A method and device for identifying diseases of dirty ballast beds during the spring thaw period

Abstract

The present invention provides a method and device for identifying diseases of dirty ballast beds during the spring thaw period, relating to the technical field of ballast bed disease identification, specifically including: establishing a discontinuous grading body cemented contact model based on the discrete element simulation method, and introducing a temperature-stiffness coupling factor and a temperature-strength coupling factor into the model; obtaining the mechanical response data of dirty ballast under different temperature gradients, dirt contents and dynamic loads through a partitioned stepped temperature-controlled large triaxial experimental device, and constructing a critical dynamic strain calculation model using the mechanical response data based on the plastic shakedown theory; establishing a ballast bed structure simulation model by combining the cemented contact model and the critical dynamic strain calculation model, simulating the microscopic contact behavior of the ballast bed under the dynamic-melting coupling action during the spring thaw period, and obtaining simulation data; generating a disease assessment index for the ballast bed through correlation analysis of the simulation data, and evaluating the disease occurrence risk based on this, realizing the dynamic assessment of the risk of dirty ballast beds during the spring thaw period.

Description

Technical Field

[0001] The present invention relates to the technical field of ballast bed disease identification, and particularly to a method and device for identifying dirty ballast bed diseases during the spring thaw period. Background Technique

[0002] During the spring thaw period, with the increase in temperature and the infiltration of snowmelt water, the mechanical properties of the dirty ballast bed will be significantly affected. Ballast bed materials, especially ballast, as the foundation of railways and roads, bear changing dynamic loads. However, the accumulation of fine particles in the dirty ballast bed will have a negative impact on the internal cementation structure of the ballast bed, resulting in a decrease in the stiffness and strength of the cementation bonds. Traditional methods for identifying ballast bed diseases mostly rely on static monitoring and empirical judgment, and cannot fully consider the temperature change and its impact on material properties, especially under dynamic loads. This method often ignores the dynamic changes in the microscopic contact behavior inside the ballast bed, resulting in the failure to timely and effectively identify and handle the mud pumping disease that occurs during the spring thaw period, thus increasing the risk of ballast bed failure.

[0003] In addition, there are certain limitations in the acquisition and analysis of mechanical response data in the prior art. Most methods fail to effectively integrate the comprehensive effects of multiple factors such as temperature gradient, dirt content, and dynamic load on the performance of the ballast bed. Therefore, there is a lack of dynamic evaluation indicators for specific environmental conditions, resulting in an incomplete and unscientific evaluation of the ballast bed state. The current evaluation system often based on fixed thresholds fails to adapt to the changes in the ballast bed state under different temperature and moisture conditions. More importantly, traditional methods fail to provide real-time feedback on the microscopic contact behavior and physical changes of the ballast bed, resulting in a lag in response in practical applications and the inability to take timely measures to prevent the aggravation of diseases. Therefore, it is particularly important to develop a technical means that can comprehensively consider the effects of temperature, dirt, and dynamic load on ballast bed diseases and provide real-time and dynamic evaluation.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and device for identifying dirty ballast bed diseases during the spring thaw period to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for identifying dirty ballast bed diseases during the spring thaw period, the specific steps include:

[0008] S1. Based on the discrete element simulation method, establish a discontinuous graded aggregate cemented contact model, and introduce temperature-stiffness coupling factor and temperature-strength coupling factor into the model. The dynamic effects of melting on the stiffness and strength of cemented bonds are characterized by the temperature-stiffness coupling factor and temperature-strength coupling factor respectively;

[0009] S2. Obtain the mechanical response data of dirty ballast under different temperature gradients, dirt contents and dynamic loads through a partitioned stepped temperature-controlled large triaxial experimental device, and construct a critical dynamic strain calculation model based on the plastic shakedown theory using the mechanical response data;

[0010] S3. Combine the cemented contact model and the critical dynamic strain calculation model to establish a track bed structure simulation model, simulate the microscopic contact behavior of the track bed under the dynamic-melting coupling action during the spring thaw period, and obtain simulation data including the cemented bond fracture rate, force chain non-uniformity and settlement rate;

[0011] S4. Generate a track bed disease assessment index by performing a correlation analysis on the cemented bond fracture rate, force chain non-uniformity and settlement rate, compare the disease assessment index with a preset threshold, and evaluate the occurrence risk of the track bed pumping and mud boiling disease according to the comparison results.

[0012] Further, the establishment of the discontinuous graded aggregate cemented contact model is specifically as follows: Based on the discrete element method, model the ballast particles as polyhedral units, and the cemented bonds between the dirty fine particles and the ballast are characterized by the parallel bond model;

[0013] The introduction of the temperature-stiffness coupling factor and temperature-strength coupling factor is specifically expressed as follows:

[0014] ;

[0015] In the formula, is the temperature-stiffness coupling factor, is the real-time temperature of the environment where the track bed is located, is the reference temperature, is the initial stiffness at the reference temperature , is the natural constant, is the first temperature attenuation coefficient, which is used to characterize the attenuation rate of the cemented bond stiffness with the increase of temperature and is calibrated through experiments; is the temperature-strength coupling factor, is the initial strength at the reference temperature , is the second temperature attenuation coefficient, which is used to characterize the attenuation rate of the cemented bond strength with the increase of temperature and is calibrated through experiments;

[0016] and The calibration method is as follows: Using a high-precision bidirectional loading tester, within the range of -10°C to 20°C, at intervals of 5°C, conduct shear tests on the dirty ballast samples with a fixed dirt content, and measure the bond stiffness and bond strength when the bond is damaged. Then, through non-linear regression fitting, determine and ; and ;

[0017] The contact force between particles is expressed by the following formula:

[0018] ;

[0019] In the formula, is the contact force between particles, is the relative displacement between particles, that is, the overlapping displacement, represents the contact area.

