based on the climate change scenarios, and set dynamic boundary conditions for the pavement based on pavement operation and maintenance data.

[0044] This step involves setting up multi-path future scenarios and pavement maintenance conditions.

[0045] The first step is setting future climate boundary conditions. The IPCC CMIP6 database refers to the CMIP6 (Sixth Coupled Model Intercomparison Programme) climate model data released by the Intergovernmental Panel on Climate Change (IPCC). Climate data from this database under various shared socio-economic pathways relevant to the Mohe region are extracted, such as SSP1-2.6 (low carbon), SSP2-4.5 (moderate carbon), and SSP5-8.5 (high carbon) scenarios. The data undergoes statistical downscaling to generate daily or monthly series of temperature, precipitation, and snow cover for the forecast period (e.g., the next 50 years).

[0046] Regarding the setting of dynamic boundary conditions for the pavement, it should be noted that the dynamic boundary conditions for the pavement are set based on the long-term effects of vehicle loads on the pavement, the equivalent thermal effects of chemical erosion by de-icing fluid, and the periodic changes in dynamic albedo.

[0047] The long-term effects of vehicle loads on the pavement, taking an airport as an example, can be equated to the long-term micro-vibration disturbance effect on the foundation soil structure by considering the dynamic load of aircraft take-off and landing as an equivalent effect. This may affect the soil's density and hydraulic conductivity. In the model, this can be approximated by periodically fine-tuning the material properties.

[0048] The equivalent thermal effect of chemical erosion by de-icing fluid refers to the reduction in freezing temperature of frozen soil caused by commonly used de-icing fluids, determined through indoor tests, and equated to an additional heat flux density acting on the pavement surface or a shift in the phase transition point of the soil.

[0049] The dynamic albedo periodic variation refers to the model setting, based on airport operation and maintenance procedures, that during winter (October to April of the following year), due to the use of de-icing fluid and intermittent snow accumulation, the pavement albedo periodically increases from a baseline value of 0.30-0.35 to 0.50-0.60; after comprehensive snow removal in spring, the albedo returns to the baseline value. This dynamic setting more realistically reflects the actual heat absorption process of the pavement.

[0050] Step S14: Input the future climate boundary conditions and pavement dynamic boundary conditions into the calibrated three-dimensional unsteady finite element model respectively, and perform transient coupling simulation of the future prediction period to obtain the spatiotemporal data of the temperature field.

[0051] This step involves long-term transient simulation and temperature field prediction, with the aim of predicting the spatiotemporal evolution of the temperature field of the foundation beneath the airport's cement concrete runway.

[0052] Multiple sets of boundary conditions set in S13 are input into the model calibrated and with uncertainties quantified in S12 to perform transient coupled simulations for the future prediction period (e.g., the next 50 years). Each scenario may be run multiple times (e.g., sampling from the posterior distribution of parameters) to capture the uncertainty of the prediction. The final output is a massive amount of spatiotemporal data of the temperature field.

[0053] Step S15: Extract multiple key evaluation indicators from the temperature field spatiotemporal data, and calculate the thermal stability index of frozen soil based on the preset weight coefficients and multiple key evaluation indicators.

[0054] It should be noted that the extracted key evaluation indicators include: annual average ground temperature, upper limit depth of permafrost, active layer thickness, and thaw disk development index. The annual average ground temperature refers to the annual average ground temperature at different depths and its variation over time; the upper limit depth of permafrost is the depth at which the highest annual ground temperature is 0℃; the active layer thickness is the difference between the depth at which the lowest annual ground temperature is 0℃ and the upper limit depth of permafrost; the thaw disk development index is a comprehensive indicator quantifying the scale of thawed soil beneath the pavement, and its calculation can be based on integration of the thaw disk's extent, depth, and degree of temperature exceedance.

[0055] The "melting plate" refers to the area of ​​foundation soil under the pavement where the ground temperature is consistently above 0°C. Its development trend is quantitatively evaluated by calculating the annual growth rate of the melting plate area, the annual growth rate of the depth, and the ratio of the melting plate volume to the projected area of ​​the pavement.

[0056] The Thermal Stability Index (PTSI) is a comprehensive index used to quantify the rate of decline of the upper limit of permafrost, the increase in the thickness of the active layer, and to identify and evaluate the development range, depth and penetration trend of the "thaw plate" under the runway pavement, thus assessing the overall degree of thermal stability degradation of the permafrost foundation.

