Method and system for determining macro-microscopic deterioration quantitative relationship of clay under freeze-thaw cycles

Abstract

The application discloses a method and system for determining the quantitative relationship between macro-microscopic deterioration of clay under the action of freeze-thaw cycles, and comprises the following steps: sample preparation and freeze-thaw cycle test, macro-mechanical and permeability test, microscopic structure test and image analysis, establishment of the quantitative relationship between strength attenuation and porosity change, construction of a permeability coefficient prediction model based on macropore content, and model verification and application steps; through indoor test and microscopic structure analysis, the quantitative relationship between the microscopic structure evolution of clay under the action of freeze-thaw cycles and the macroscopic property deterioration is clarified.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering and geological disaster prevention technology in cold regions, and in particular to a method and system for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles. Background Technology

[0002] Freeze-thaw cycles are a key factor inducing slope instability and failure in cold regions. In permafrost and seasonally frozen soil areas widely distributed in Northeast, Northwest, and the Qinghai-Tibet Plateau of my country, slope instability disasters occur frequently, affecting not only the safe operation of infrastructure but also posing a serious threat to the lives and property of local people. Therefore, clarifying the instability and failure mechanisms of slopes under freeze-thaw cycles is of great significance for the prevention and control of slope disasters in cold regions.

[0003] The slope instability process under freeze-thaw cycles exhibits significant hysteresis, cumulativeity, and suddenness, with an extremely complex evolution mechanism that is difficult to accurately simulate and predict using traditional methods. Under the influence of freeze-thaw cycles, the physical and mechanical parameters of slope soil undergo significant changes, exhibiting obvious spatiotemporal nonlinearity and uncertainty. Existing research shows that freeze-thaw cycles cause significant changes in the internal microstructure of clay, leading to the deterioration of its macroscopic mechanical and permeability properties. However, the quantitative relationship between the macroscopic properties and microstructure of clay under freeze-thaw cycles is currently inconsistent. Existing studies have yielded inconsistent laws governing the evolution of physical and mechanical parameters, and a quantitative model that directly correlates microstructural evolution parameters with macroscopic performance degradation is lacking. This makes it difficult to predict macroscopic performance changes through microstructural observation in practical engineering, and also fails to provide a reliable theoretical basis for long-term stability assessment of cold-region engineering projects. Therefore, establishing a quantitative relationship between the macroscopic and microscopic deterioration of freeze-thawed clay is of significant theoretical and engineering value for the design, safety assessment, and disaster early warning of cold-region engineering projects. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a method and system for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles. Through indoor experiments and microstructure analysis, the quantitative relationship between the evolution of clay microstructure and the deterioration of macro-properties under freeze-thaw cycles is clarified.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles includes the following steps: Step 1: Prepare clay samples with different freeze-thaw cycles, set multiple freeze-thaw cycle gradients, and conduct freeze-thaw cycle tests under closed system conditions. Each freeze-thaw cycle includes freezing at a negative temperature and thawing at a positive temperature. Step 2: Perform macroscopic mechanical and permeability tests on clay samples that have undergone different freeze-thaw cycles, determine their breaking strength, elastic modulus, cohesion and saturated permeability coefficient, and record the evolution data of macroscopic mechanical and permeability characteristics under each freeze-thaw cycle. Step 3: Conduct microstructure tests on clay samples that have undergone different freeze-thaw cycles, obtain microstructure images or pore distribution data, and use image processing software to extract pore structure parameters, including macropore content, mesopore content, micropore content, average pore size and porosity. Statistically analyze the evolution of microstructure parameters under different freeze-thaw cycles. Step 4: Define the intensity damage variable ,in The breaking strength of the unfrozen specimen. For experience N Destructive strength of specimens after one freeze-thaw cycle; analysis of damage variables. The quantitative relationship between strength damage variable and porosity increment Δn is established by nonlinear regression, which yields an empirical relationship between the strength damage variable and porosity increment. Step 5: Analyze the evolution characteristics of pore structure during freeze-thaw cycles, determine the variation law of macropore content with the number of freeze-thaw cycles, and establish a permeability coefficient prediction model based on macropore content and the improved Kozeny-Carman equation. Step 6: Use independent experimental data to verify the empirical relationships and prediction models established in Steps 4 and 5, and apply the verified models to the long-term stability assessment of slopes, roadbeds or dams in cold regions.