[0020] Furthermore, adopt a partitioned stepped temperature control large triaxial experimental device to obtain the mechanical response data of the dirty ballast samples under different temperature gradients, dirt contents and dynamic loads. The mechanical response data includes the dynamic stress amplitude, cumulative energy dissipation, and the failure stress, temperature and water content when the sample is damaged;

[0021] The range of the temperature gradient is [-10°C, 20°C], the range of the dirt content is 5%-20%, specifically the mass ratio of the dirty fine particles to the dirty ballast sample, the frequency range of the dynamic load is 1-5Hz, and the amplitude range is 50-200kPa;

[0022] Based on the plastic shakedown theory, use the mechanical response data to construct a critical dynamic strain calculation model. The formula is as follows:

[0023] ;

[0024] In the formula, is the critical dynamic strain, that is, the dynamic strain recorded when the sample is damaged, is the dynamic stress amplitude, is the reference stress, is the water content, is the temperature, is the reference temperature, is the cumulative energy dissipation, is the reference energy dissipation, calibrated through experiments, , , , and are fitting coefficients, determined through non-linear regression;

[0025] Among them, the cumulative energy dissipation The calculation formula is as follows:

[0026] ;

[0027] In the formula, is the strain rate, that is, the derivative of strain with respect to time, represents the loading time, is a function of strain varying with time, represents the sinusoidal wave load, is the sine function, is the dynamic stress amplitude, and ranges from 50 to 200 kPa, is the pi, is the frequency, and , with the unit of Hz.

[0028] Furthermore, combining the cemented contact model and the critical dynamic strain calculation model, a simulation model of the ballast bed structure is established based on the discrete element method. The ballast particles are modeled as polyhedral elements, and the dirty fine particles are simplified as small spheres and filled in the ballast gaps according to the preset gradation; cemented bonds are generated at the particle contact points, and their stiffness and strength are dynamically updated according to the real-time temperature . A sinusoidal wave load is applied at the top of the model, and at the same time, a temperature gradient field is set to simulate the melting process during the spring thaw period, and the cemented bond fracture rate, force chain non-uniformity and settlement rate are monitored;

[0029] The preset gradation means that the range of the mass ratio of the dirty fine particles to the ballast particles is 5% - 20%;

[0030] The temperature gradient field is [-10°C, 20°C];

[0031] The sinusoidal wave load has the following expression:

[0032] ;

[0033] In the formula, is the sine function, is the dynamic stress amplitude, and ranges from 50 to 200 kPa, is the pi, is the frequency, and , with the unit of Hz, represents the loading time.

[0034] Furthermore, the specific logic for monitoring the cement bond fracture rate, force chain non-uniformity, and settlement rate is as follows: During the simulation, the number of fractured cement bonds in each calculation step is counted and the ratio to the total number of initial cement bonds is calculated as the cement bond fracture rate, i.e.:

[0035] ;

[0036] In the formula, is the cement bond fracture rate, is the number of fractured cement bonds, which is determined to be fractured when the sine wave fluctuating load exceeds the contact force , is the total number of cement bonds generated during model initialization; the calculation step is the time increment for each iteration;

[0037] The force chain non-uniformity is calculated according to the following formula :

[0038] ;

[0039] In the formula, is the average contact force, is the total number of cement bonds generated during model initialization, is the contact force at the th contact point, is the index of the contact point, is the standard deviation of the contact force, which is used to characterize the dispersion degree of the force chain distribution, is the force chain non-uniformity;

[0040] A particle array is selected in the central area at the top of the model as the representative particles, and the settlement rate of each particle is calculated, and the average value of the settlement rates of all representative particles is taken to characterize the overall settlement rate;

[0041] Among them, the formula for calculating the settlement rate is as follows:

[0042] ;

[0043] In the formula, is the settlement rate, is the dynamic stress amplitude, is the critical dynamic strain, is the proportionality coefficient, which is determined according to the material properties, and .

[0044] Furthermore, a correlation analysis is performed on the cement bond fracture rate, force chain non-uniformity, and settlement rate to generate a disease assessment index for characterizing the occurrence risk of the subgrade pumping and mud boiling disease. The formula is as follows:

[0045] ;

[0046] In the formula, is the disease assessment index, is the bonding bond fracture rate, is the force chain non-uniformity, is the settlement rate, is the natural constant, , and are preset weights determined according to the analytic hierarchy process.

[0047] Further, according to the analytic hierarchy process, determine , and , and the specific logic is as follows:

[0048] Mark the three indicators of the bonding bond fracture rate, the force chain non-uniformity, and the settlement rate, determine the relative importance values between each pair through the nine-scale method, and construct a judgment matrix. Among them, mark the bonding bond fracture rate as 1, the force chain non-uniformity as 2, and the settlement rate as 3. The constructed judgment matrix is:

[0049] ;

[0050] Among them, , both represent the index of the exponent, and , , represents that the exponent with index is more important than the exponent with index . The importance is measured by the 1-9 scale method, and The larger the value, the more important the exponent with index is compared to the exponent with index , and , ;

[0051] Divide each element value in the judgment matrix by the sum of its columns to obtain the normalized judgment matrix. Calculate the mean value of each row element value in the normalized judgment matrix, take the mean value of the first row element value as the weight of the bonding bond fracture rate, take the mean value of the second row element value as the weight of the force chain non-uniformity, and take the mean value of the third row element value as the weight of the settlement rate. With the constraint that the sum of the scaled values is equal to 1, scale the three weights proportionally, and use the scaled weights as the proportionality coefficients of the corresponding exponents.