[0057] It should be noted that the thermal stability index of the frozen soil is calculated using the following formula:

[0058] in, To predict the average ground temperature of the frozen soil at the end of the period, T0 is its initial value; ΔH 50 H0 represents the initial depth of the predicted upper limit of permafrost during the forecast period; TDI 50 To predict the melting plate development index at the end of the period; TDI ref This serves as a reference value for the melting plate development index; , , These are the weighting coefficients, and ; To avoid small quantities with a denominator of zero.

[0059] The Permafrost Thermal Stability Index (PTSI) avoids the limitations of single-indicator assessments. This index integrates information from three core aspects: first, temperature state, reflecting the increase in the average temperature of the permafrost; second, degradation depth, reflecting the degree of subsidence of the upper limit of permafrost; and third, spatial catastrophism, reflecting the severity of local thaw plate development. By assigning different weights, customized assessments can be performed for different airports based on their importance and tolerance. The PTSI ultimately provides a scalar value from 0 to 1 (or a specific range), intuitively representing the thermal stability level of the permafrost foundation (e.g., stable, unstable, unstable).

[0060] Step S16: Input the spatiotemporal data of the temperature field and the thermal stability index of the frozen soil into the pre-constructed pavement structural mechanical response model, calculate the uneven settlement and stress redistribution of the pavement, obtain the risk matrix, and divide the pavement into different risk warning levels according to the risk matrix.

[0061] This step involves risk warning and classification of pavement structure service performance, assessing the risks of differential settlement, warping, and misalignment of pavement panels caused by the development of "melting plates" and uneven melting settlement.

[0062] This step begins with a mechanical response analysis, using the predicted spatial distribution of the melting plate and the thickness of the active layer as inputs to drive a simplified finite element model of the pavement structure. The pavement stress, strain, and uneven settlement are calculated under the combined effects of aircraft loads and softening of the foundation support stiffness. A two-dimensional or multi-dimensional risk matrix is ​​then established. For example, PTSI (characterizing the degree of thermal degradation) and maximum differential settlement are used as the horizontal axes, with the actual values ​​represented on the vertical axis. Based on the risk matrix, the runway is classified into different risk warning levels.

[0063] In one embodiment, the pavement is divided into different risk warning levels, including: dividing the pavement into low-risk areas, medium-risk areas and high-risk areas based on the upper limit of permafrost descent, the development range of the thaw plate and the estimated amount of pavement settlement; and generating a pavement risk zoning plan based on the pavement risk zone division.

[0064] For example, Level I (Low Risk / Green): High PTSI, small differential settlement. Routine monitoring is recommended. Level II (Medium Risk / Yellow): Moderate PTSI, controllable differential settlement. Enhanced monitoring and preventative maintenance (such as joint sealing) are recommended. Level III (High Risk / Red): Low PTSI, significant differential settlement. Engineering measures are recommended, such as installing heat pipes on the shoulder and setting up insulation layers. If differential settlement has jeopardized safety, major repairs or foundation reinforcement should be considered. For areas at "Medium Risk" and "High Risk" levels, the proposed treatment recommendations include, but are not limited to: joint sealing reinforcement, setting up an insulation layer under the pavement, or fundamental treatment measures such as foundation modification.

[0065] The final result is an intuitive runway risk zoning plan, which clearly marks the area range of different risk levels and includes specific treatment strategy suggestions for each level, providing airport management with precise "one map" decision support.

[0066] The technical solution shown in this embodiment has the following technical effects: A major breakthrough in mechanism simulation: By introducing a dynamic mathematical model of the joint thermal infiltration effect, the periodic infiltration of water in the joint and its local enhanced heating effect on the temperature field of the underlying foundation are realistically reproduced in numerical simulation, solving the core defects of traditional models in this regard.

[0067] Improved predictive reliability: By employing a Bayesian inversion framework, not only is the model calibrated, but more importantly, the uncertainty of key parameters is quantified and this uncertainty is propagated into long-term predictions. This transforms the evaluation conclusion from "a definite value" to "a probability distribution," resulting in richer and more scientific decision-making information. Setting up multiple future scenarios for climate and operational maintenance, and using observational data for inversion verification, makes the model closer to the actual airport operation scenario, leading to more reliable prediction results.