[0006] Preferably, in step 1, the freeze-thaw cycle number gradient is set to 0, 1, 3, 5, and 10 times; the freezing temperature is -20°C, the thawing temperature range is 20°C, and each freeze-thaw cycle is 24 hours, of which freezing and thawing each last 12 hours.

[0007] Preferably, in step 2, the macroscopic mechanical test adopts a triaxial compression test or a direct shear test, and the confining pressure settings include a low confining pressure of 50-100 kPa and a high confining pressure of 200 kPa.

[0008] Preferably, in step 2, the permeability characteristic test adopts a variable head permeability test or a triaxial permeability test to analyze the evolution law of the permeability coefficient under different confining pressure conditions.

[0009] Preferably, in step 3, the microstructure test is performed using a scanning electron microscope or a mercury porosimetry test; the SEM image is binarized using Image-Pro Plus or ImageJ image processing software, and pores and soil particles are distinguished by setting a grayscale threshold, and pore morphology parameters and distribution characteristics are calculated.

[0010] Preferably, in step 3, the porosity threshold is determined based on the soil type; for clay, a pore size greater than 10 mm is used. The pores are macropores, the pores with a diameter of 1-10 μm are mesopores, and the pores with a diameter of less than 1 μm are small voids.

[0011] Preferably, in step 4, the empirical relationship between the strength damage variable and the porosity increment is a linear relationship: =aN+b; Where a and b are the fitting parameters obtained by nonlinear regression of experimental data.

[0012] Preferably, in step 5, the permeability coefficient prediction model is a modified KC equation:

[0013] in, K The saturated permeability coefficient after freeze-thaw cycles. Liquid limit, denoted as porosity after freeze-thaw cycles, where porosity represents the macropore content. c and d are model parameters calibrated using experimental data.

[0014] Preferably, in step 6, the model validation uses root mean square error and relative error as evaluation indicators, and the relative error between the predicted value and the measured value is controlled within 30%.

[0015] In addition, this invention also discloses a system for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles, used to implement the above-mentioned method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles, comprising: Freeze-thaw cycle test module: used to subject clay samples to different numbers of freeze-thaw cycles; Macroscopic performance testing module: used to determine the breaking strength, elastic modulus, cohesion and permeability coefficient of the sample; Microstructure analysis module: includes scanning electron microscope, mercury porosimeter and image processing software, used to acquire and analyze the microstructure parameters of the sample; Quantitative Relationship Modeling Module: Used to establish empirical relationships between strength damage variables and porosity changes, as well as permeability coefficient prediction models; Model Validation and Application Module: Used to validate model accuracy and apply it to long-term stability assessment of cold region engineering projects.

[0016] Beneficial effects of this invention: (1) Through systematic indoor experiments and microstructure analysis, this invention elucidates for the first time the quantitative relationship between the evolution of clay microstructure and the deterioration of macroscopic properties under freeze-thaw cycles, revealing that the development of large pores and microcracks caused by freeze-thaw cycles is the main reason for the deterioration of the macroscopic properties of soil. The study found that with the increase of the number of freeze-thaw cycles, the breaking strength, elastic modulus and cohesion of clay all decrease exponentially and tend to stabilize after 5 freeze-thaw cycles; in the unconsolidated undrained triaxial test, the stress-strain relationship of the sample before freeze-thaw is strain hardening type, and after freeze-thaw, it changes to strain softening type.

[0017] (2) This invention establishes an empirical relationship between strength damage variables and porosity changes, achieving the goal of predicting macroscopic mechanical property degradation through microstructural parameters, and providing a theoretical basis for long-term stability assessment of cold-region engineering. Studies have shown that freeze-thaw cycles lead to a significant increase in the porosity of the samples and an increase in the proportion of large pores, which is the fundamental reason for the deterioration of soil mechanical properties.

[0018] (3) Based on the high porosity and the improved Kozeny-Carman equation, this invention proposes a prediction model for the permeability coefficient of clay under different freeze-thaw conditions, overcoming the shortcomings of traditional permeability coefficient prediction methods that do not fully consider the evolution of freeze-thaw pore structure. Studies have shown that under low confining pressure, the soil permeability coefficient increases linearly with the number of freeze-thaw cycles; under high confining pressure, the permeability coefficient increases slowly logarithmically. The higher the initial moisture content and the lower the freezing temperature, the greater the permeability coefficient of the soil after freeze-thaw.