[0052] Further, compare the disease assessment index with the preset threshold Compare, and the specific logic is as follows:

[0053] When it is judged as a low-risk level, indicating that the current ballast bed state is relatively good, and the fracture rate of the bonding keys, the non-uniformity of the force chains, and the settlement rate are all within the acceptable range;

[0054] When it is judged as a medium-risk level, indicating that there is a certain degree of disease risk in the current ballast bed, and monitoring and evaluation measures should be taken, and maintenance should be implemented if necessary;

[0055] When it is judged as a high-risk level, indicating that there is a high risk of mud pumping disease in the current ballast bed, and the fracture rate of the bonding keys, the non-uniformity of the force chains, and the settlement rate have all reached dangerous levels, and on-site evaluation and inspection should be carried out immediately, and emergency maintenance measures should be taken.

[0056] Furthermore, the calibration method of the preset threshold is as follows:

[0057] ;

[0058] In the formula, is the basic risk threshold, determined by the expert evaluation method, is the real-time temperature of the environment where the ballast bed is located, is the moisture content; and are step functions. When , , otherwise . When , , otherwise .

[0059] The present invention also further provides a device for identifying diseases of a dirty ballast bed during the spring thaw period. The device for identifying diseases of a dirty ballast bed during the spring thaw period is used to execute the above-mentioned method for identifying diseases of a dirty ballast bed during the spring thaw period, and includes:

[0060] A non-continuous graded aggregate cemented contact model construction module, based on the discrete element simulation method, establishes a non-continuous graded aggregate cemented contact model, and introduces a temperature-stiffness coupling factor and a temperature-strength coupling factor into the model, and respectively characterizes the dynamic influence of the melting effect on the cemented key stiffness and the cemented key strength through the temperature-stiffness coupling factor and the temperature-strength coupling factor;

[0061] A critical dynamic strain calculation model construction module, which is used to obtain the mechanical response data of dirty ballast under different temperature gradients, dirt contents and dynamic loads through a partitioned stepped temperature-controlled large triaxial test device, and constructs a critical dynamic strain calculation model based on the plastic shakedown theory using the mechanical response data;

[0062] The ballast bed structure simulation model construction module is used to establish a ballast bed structure simulation model by combining a bonded contact model and a critical dynamic strain calculation model, simulate the microscopic contact behavior of the ballast bed under the dynamic-melting coupling action during the spring thaw period, and obtain simulation data including the bonded key fracture rate, force chain non-uniformity, and settlement rate;

[0063] The disease risk assessment module is used to generate a disease assessment index for the ballast bed by performing a correlation analysis on the bonded key fracture rate, force chain non-uniformity, and settlement rate, compare the disease assessment index with a preset threshold, and evaluate the occurrence risk of the ballast bed pumping and mud gushing disease according to the comparison result.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] The present invention provides a scientific and systematic evaluation mechanism by introducing discrete element simulation and zoned stepped temperature-controlled large triaxial experiments. Specifically, based on the dynamic models of temperature-stiffness coupling factor and temperature-strength coupling factor, the stiffness and strength of the bonded keys can reflect the environmental temperature changes in real time, so as to effectively capture the microscopic contact behavior of the ballast bed under different conditions. This dynamic monitoring method can timely identify the fracture rate of the bonded keys and the non-uniformity of the force chains inside the ballast bed, and reveal the potential risk of pumping and mud gushing diseases.

[0066] By generating the disease assessment index and comparing it with the preset threshold, the state of the ballast bed can be subdivided into low, medium, and high risk levels. This clear risk classification provides a practical basis for subsequent maintenance and management, ensuring that preventive measures are taken in time before potential risks occur and reducing the possibility of ballast bed failure. In addition, the flexibility and adaptability of this method make the evaluation results more realistic and provide strong technical support for the safe operation of the ballast bed. Brief Description of the Drawings

[0067] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0068] Figure 2 It is a schematic diagram of the overall device module of the present invention. Detailed Embodiments

[0069] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0070] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0071] Embodiment:

[0072] Please refer to Figure 1 , the present invention provides a technical solution:

[0073] A method for identifying diseases of dirty ballast beds during the spring thaw period, the specific steps include:

[0074] S1, based on the discrete element simulation method, establish a discontinuous grading ligand cementation contact model, and introduce a temperature-stiffness coupling factor and a temperature-strength coupling factor into the model. The dynamic effects of the melting action on the stiffness and strength of the cementation bond are characterized by the temperature-stiffness coupling factor and the temperature-strength coupling factor respectively;

[0075] In this embodiment, the establishment of the discontinuous grading ligand cementation contact model is specifically as follows: based on the discrete element method, the ballast particles are modeled as polyhedral units, and the cementation bond between the dirty fine particles and the ballast is characterized by a parallel bond model;

[0076] The introduction of the temperature-stiffness coupling factor and the temperature-strength coupling factor, the specific expressions are as follows:

[0077] ;

[0078] In the formula, is the temperature-stiffness coupling factor, is the real-time temperature of the environment where the ballast bed is located, and ranges from -10°C to 20°C, and is used to simulate the temperature change during the spring thaw period; is the reference temperature, and in this embodiment, the freezing point 0°C is taken as the reference temperature, is the reference temperature of the initial stiffness, is the natural constant, is the first temperature attenuation coefficient, which is used to characterize the attenuation rate of the bond stiffness with the increase of temperature and is calibrated through experiments; is the temperature-strength coupling factor, is the reference temperature and the initial strength at is the second temperature attenuation coefficient, which is used to characterize the attenuation rate of the bond strength with the increase of temperature and is calibrated through experiments;