[0068] The comprehensiveness and intuitiveness of the evaluation system: The created "Permafrost Thermal Stability Index (PTSI)" integrates multi-dimensional information, providing a unified and quantitative stability benchmark. Combined with the final risk zoning map, it makes the complex permafrost degradation problem measurable, zoned, and controllable, greatly enhancing the engineering practical value of the method.

[0069] The entire technology chain is integrated: from refined modeling, uncertainty inversion, and multi-scenario prediction to comprehensive index evaluation and final risk classification and control, a complete, closed-loop, and logically rigorous technical system has been formed. It is applicable to the site selection and design stage of new airports and the operation and maintenance diagnosis stage of existing airports, which improves the construction, operation safety and efficiency of airports and provides reliable protection for the full life cycle operation safety of airport runways in permafrost areas.

[0070] The following is an illustration using a real-world example. The airport is located at 52 degrees North latitude in a permafrost region. The runway is a concrete structure, 45 meters wide. The site is an island-like permafrost region, with the foundation soil primarily consisting of completely weathered tuff-like clay interspersed with numerous ice lenses. The initial permafrost depth is approximately 1.5-2.0 meters, and the average annual ground temperature is approximately -1.0℃ to -0.5℃, classifying it as a high-temperature, highly unstable permafrost.

[0071] First, multi-source data is collected and preprocessed to build a refined model.

[0072] The model was built using COMSOL Multiphysics software. (See [link]) Figure 2The model dimensions are 100m long (along the runway direction, containing 20 panels) and 48m wide (covering the runway and part of the shoulder). In the Greater Khingan Mountains region where the airport is located, the annual temperature variation depth of the permafrost layer is generally between 8m and 18m; therefore, the soil thickness in the model is taken as 20m. A precise 4.5m × 5m network of pavement panels and joints was established. Detailed pavement structure design drawings and thermal properties of each layer of materials were obtained. Through geological surveys, soil stratification, thermal physical parameters, and initial ground temperature field within a 20-meter depth were obtained.

[0073] See Table 1 for pavement structure layer parameters and Table 2 for soil layer parameters.

[0074] Table 1

[0075] Table 2

[0076] The joint material is set as silicone sealant. In the finite element model, the joint element is treated as a special material with equivalent thermophysical parameters. The convective heat transfer coefficient and permeability coefficient are set to be significantly higher than those of the normal pavement area to realize the mathematical model of "joint thermal wetting effect".

[0077] The equivalent volumetric heat capacity and equivalent thermal conductivity are dynamically calculated using formulas.

[0078] The upper boundary of the model is the pavement surface, considering the combined effects of solar radiation, long-wave radiation, convective heat transfer, and evaporation / condensation. The lower boundary is set as a constant heat flux or constant temperature boundary. The side boundaries can be set as adiabatic or periodic boundaries according to actual conditions. The model inputs detailed thermophysical parameters of each pavement layer (surface layer, base layer, subbase layer) and foundation soil layer. In particular, this invention innovatively considers the "joint thermal wetting effect" of pavement joints, simulating enhanced thermal convection caused by rainwater infiltration by locally modifying the boundary conditions.

[0079] Then, step S12 is executed to collect measured ground temperature data and perform inverse model calibration. This example uses fiber optic temperature measurement data from 2018 to 2021 (sampling frequency once per day, depth 0.5-20m) for inversion. The parameters a and b of the unfrozen water content in the completely weathered tuff clay layer are selected (assuming the relationship is...). The parameters to be inverted are ). The MCMC algorithm (such as the Metropolis-Hastings algorithm) is used for 100,000 samplings. The inversion results are as follows: Figure 3 As shown, the posterior distribution is concentrated around a=0.08 and b=0.45, and the relatively wide prior distribution narrows significantly, indicating that the observed data provide strong constraints. The root mean square error between the simulated temperature and the observed temperature of the model running with the posterior mean parameters is less than 0.15℃.