[0019] (4) The error between the predicted and measured values ​​of the macro-micro quantitative relationship model established in this invention is less than 20%, which can be used to predict the long-term service performance of slopes, roadbeds and dams in cold regions, and provide key parameter support for engineering design and disaster early warning. By revealing the evolution law of soil physical and mechanical parameters under freeze-thaw cycle, it provides a scientific basis for in-depth understanding of slope instability mechanism and disaster prevention and control in cold regions. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of the method of the present invention; Figure 2 The relationship between freeze-thaw damage variables and clay porosity; Figure 3 SEM images of clay samples after 0, 3, and 9 freeze-thaw cycles; Figure 4 Fitting results of permeability coefficient under freeze-thaw cycles. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0022] Example 1: As Figure 1 As shown, a method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles includes the following steps: Step 1. Prepare clay samples with different freeze-thaw cycles. Collect undisturbed clay samples or prepare remolded clay samples from the site, and set multiple freeze-thaw cycle gradients, including 0, 1, 3, 5, and 10 cycles. Conduct freeze-thaw cycle tests under closed system conditions or without external water supply. Each freeze-thaw cycle consists of freezing at a negative temperature environment (e.g., -20°C) for 12 hours, followed by thawing at a positive temperature environment (e.g., 20°C) for 12 hours, constituting a complete cycle, totaling 24 hours.

[0023] Step 2. Macroscopic mechanical and permeability testing Clay samples subjected to different freeze-thaw cycles were subjected to triaxial compression or direct shear tests to determine their breaking strength, elastic modulus, and cohesion. Two confining pressure conditions were set for the triaxial tests: low confining pressure (50-100 kPa) and high confining pressure (200 kPa), to analyze the evolution of mechanical properties under different confining pressures. In unconsolidated undrained triaxial tests, the changes in the stress-strain relationship of the samples before and after freeze-thaw were observed: the samples were strain-hardening before freeze-thaw and transformed into strain-softening samples after freeze-thaw.

[0024] Simultaneously, variable head permeability tests or triaxial permeability tests were conducted to determine the saturated permeability coefficient. The evolution of the permeability coefficient under low and high confining pressure conditions was analyzed: under low confining pressure, the soil permeability coefficient showed a linear increasing trend with the number of freeze-thaw cycles; under high confining pressure, the permeability coefficient showed a slow logarithmic increasing trend. The evolution data of macroscopic mechanical and permeability properties under each freeze-thaw cycle were recorded.

[0025] Step 3. Microstructure testing and image analysis Scanning electron microscopy (SEM) or mercury porosimetry (MIP) was used to test clay samples subjected to different freeze-thaw cycles to obtain microstructure images or pore distribution data. Image-Pro Plus or ImageJ image processing software was used to binarize the SEM images, and grayscale thresholds were set to distinguish pores from soil particles. Pore structure parameters were extracted, including macropore content (pore size >10 μm), mesopore content (1-10 μm), micropore content (<1 μm), average pore size, and porosity. The evolution of microstructure parameters under different freeze-thaw cycles was statistically analyzed, revealing the microstructure evolution characteristics of a significant increase in macropore content, a decrease in micropore and mesopore content, and an increase in average pore size caused by freeze-thaw cycles.

[0026] Step 4. Establish a quantitative relationship between intensity decay and porosity change. Define intensity damage variables ,in The breaking strength of the unfrozen specimen. The destructive strength of the specimen after N freeze-thaw cycles is given. Damage variables are analyzed. The quantitative relationship between porosity increment n and the linear empirical relationship is established through nonlinear regression: =a×N+b; Where a and b are the fitting parameters obtained by nonlinear regression of experimental data.

[0027] Step 5. Construct a permeability coefficient prediction model based on macropore content. The evolution characteristics of pore structure during freeze-thaw cycles were analyzed to determine the variation of macropore content with the number of freeze-thaw cycles. Based on the improved Kozeny-Carman equation, a permeability coefficient prediction model was established.