[0079] The temperature-stiffness coupling factor and the temperature-strength coupling factor are used to quantify the dynamic effect of temperature change on the mechanical properties of the bond and characterize the influence of temperature change on the bond stiffness and strength. When the temperature rises, the material undergoes thermal expansion, resulting in changes in its microstructure. The molecular motion of the cementitious material increases, leading to an increase in the microscopic contact area of the bond, thereby reducing the overall stiffness and strength. The exponential function is commonly used in physics and engineering to describe the properties of materials that change with time or temperature, and this form can effectively capture the rapid change characteristics of the material stiffness and strength within a specific temperature range. By introducing the reference temperature , the formula can compare the change in temperature relative to a known reference. This approach helps to determine the mechanical properties of the bond under specific temperature conditions, so the stiffness and strength of the bond decay exponentially with the increase of temperature. The nonlinear decay of the bond performance with the increase of temperature is simulated by the exponential decay function, which more realistically reflects the weakening effect of temperature fluctuations during the spring thaw period on the microstructure of the ballast bed.

[0080] and are calibrated as follows: Using a high-precision bidirectional loading tester, within the range of -10°C to 20°C, at intervals of 5°C, conduct shear tests on the fouled ballast samples with a fixed fouling content, measure the bond stiffness and the bond strength of the bond when it fails, and through nonlinear regression fitting, determine and ;

[0081] The contact force between particles is expressed by the following formula:

[0082] ;

[0083] In the formula, is the contact force between particles, is the relative displacement between particles, that is, the overlapping displacement, represents the contact area.

[0084] The advantage of step S1 is that by introducing the temperature-stiffness coupling factor and the temperature-strength coupling factor, the stiffness and strength of the cemented bond can dynamically reflect the change of the environmental temperature. This method effectively overcomes the defect of the traditional disease identification technology's insufficient response to environmental changes, can more realistically simulate the actual mechanical behavior of the dirty ballast bed during the spring thaw period, and greatly improves the accuracy and reliability of disease identification. In this solution, adopting this step can provide a scientific and dynamic evaluation basis for the overall solution, making the subsequent acquisition of mechanical response data and the construction of the critical dynamic strain calculation model more in line with the actual situation. By accurately simulating the microscopic contact behavior of the ballast bed, it can provide key data support for the generation of subsequent disease evaluation indexes, thus comprehensively improving the scientificity and effectiveness of the ballast bed disease risk assessment and ensuring that maintenance measures can be taken in time before potential risks occur.

[0085] S2. Obtain the mechanical response data of the dirty ballast under different temperature gradients, dirt contents and dynamic loads through a partitioned stepped temperature-controlled large triaxial experimental device, and construct a critical dynamic strain calculation model based on the plastic shakedown theory using the mechanical response data.

[0086] In this embodiment, a partitioned stepped temperature-controlled large triaxial experimental device is used to obtain the mechanical response data of the dirty ballast sample under different temperature gradients, dirt contents and dynamic loads. The mechanical response data includes the dynamic stress amplitude, the cumulative energy dissipation, and the failure stress, temperature and moisture content when the sample is damaged.

[0087] The range of the temperature gradient is [-10°C, 20°C], and a temperature experimental point is set every 5°C. The specific temperature experimental points include: -10°C, -5°C, 0°C, 5°C, 10°C, 15°C, 20°C.

[0088] The range of the dirt content is 5%-20%, specifically the mass ratio of the dirty fine particles to the dirty ballast sample. The specific dirt content experimental points include: 5%, 10%, 15%, 20%.

[0089] The range of the moisture content is 5%-20%. The specific moisture content experimental points include: 5%, 10%, 15%, 20%.

[0090] The frequency range of the dynamic load is 1-5 Hz, the amplitude range is 50-200 kPa, and an amplitude point is set every 50 kPa. The specific amplitude points include: 50 kPa, 100 kPa, 150 kPa, 200 kPa.

[0091] Randomly combine different dirt content and temperature experimental points to make multiple groups of dirty ballast samples. Adjust the temperature of the experimental device to the required experimental gradient and keep it stable. Apply the preset dynamic load and record the stress and strain data under different amplitudes, frequencies, and moisture contents. Under each experimental condition, record the dynamic stress amplitude, cumulative energy dissipation, failure stress, temperature, and moisture content in real time. Conduct multiple experiments under different temperatures and dirt contents to obtain mechanical response data to construct a critical dynamic strain calculation model.

[0092] Based on the plastic shakedown theory, use the mechanical response data to construct a critical dynamic strain calculation model. The formula is as follows:

[0093] ;

[0094] In the formula, is the critical dynamic strain, that is, the dynamic strain recorded when the specimen is damaged. is the dynamic stress amplitude, which refers to the amplitude of the instantaneous maximum stress applied to the structure. is the reference stress. is the moisture content. is the temperature, that is, the temperature when the specimen is damaged. is the reference temperature. is the cumulative energy dissipation. is the reference energy dissipation, which is calibrated through experiments. , , , and are fitting coefficients, which are determined by nonlinear regression, and , , , , .