[0080] Perform step S13 to set up multiple future scenarios for climate, operation, and maintenance. Nearly 15 years of operation at airports in permafrost regions have revealed an upward trend in permafrost temperatures due to human activities and the greenhouse effect. Therefore, the model primarily operates under two climate scenarios: Scenario SSP2-4.5: The average annual temperature in the Greater Khingan Mountains region, where the airport is located, will rise by approximately 1.8°C over the next 50 years.

[0081] Scenario SSP5-8.5: The average annual temperature in the Greater Khingan Mountains region, where the airport is located, will rise by approximately 3.0°C over the next 50 years.

[0082] Simultaneously, considering operational and maintenance scenarios, dynamic albedo boundaries and the equivalent heat effect of de-icing fluid are applied in both scenarios. Based on airport flight schedules, the annual usage of de-icing fluid is estimated, and its equivalent heat flux density is determined to be +0.5 W / m² through indoor testing. 2 (Because it lowers the freezing temperature, it is equivalent to heating).

[0083] Table 3 shows the total solar radiation for each month based on data from the meteorological station in the Daxinganling region where the airport is located. Using a program to define total solar radiation according to Table 3, the dynamic albedo of solar radiation for cement concrete pavement is 0.30-0.35, for natural ground is 0.35-0.40, for silicone joint materials is 0.25-0.30, and for de-icing fluid is 0.45-0.50. The convective heat transfer coefficient between the surface and the atmosphere is mainly affected by wind speed; the thermal convection coefficient was calculated.

[0084] Table 3

[0085] Long-term simulations and index extraction were performed. A 50-year transient simulation was conducted for each scenario. Figure 4 The study shows that under the SSP5-8.5 scenario, the upper limit of permafrost below the runway centerline continuously decreases from an initial 1.8 meters to approximately 6.5 meters after 50 years, and the prediction curve includes a confidence interval due to parameter uncertainties. It also vividly demonstrates that the thaw pan preferentially develops along longitudinal joints, eventually forming a "rib-like" continuous thaw zone, with the TDI index rapidly increasing from 0 to 0.45.

[0086] The thermal stability index (PTSI) of frozen soil was calculated. T0 = -0.8℃, H0 = 1.8m, and TDI was... ref =0.5, weight taken =0.4, =0.3, =0.3. The calculated PTSI (Potential Thermal Stability Index) for permafrost under the SSP5-8.5 scenario in year 50 is 0.32, which is classified as "unstable".

[0087] Risk warning and classification were conducted. Mechanical analysis showed that differential settlement was most significant at the intersection of longitudinal and transverse joints, with a maximum value of 12.3 cm. A risk zoning map was drawn based on the Permafrost Thermal Stability Index (PTSI) and differential settlement. (See attached map.) Figure 5 The results showed that the area 600m inside the southeast end of the runway was classified as "high-risk (Level III)". The evaluation report recommended: implementing a heat pipe cooling system for the Level III area; conducting an immediate detailed survey; and preparing for foundation replacement or reinforcement treatment during the next major overhaul cycle. This invention provides precise and quantitative scientific basis for the future maintenance planning of Mohe Airport in the permafrost region.

[0088] In another embodiment, a device for evaluating the impact of changes in the temperature field of permafrost foundation is provided, comprising: The main controller and the memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0089] In another embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the method described in any of the preceding embodiments.

[0090] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0091] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0092] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0093] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0094] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0095] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0096] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0097] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0098] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the influence of changes in the temperature field of a pavement frost foundation, characterized by The method comprises the following steps: According to the pre-acquired meteorological data, engineering data and permafrost data, a three-dimensional non-steady-state finite element model containing the geometric shape of the pavement slab and the joint is established, and a joint thermal infiltration effect mathematical model is set in the three-dimensional non-steady-state finite element model; According to the collected historical ground temperature monitoring data, a reverse analysis based on the Bayesian theory is performed on the three-dimensional non-steady-state finite element model, and the unfrozen water content parameters of the key soil layers of the foundation are inversed to quantify the uncertainty of the unfrozen water content parameters and calibrate the three-dimensional non-steady-state finite element model; From the IPCC CMIP6 database, the climate change scenarios related to the pavement area are extracted, and future climate boundary conditions are set according to the climate change scenarios; pavement dynamic boundary conditions are set according to pavement operation and maintenance data; The future climate boundary conditions and the pavement dynamic boundary conditions are input into the calibrated three-dimensional non-steady-state finite element model, and transient coupling simulation in the future prediction period is performed to obtain the temperature field spatiotemporal data; From the temperature field spatiotemporal data, a plurality of key evaluation indexes are extracted, and a permafrost thermal stability index is calculated according to a preset weight coefficient and the plurality of key evaluation indexes; The temperature field spatiotemporal data and the permafrost thermal stability index are input into a pre-constructed pavement structure mechanical response model to calculate pavement uneven settlement and stress redistribution, and a risk matrix is obtained, and the pavement is divided into different risk warning levels according to the risk matrix.