[0028] in, K The saturated permeability coefficient after freeze-thaw cycles. Liquid limit, The porosity is the porosity after freeze-thaw cycles. It should be emphasized that the porosity here is the macropore content, and c and d are model parameters calibrated through experimental data.

[0029] Step 6. Model Validation and Application Independent experimental data were used to validate the empirical relationships and prediction models established in steps 4 and 5. Root mean square error and relative error were used as evaluation indicators to ensure that the relative error between predicted and measured values ​​was controlled within 30%. The validated model was then applied to the long-term stability assessment of slopes, roadbeds, or dams in cold-region engineering projects. The degree of degradation of macroscopic soil properties was predicted based on the number of freeze-thaw cycles, providing key parameter support for engineering design and disaster early warning.

[0030] Preferably, in step 1, multiple gradients of initial moisture content are set (such as 15%, 20%, 25%), and the influence of initial moisture content on the permeability coefficient of soil after freeze-thaw is analyzed: the higher the initial moisture content, the greater the permeability coefficient of soil after freeze-thaw.

[0031] Preferably, in step 2, the triaxial test includes two types: unconsolidated undrained test and consolidated undrained test. The stress-strain curves under different test conditions are compared and analyzed: under high confining pressure, the stress-strain curves of the specimens all show strain hardening type; under low confining pressure, they change to strain softening type after freeze-thaw.

[0032] Preferably, in step 3, the microstructure analysis also includes the observation of particle breakage and weakening of interparticle connections, revealing the microscopic mechanism by which repeated freeze-thaw cycles lead to weakening of interparticle connections, intensified particle breakage, and the generation of a large number of large pores and microcracks, forming preferential seepage channels.

[0033] Preferably, in step 5, the model parameters a, b, c, d are calibrated using multiple sets of experimental data through multivariate nonlinear regression, and the coefficient of determination R² is used to evaluate the goodness of fit of the model.

[0034] Preferably, in step 6, the model application also includes comparison and verification with long-term on-site monitoring data, and dynamic correction of model parameters based on feedback.

[0035] Example 2: Macro- and micro-deterioration analysis of clay slopes in a seasonally frozen soil region, the process is as follows: Figure 1 It includes the following steps: Step 1. Sample preparation and freeze-thaw cycle test Uncirculated soil samples were collected from a typical clay slope in a seasonally frozen soil region, and remolded clay samples were prepared for comparison. Basic physical properties of the samples: liquid limit 42%, plastic limit 22%, plasticity index 20, optimum moisture content 18.5%, maximum dry density 1.57 g / cm³. Triaxial permeability tests were conducted using cylindrical samples with a diameter of 50 mm and a height of 100 mm, prepared by static pressure compaction in 5 layers. Thawing cycle gradients of 0, 1, 3, 5, 10, and 20 were set, with 6 parallel samples prepared for each gradient. Freeze-thaw tests were conducted in a closed system, with a freezing temperature of -20℃ (12 h) and a thawing temperature of 20℃ (12 h), for a total of 24 h as one complete cycle.

[0036] Step 2. Macroscopic mechanical and permeability testing Unconsolidated undrained triaxial compression tests were conducted on specimens after different freeze-thaw cycles, with four confining pressure levels of 15, 50, 100, and 150 kPa. The breaking strength, elastic modulus, and cohesion were measured. Consolidated undrained triaxial tests were also performed simultaneously to compare and analyze the stress-strain relationship under different test conditions. Variable head permeability tests and triaxial permeability tests were conducted to determine the saturated permeability coefficient, with confining pressures of 50 kPa (low confining pressure) and 20 kPa (high confining pressure), respectively.

[0037] The experimental results show that: With increasing freeze-thaw cycles, the breaking strength, elastic modulus, and cohesion exhibit an exponential decay pattern. After 10 freeze-thaw cycles, the decay tends to stabilize, and after 30 cycles, the strength loss reaches 40%-50%.

[0038] In the unconsolidated undrained triaxial test, the stress-strain relationship of the specimen before freeze-thaw is strain hardening type, and after freeze-thaw, it changes to strain softening type.

[0039] In consolidated undrained triaxial tests, the stress-strain curves of the specimens under high confining pressure all exhibit strain hardening characteristics; under low confining pressure, the strain softening characteristics are transformed after freeze-thaw cycles.