[0095] In this formula, the critical dynamic strain is used to characterize the threshold strain at which the dirty ballast transitions from stable deformation to accelerated failure under the coupled action of cyclic dynamic load and temperature. When the actual dynamic strain value of the ballast bed is greater than , it is considered to enter the unstable deformation stage, and the risk of mud pumping and upheaval significantly increases. is used to characterize the influence of dynamic load on the critical strain. The exponent reflects the nonlinear effect of dynamic stress, where , which conforms to the law in the plastic shakedown theory that "the increase in stress leads to a decrease in critical strain"; The term is used to quantify the softening effect of moisture content on ballast, where , indicating that an increase in moisture content will reduce the critical strain, which is consistent with the experimental phenomenon of the strength attenuation of saturated ballast; Item describes the weakening effect of temperature rise during the spring thaw period on the cemented bond; Item reflects the amplification effect of cumulative damage on the critical strain, and the cumulative energy dissipation directly characterizes the plastic work under cyclic loading. This formula is used to predict the failure threshold strain of ballast under dynamic loading, taking into account the dynamic stress amplitude, moisture content, temperature, and cumulative energy dissipation, and quantifies the impact of the complex environment during the spring thaw period on the stability of the ballast bed.

[0096] Among them, the cumulative energy dissipation has the following calculation formula:

[0097] ;

[0098] In the formula, is the strain rate, that is, the derivative of strain with respect to time, represents the loading time, is the function of strain varying with time, represents the sinusoidal fluctuating load, is the sine function, is the dynamic stress amplitude, and ranges from 50 to 200 kPa, is the pi, is the frequency, and , with the unit of Hz.

[0099] The advantage of step S2 is that by using a partitioned stepped temperature-controlled large triaxial experimental device, the mechanical response data of contaminated ballast under different temperature gradients, contamination contents, and dynamic loads can be obtained, thereby enabling accurate analysis of the mechanical behavior of the ballast bed. This method provides a more detailed dynamic performance evaluation than traditional static tests, making the response of the ballast bed material under actual working conditions more realistic and effectively revealing the causes of ballast bed diseases during the spring thaw period.

[0100] In this solution, adopting this step can provide key experimental data support for the overall solution, making the construction of the critical dynamic strain calculation model more scientific and reasonable. The acquisition of such experimental data not only improves the accuracy of the subsequent simulation model but also ensures the comprehensiveness and reliability of the ballast bed disease assessment. By combining the changes in dynamic loads and environmental conditions, this step provides an empirical basis for disease assessment, thereby enabling more effective prediction and assessment of the risk of ballast bed mud pumping diseases and ensuring the safe operation of railways and highways.

[0101] S3. Combine the cemented contact model and the critical dynamic strain calculation model to establish a ballast bed structure simulation model, simulate the microscopic contact behavior of the ballast bed under the dynamic-melting coupling action during the spring thaw period, and obtain simulation data including the cemented bond fracture rate, force chain non-uniformity, and settlement rate;

[0102] In this embodiment, a simulation model of the ballast bed structure is established based on the discrete element method by combining the cementitious contact model and the critical dynamic strain calculation model. The ballast particles are modeled as polyhedral elements, and the dirty fine particles are simplified as small spheres and filled in the ballast gaps according to a preset gradation. Cementitious bonds are generated at the particle contact points, and their stiffness and strength are dynamically updated according to the real-time temperature . A sinusoidal fluctuating load is applied at the top of the model, and at the same time, a temperature gradient field is set to simulate the melting process during the spring thaw period. The fracture rate of the cementitious bonds, the non-uniformity of the force chains, and the settlement rate are monitored

[0103] The preset gradation means that the range of the mass ratio of the dirty fine particles to the ballast particles is 5%-20%

[0104] The temperature gradient field is [-10°C, 20°C]

[0105] The sinusoidal fluctuating load has the following expression

[0106] ;

[0107] In the formula, is the sine function is the amplitude of the dynamic stress, and ranges from 50 to 200 kPa is the pi is the frequency, and with the unit of Hz represents the loading time

[0108] The specific logic for monitoring the fracture rate of the cementitious bonds, the non-uniformity of the force chains, and the settlement rate is as follows: During the simulation process, the number of fractured cementitious bonds in each calculation step is counted and compared with the total number of initial cementitious bonds to calculate the ratio, which is used as the fracture rate of the cementitious bonds, that is

[0109] ;

[0110] In the formula, is the fracture rate of the cementitious bonds is the number of fractured cementitious bonds. When the sinusoidal fluctuating load exceeds the contact force , it is determined to be fractured is the total number of cementitious bonds generated during model initialization; the calculation step is the time increment for each iteration

[0111] The fracture rate of the cementitious bonds It directly reflects the damage degree of the connection structure between ballast particles. When the fracture rate increases, the mechanical transmission ability between particles decreases significantly, resulting in a reduction in the overall stiffness of the ballast bed. This process will trigger two key problems: First, the ability of the ballast bed to resist deformation weakens, and plastic deformation accumulation is more likely to occur under the action of train dynamic loads; Second, the microcracks formed by fractures provide channels for water penetration and fine particle migration, significantly increasing the risk of mud pumping. Existing experimental data show that when the fracture rate of the cementitious bond exceeds 30%, the elastic modulus of the ballast bed may decrease by more than 50%.

[0112] Calculate the force chain non-uniformity according to the following formula :

[0113] ;

[0114] In the formula, is the average contact force, is the total number of cementitious bonds generated during model initialization, is the contact force at the th contact point, is the index of the contact point, is the standard deviation of the contact force, used to characterize the dispersion degree of the force chain distribution, is the force chain non-uniformity.