2. The method of claim 1, wherein, The joint thermal infiltration effect mathematical model is set in the three-dimensional non-steady-state finite element model, which comprises: In the three-dimensional non-steady-state finite element model, the joint element is treated as a material with equivalent thermal physical parameters, and the equivalent thermal physical parameters are dynamically calculated by the following formula: where C eff is the equivalent volumetric heat capacity, C dry is the volumetric heat capacity of the dry state of the joint material, C wet is the volumetric heat capacity of the wet state of the joint material, respectively; λ eff is the thermal conductivity, λ dry is the thermal conductivity of the dry state of the joint material, λ wet is the thermal conductivity of the wet state of the joint material; is the porosity of the joint material; is the equivalent saturation varying over time .

3. The method of claim 2, wherein, The control equation of the three-dimensional non-steady-state finite element model comprises: a transient heat conduction equation considering phase change and water convection, a water migration equation based on unsaturated seepage theory, and an elastic-plastic constitutive relationship considering frost heaving and thawing settlement; The transient heat conduction equation considering phase change and water convection is specifically: where T is temperature, t is time; is the density of water, is the specific heat capacity of water; is the water infiltration velocity vector; L is the latent heat of phase change of water; is the density of ice; is the volume ice content.

4. The method of claim 1, wherein, The plurality of key evaluation indexes extracted comprise: annual average ground temperature, permafrost upper limit depth, active layer thickness, and thawing disc development index; The annual average ground temperature is the annual average value of the ground temperature at different depths and its change over time; The permafrost upper limit depth is the depth at which the annual maximum ground temperature is 0℃; The active layer thickness is the difference between the depth at which the annual minimum ground temperature is 0℃ and the permafrost upper limit depth; The thawing disc development index is a comprehensive index quantifying the scale of the thawing soil body under the pavement.

5. The method of claim 4, wherein, Further comprising: The permafrost thermal stability index is calculated by the following formula: wherein, To predict the average ground temperature of the permafrost body at the end of the period, T0 is the initial value thereof; ΔH 50 H0is the initial depth of the permafrost table for the prediction period; TDI 50 TDI is the thawing disc development index at the end of the prediction period; TDI ref TDI is the reference value of the thawing disc development index; , , is a weighting factor, and ; is a small quantity to avoid division by zero.

6. The method of claim 1, wherein, The historical ground temperature monitoring data are collected, which comprises: The historical ground temperature monitoring data are collected by a distributed optical fiber sensing array arranged on the center line of the pavement and the soil surface area on both sides; the monitoring depth of the distributed optical fiber sensing array is from 0.5 meters below the pavement to at least 5 meters below the top surface of the stable permafrost layer, and the total depth is not less than 20 meters.

7. The method of claim 1, wherein, The pavement dynamic boundary conditions are set according to the pavement operation and maintenance data, which comprises: According to the equivalent thermal effects of the long-term effects of vehicle loads on the pavement, the chemical erosion of deicing liquid, and the periodic changes of dynamic albedo, the pavement dynamic boundary conditions are set.

8. The method of claim 1, wherein, The pavement is divided into different risk warning levels, including: According to the depth of the permafrost upper limit, the development range of the thawing disc and the estimated amount of pavement settlement, the pavement is divided into low-risk areas, medium-risk areas and high-risk areas; According to the pavement risk area division, a pavement risk zoning plan is generated.

9. A device for evaluating the influence of changes in the temperature field of a pavement frost foundation, characterized in that It comprises: A main control unit and a memory connected to the main control unit; The memory has program instructions stored therein; The main control unit is configured to execute the program instructions stored in the memory to perform the method of any one of claims 1-8.

10. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by a processor to implement the method of any one of claims 1-8.

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