[0040] The permeability coefficient increased significantly: under low confining pressure, the permeability coefficient increased to 5-8 times the initial value after 30 freeze-thaw cycles, showing a linear growth trend; under high confining pressure, the permeability coefficient increased to 2-3 times the initial value after 30 freeze-thaw cycles, showing a slow logarithmic growth trend.

[0041] The sample with an initial moisture content of 20% had a 40% greater permeability coefficient after freeze-thaw compared to the sample with 15% moisture content; the sample with a freezing temperature of -20℃ had a 25% greater permeability coefficient after freeze-thaw compared to the sample with a freezing temperature of -10℃.

[0042] Step 3. Microstructure testing and image analysis The freeze-thawed samples were then freeze-dried, and their microstructure was observed using a scanning electron microscope (SEM) to obtain SEM images at 500x and 2000x magnification. Mercury intrusion porosimetry (MIP) was also performed to obtain pore size distribution curves. Image-Pro Plus software was used to binarize the SEM images, and pore structure parameters were statistically analyzed.

[0043] The results show that: With increasing freeze-thaw cycles, the content of macropores (>10μm) increased from 5% initially to 18% after 30 cycles, the content of micropores (<1μm) decreased from 35% initially to 22% after 30 cycles, and the average pore size increased from 2.5μm initially to 6.8μm after 30 cycles.

[0044] Repeated freeze-thaw cycles weaken the bonds between particles, exacerbate particle breakage, and generate a large number of large pores and microcracks, forming preferential seepage channels.

[0045] The porosity increased from an initial 38% to 45% after 30 freeze-thaw cycles, and the porosity increment Δn showed a linear relationship with the number of freeze-thaw cycles N.

[0046] Step 4. Establish a quantitative relationship between intensity decay and porosity change. Define intensity damage variables Where σ_0 is the breaking strength of the unfrozen specimen (taken as the average value under a confining pressure of 200 kPa). Analysis The relationship between porosity increment Δn and the linear empirical relationship is established through nonlinear regression (see...). Figure 2 ).

[0047] Step 5. Construct a permeability coefficient prediction model based on macropore content. Based on the macropore content P_macro and the improved Kozeny-Carman equation, a permeability coefficient prediction model was established (see...). Figure 3 ):

[0048] Model parameters were calibrated using experimental data from 10 and 20 freeze-thaw cycles. Model validation was performed using experimental data from 5 and 30 freeze-thaw cycles. The relative error between predicted and measured values ​​was within 15%, indicating that the model has good predictive accuracy (see...). Figure 4 ).

[0049] Step 6. Model Validation and Application The established macro-micro quantitative relationship was applied to the long-term stability assessment of the clay slope. According to local climate data, the surface soil of the slope experiences approximately 15 freeze-thaw cycles per year.

[0050] Based on the forecast results, it is recommended to implement insulation measures on the slope surface and strengthen the drainage system design to mitigate the effects of freeze-thaw degradation. It is also recommended to establish long-term monitoring points on the slope shoulder and slope surface, focusing on monitoring displacement changes during the spring thaw.

[0051] The model prediction results were compared with 3 years of on-site monitoring data. The measured displacement and the displacement predicted based on strength attenuation were in good agreement, with a relative error of less than 20%, which verified the engineering applicability of the model.

[0052] Example 3: This invention also discloses a system for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles, used to implement the above-mentioned method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles, comprising: Freeze-thaw cycle test module: used to subject clay samples to different numbers of freeze-thaw cycles; Macroscopic performance testing module: used to determine the breaking strength, elastic modulus, cohesion and permeability coefficient of the sample; Microstructure analysis module: includes scanning electron microscope, mercury porosimeter and image processing software, used to acquire and analyze the microstructure parameters of the sample; Quantitative Relationship Modeling Module: Used to establish empirical relationships between strength damage variables and porosity changes, as well as permeability coefficient prediction models; Model Validation and Application Module: Used to validate model accuracy and apply it to long-term stability assessment of cold region engineering projects.