[0115] The force chain non-uniformity characterizes the equilibrium degree of the contact force distribution between ballast particles. An increase in non-uniformity means that there is an obvious stress concentration phenomenon in the force chain network. This non-uniform distribution will bring three effects: The particles in the stress concentration area are more likely to break or slip, accelerating local settlement; The discontinuous distribution of the force chain weakens the overall stability of the ballast bed and may trigger sudden deformation; Under long-term action, high non-uniformity will lead to the anisotropy of the ballast bed structure, manifested as a rapid deterioration of the track geometry. Monitoring data shows that when the non-uniformity exceeds 0.4, the service performance of the ballast bed will decline sharply. The force chain non-uniformity is calculated based on the concept of statistical standard deviation, is the standard deviation of the contact force, representing the dispersion degree of the contact force, is the average contact force, and the ratio eliminates the influence of dimensions, facilitating cross-model comparison.

[0116] Select a particle array in the central area at the top of the model as the representative particles, calculate the settlement rate of each particle, and take the average value of the settlement rates of all representative particles to characterize the overall settlement rate;

[0117] Among them, the formula for calculating the settlement rate is as follows:

[0118] ;

[0119] In the formula, is the settlement rate, is the dynamic stress amplitude, is the critical dynamic strain, is the proportionality coefficient, which is determined according to the material properties, and .

[0120] The dynamic stress amplitude is the amplitude of the instantaneous maximum stress applied to the ballast bed structure. When the structure is subjected to a greater dynamic stress, the internal stress state will intensify, promoting the sliding and displacement between particles, thereby accelerating the settlement process, that is, when increases, the settlement rate will increase accordingly; when the critical dynamic strain increases, it means that the structure's resistance to deformation is enhanced, and the structure can still continue to settle under higher stress, resulting in an increase in the settlement rate; that is to say, , and the settlement rate show a positive correlation.

[0121] The settlement rate is the most intuitive macroscopic index for evaluating the ballast bed state. It refers to the sinking speed of particles under dynamic load and reflects the settlement behavior of particles under specific conditions. This rate is an important index for evaluating the stability and safety of ballast bed materials during the spring thaw period. An increase in the rate often indicates three potential problems: the fracture of the cementation bond and the reconstruction of the force chain have developed to the macroscopic visible stage; the foundation under the ballast layer may be softened or eroded; the drainage system fails, resulting in an increase in pore water pressure. These problems will significantly increase the risk of mud pumping.

[0122] The advantage of step S3 is that by combining the cementation contact model and the critical dynamic strain calculation model, and establishing a ballast bed structure simulation model based on the discrete element method, it can more realistically simulate the microscopic contact behavior of the ballast bed during the spring thaw period. This method effectively integrates the physical model and experimental data, improves the accuracy of the simulation results, and makes the evaluation of the internal mechanical state of the ballast bed more comprehensive. This dynamic simulation can not only capture important parameters such as the fracture rate of the cementation bond, the non-uniformity of the force chain, and the settlement rate, but also better understand the response characteristics of the ballast bed under different environmental conditions. In this solution, adopting this step can provide key simulation data support for the overall solution, comprehensively improving the accuracy and effectiveness of disease identification. By deeply analyzing the dynamic behavior of the ballast bed during the spring thaw period, the potential risks of mud pumping diseases can be better predicted and identified. This process not only helps to formulate more effective maintenance strategies, but also provides a solid theoretical basis for subsequent risk assessment and management, thus ensuring the safe operation of railways and highways in practical applications.

[0123] S4. By performing a correlation analysis on the cement bond fracture rate, the force chain non-uniformity, and the settlement rate, a disease assessment index of the roadbed is generated, and the disease assessment index is compared with a preset threshold. According to the comparison result, the occurrence risk of the mud pumping disease of the roadbed is evaluated;

[0124] In this embodiment, a correlation analysis is performed on the cement bond fracture rate, the force chain non-uniformity, and the settlement rate to generate a disease assessment index for characterizing the occurrence risk of the mud pumping disease of the roadbed. The formula is as follows:

[0125] ;

[0126] In the formula, is the disease assessment index, which is used to quantify the occurrence risk of the mud pumping disease of the roadbed. This index comprehensively considers multiple influencing factors such as the cement bond fracture rate, the force chain non-uniformity, and the settlement rate, so as to evaluate the health status of the entire roadbed. is the cement bond fracture rate, is the force chain non-uniformity, is the settlement rate, is the natural constant, , and are preset weights, which are determined according to the analytic hierarchy process.

[0127] The disease assessment index is used to characterize the occurrence risk of the mud pumping disease of the dirty roadbed. The larger its value, the higher the occurrence risk of the mud pumping disease of the roadbed. By calculating , it can effectively predict the possible diseases of the roadbed, helping engineers and maintenance personnel formulate corresponding maintenance and reinforcement measures to improve the safety and service life of the roadbed. In addition, the quantitative form of the evaluation index is also conducive to comparing different construction or maintenance plans. The three parameters of the cement bond fracture rate, force chain non-uniformity, and settlement rate in the formula respectively characterize the damage state of the roadbed at different scales: the cement bond fracture rate directly reflects the integrity of the microscopic connection structure. The higher the cement bond fracture rate, the worse the connectivity of the roadbed material, which may lead to a higher risk of diseases. The force chain non-uniformity characterizes the balance of mesoscopic mechanical transmission. The force chain non-uniformity reflects the internal force distribution of the material. The higher the non-uniformity, the worse the bearing capacity and stability of the material, increasing the possibility of diseases. The settlement rate reflects the development trend of macroscopic deformation. The settlement rate directly affects the stability of the roadbed material. The higher the settlement rate, the more unstable the material may be, further increasing the risk of diseases. The combination of these multi-scale parameters can comprehensively capture the whole process of roadbed from microscopic damage to macroscopic failure. The cement bond fracture rate uses a linear term to reflect its direct proportional relationship with the disease risk; the force chain non-uniformity uses a square term to highlight its non-linear effect of stress concentration, and the settlement rate uses an exponential term to accurately capture the mutation characteristics of the settlement acceleration stage. This design not only conforms to the physical meaning of each parameter but also amplifies the sensitivity of high-risk signals. Since it has been separately explained in the previous text that an increase in the cement bond fracture rate, force chain non-uniformity, or settlement rate will lead to an increase in the risk of mud pumping diseases of the roadbed, it will not be elaborated here.

[0128] Determined according to the analytic hierarchy process , and , and the specific logic is as follows:

[0129] Mark the three indicators of the cement bond fracture rate, force chain non-uniformity, and settlement rate, and determine the relative importance values between each pair through the nine-scale method to construct a judgment matrix. Among them, mark the cement bond fracture rate as 1, the force chain non-uniformity as 2, and the settlement rate as 3. The constructed judgment matrix is:

[0130] ;

[0131] Among them, , both represent the index of the exponent, and , , represents the exponent with index relative to the exponent with index importance. The importance is measured by the 1-9 scale method, and the larger the value, the more important the exponent with index compared to the exponent with index The greater the importance of the exponent, and , ;

[0132] Divide each element value in the judgment matrix by the sum of its column to obtain a normalized judgment matrix. Calculate the mean value of each row element value in the normalized judgment matrix, take the mean value of the first row element value as the weight of the cement bond fracture rate, take the mean value of the second row element value as the weight of the force chain non-uniformity, and take the mean value of the third row element value as the weight of the settlement rate. With the constraint that the sum of the scaled values is equal to 1, scale the three weights proportionally and use the scaled weights as the proportional coefficients of the corresponding exponents.

[0133] Compare the disease assessment index with the preset threshold . The specific logic is as follows:

[0134] When , it is judged as a low risk level, indicating that the current ballast bed state is relatively good, and the cement bond fracture rate, force chain non-uniformity and settlement rate are all within the acceptable range;

[0135] When , it is judged as a medium risk level, indicating that there is a certain degree of disease risk in the current ballast bed, and monitoring and assessment measures should be taken, and maintenance should be implemented if necessary;

[0136] When , it is judged as a high risk level, indicating that there is a high risk of mud pumping and gushing in the current ballast bed, and the cement bond fracture rate, force chain non-uniformity and settlement rate have all reached dangerous levels, and on-site assessment and inspection should be carried out immediately, and emergency maintenance measures should be taken.

[0137] The calibration method of the preset threshold is as follows:

[0138] ;

[0139] In the formula, is the basic risk threshold, which is determined according to the expert evaluation method, is the real-time temperature of the environment where the ballast bed is located, is the moisture content; and are step functions. When , , otherwise , when , , otherwise .

[0140] Set The correction term is because: 10°C is the phase change critical point of typical ballast cementing materials. After exceeding this temperature, the cementing strength will decay rapidly. The coefficient 0.2 corresponds to the empirical value that when the temperature rises from 10°C to 20°C, the threshold needs to be lowered by 20%; set The correction term is because: 10% moisture content is the critical moisture content of ballast performance. After exceeding this value, the lubrication effect of water makes the friction coefficient between particles decrease significantly, forming a synergistic effect with the temperature correction; moreover, the form of the step function simplifies the calculation and meets the requirements of engineering applications.

[0141] The advantage of step S4 is that by performing a correlation analysis on the cementing bond fracture rate, force chain non-uniformity, and settlement rate, a disease assessment index of the roadbed can be generated. Compared with the existing technology, this method can comprehensively consider multiple mechanical indexes to form a unified assessment index, thereby improving the accuracy and effectiveness of disease identification. This comprehensive analysis enables the performance assessment of the roadbed not to rely solely on a single index, avoiding the one-sidedness that may occur in traditional methods, and providing a more comprehensive and scientific basis for subsequent maintenance decisions. In this solution, adopting this step can provide a key risk assessment tool for the overall solution to ensure the timely identification and assessment of potential disease risks of the roadbed during the spring thaw period. By comparing the disease assessment index with the preset threshold, the health status of the roadbed can be quickly judged, and corresponding monitoring and maintenance measures can be taken according to the risk level. This decision-making support mechanism can effectively reduce maintenance costs, improve the safety and reliability of the roadbed, and thus ensure the safe operation of railways and highways in practical applications.

[0142] Please refer to Figure 2 , a device for identifying diseases of a dirty roadbed during the spring thaw period, comprising:

[0143] A non-continuous grading body cementing contact model construction module, based on the discrete element simulation method, establishes a non-continuous grading body cementing contact model, and introduces a temperature-stiffness coupling factor and a temperature-strength coupling factor into the model. The dynamic effects of the melting action on the stiffness and strength of the cementing bond are characterized by the temperature-stiffness coupling factor and the temperature-strength coupling factor respectively;

[0144] A critical dynamic strain calculation model construction module, used to obtain the mechanical response data of dirty ballast under different temperature gradients, dirty content, and dynamic loads through a partitioned stepped temperature-controlled large triaxial test device, and construct a critical dynamic strain calculation model based on the plastic shakedown theory using the mechanical response data;

[0145] A roadbed structure simulation model construction module, used to establish a roadbed structure simulation model by combining the cementing contact model and the critical dynamic strain calculation model, simulate the microscopic contact behavior of the roadbed under the dynamic-melting coupling action during the spring thaw period, and obtain simulation data including the cementing bond fracture rate, force chain non-uniformity, and settlement rate;

[0146] The disease risk assessment module is used to generate a disease assessment index for the roadbed by performing a correlation analysis on the bond breakage rate, the force chain non-uniformity, and the settlement rate, compare the disease assessment index with a preset threshold, and evaluate the occurrence risk of the disease of mud pumping and gushing in the roadbed according to the comparison result.