[0053] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles, characterized in that, Includes the following steps: Step 1: Prepare clay samples with different freeze-thaw cycles, set multiple freeze-thaw cycle gradients, and conduct freeze-thaw cycle tests under closed system conditions. Each freeze-thaw cycle includes freezing at a negative temperature and thawing at a positive temperature. Step 2: Perform macroscopic mechanical and permeability tests on clay samples that have undergone different freeze-thaw cycles, determine their breaking strength, elastic modulus, cohesion and saturated permeability coefficient, and record the evolution data of macroscopic mechanical and permeability characteristics under each freeze-thaw cycle. Step 3: Conduct microstructure tests on clay samples that have undergone different freeze-thaw cycles, obtain microstructure images or pore distribution data, and use image processing software to extract pore structure parameters, including macropore content, mesopore content, micropore content, average pore size and porosity. Statistically analyze the evolution of microstructure parameters under different freeze-thaw cycles. Step 4: Define the intensity damage variable ,in The breaking strength of the unfrozen specimen. For experience N Destructive strength of the specimen after one freeze-thaw cycle; Analysis of damage variables The quantitative relationship between strength damage variable and porosity increment Δn is established by nonlinear regression, which yields an empirical relationship between the strength damage variable and porosity increment. Step 5: Analyze the evolution characteristics of pore structure during freeze-thaw cycles, determine the variation law of macropore content with the number of freeze-thaw cycles, and establish a permeability coefficient prediction model based on macropore content and the improved Kozeny-Carman equation. Step 6: Use independent experimental data to verify the empirical relationships and prediction models established in Steps 4 and 5, and apply the verified models to the long-term stability assessment of slopes, roadbeds or dams in cold regions.

2. The method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles according to claim 1, characterized in that: In step 1, the freeze-thaw cycle number gradient is set to 0, 1, 3, 5, and 10 times; the freezing temperature is -20℃, the thawing temperature range is 20℃, and each freeze-thaw cycle is 24 hours, of which freezing and thawing each last 12 hours.

3. The method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles according to claim 1, characterized in that: In step 2, the macroscopic mechanical test adopts a triaxial compression test or a direct shear test, and the confining pressure settings include low confining pressure of 50-100 kPa and high confining pressure of 200 kPa.

4. The method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles according to claim 1, characterized in that: In step 2, the permeability characteristics test adopts a variable head permeability test or a triaxial permeability test to analyze the evolution law of the permeability coefficient under different confining pressure conditions.

5. The method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles according to claim 1, characterized in that: In step 3, the microstructure test is performed using a scanning electron microscope or mercury porosimetry; the SEM image is binarized using Image-ProPlus or ImageJ image processing software, and pores and soil particles are distinguished by setting a grayscale threshold, and pore morphology parameters and distribution characteristics are calculated.

6. The method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles according to claim 1, characterized in that: In step 3, the porosity threshold is determined based on the soil type; for clay, a pore size greater than 10 is used. The pores are macropores, the pores with a diameter of 1-10 μm are mesopores, and the pores with a diameter of less than 1 μm are small voids.

7. The method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles according to claim 1, characterized in that: In step 4, the empirical relationship between the strength damage variable and the porosity increment is linear: =aN+b; Where a and b are the fitting parameters obtained by nonlinear regression of experimental data.

8. The method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles according to claim 1, characterized in that: In step 5, the permeability coefficient prediction model is a modified KC equation: in, K The saturated permeability coefficient after freeze-thaw cycles. Liquid limit, denoted as porosity after freeze-thaw cycles, where porosity represents the macropore content. c and d are model parameters calibrated using experimental data.

9. The method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles according to claim 1, characterized in that: In step 6, the model validation uses root mean square error and relative error as evaluation indicators, and the relative error between the predicted value and the measured value is controlled within 30%.

10. A system for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles, used to implement the method for determining the quantitative relationship between macro- and micro-deterioration of clay under freeze-thaw cycles as described in any one of claims 1-9, characterized in that, include: Freeze-thaw cycle test module: used to subject clay samples to different numbers of freeze-thaw cycles; Macroscopic performance testing module: used to determine the breaking strength, elastic modulus, cohesion and permeability coefficient of the sample; Microstructure analysis module: includes scanning electron microscope, mercury porosimeter and image processing software, used to acquire and analyze the microstructure parameters of the sample; Quantitative Relationship Modeling Module: Used to establish empirical relationships between strength damage variables and porosity changes, as well as permeability coefficient prediction models; Model Validation and Application Module: Used to validate model accuracy and apply it to long-term stability assessment of cold region engineering projects.

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