[0147] All the above formulas are dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to get the closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0148] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A method for identifying diseases of dirty ballast beds during the spring thaw period, characterized in that, The specific steps include: S1: Based on the discrete element simulation method, establish a discontinuous graded ligand-cement contact model, and introduce temperature-stiffness coupling factors and temperature-strength coupling factors into the model. The dynamic effects of melting on the stiffness and strength of cementitious bonds are characterized by the temperature-stiffness coupling factor and the temperature-strength coupling factor respectively. S2: Obtain the mechanical response data of contaminated ballast under different temperature gradients, contamination contents and dynamic loads through a partitioned stepped temperature-controlled large triaxial test device, and construct a critical dynamic strain calculation model using the mechanical response data based on the plastic shakedown theory. S3: Combine the cementitious contact model and the critical dynamic strain calculation model to establish a ballast bed structure simulation model, simulate the microscopic contact behavior of the ballast bed under the dynamic-melting coupling action during the spring thaw period, and obtain simulation data including the cementitious bond fracture rate, the non-uniformity of force chains and the settlement rate. S4: Through the correlation analysis of the cementitious bond fracture rate, the non-uniformity of force chains and the settlement rate, generate a disease assessment index for the ballast bed, compare the disease assessment index with a preset threshold, and evaluate the occurrence risk of the ballast bed pumping and mud boiling disease according to the comparison results.

2. The method for identifying diseases of a dirty roadbed during the spring thaw period according to claim 1, wherein: The establishment of the discontinuous graded ligand-cement contact model is specifically as follows: Based on the discrete element method, model the ballast particles as polyhedral units, and the cementitious bonds between the contaminated fine particles and the ballast are characterized by the parallel bond model. The introduction of the temperature-stiffness coupling factor and the temperature-strength coupling factor is specifically expressed as follows: ； In the formula, is the temperature-stiffness coupling factor, is the real-time temperature of the environment where the ballast bed is located, is the reference temperature, is the reference temperature and the initial stiffness at this reference temperature, is the natural constant, is the first temperature attenuation coefficient, which is used to characterize the attenuation rate of the bond stiffness with the increase of temperature and is calibrated through experiments; is the temperature-strength coupling factor, is the reference temperature and the initial strength at this reference temperature, is the second temperature attenuation coefficient, which is used to characterize the attenuation rate of the bond strength with the increase of temperature and is calibrated through experiments; and The calibration method for is as follows: Using a high-precision bidirectional loading tester, within the range of -10°C to 20°C, at intervals of 5°C, conduct shear tests on the dirty ballast specimens with a fixed dirt content, and measure the bond stiffness and bond strength when the bonding key fails. Then, through non-linear regression fitting, determine and ; and the bond strength , and through non-linear regression fitting, determine and ; The contact force between particles is expressed by the following formula: ； In the formula, is the contact force between particles, is the relative displacement between particles, that is, the overlapping displacement, represents the contact area.

3. The method for identifying diseases of a dirty roadbed during the spring thawing period according to claim 1, characterized in that: Use a partitioned stepped temperature-controlled large triaxial test device to obtain the mechanical response data of contaminated ballast specimens under different temperature gradients, contamination contents and dynamic loads. The mechanical response data includes the dynamic stress amplitude, the cumulative energy dissipation, and the failure stress, temperature and moisture content when the specimen is damaged. The range of the temperature gradient is [-10°C, 20°C], the range of the contamination content is 5%-20%, specifically the mass ratio of the contaminated fine particles to the contaminated ballast specimen, the frequency range of the dynamic load is 1-5 Hz, and the amplitude range is 50-200 kPa. Based on the plastic shakedown theory, use the mechanical response data to construct a critical dynamic strain calculation model. The formula relied on is as follows: ； In the formula, is the critical dynamic strain, that is, the dynamic strain recorded when the specimen is damaged, is the dynamic stress amplitude, is the reference stress, is the water content, is the temperature, is the reference temperature, is the cumulative energy dissipation, is the reference energy dissipation, which is calibrated through experiments, 、 、 、 and are fitting coefficients, which are determined by nonlinear regression; Among them, the cumulative energy dissipation is calculated as follows: ； In the formula, is the strain rate, that is, the derivative of strain with respect to time, represents the loading time, is a function of strain varying with time, represents a sinusoidal wave load, is the sine function, is the dynamic stress amplitude, and ranges from 50 to 200 kPa, is the pi, is the frequency, and , with the unit of Hz.

4. A method for identifying diseases of a dirty ballast bed during the spring thaw period according to claim 1, characterized in that: Combine the cementitious contact model and the critical dynamic strain calculation model, and based on the discrete element method, establish a ballast bed structure simulation model, simulate the microscopic contact behavior of the ballast bed under the dynamic-melting coupling action during the spring thaw period, and obtain simulation data including the cementitious bond fracture rate, the non-uniformity of force chains and the settlement rate. Model the ballast particles as polyhedral units, simplify the dirty fine particles as small spheres, and fill them in the ballast gaps according to the preset gradation; generate cementitious bonds at the particle contact points, and their stiffness and strength are dynamically updated according to the real-time temperature Apply a sinusoidal fluctuating load at the top of the model, and at the same time set a temperature gradient field to simulate the melting process during the spring thaw period, and monitor the cementitious bond fracture rate, force chain non-uniformity and settlement rate; The preset gradation refers to the range of the mass ratio of the contaminated fine particles to the ballast particles being 5%-20%. The temperature gradient field is [-10°C, 20°C]. The sine wave load has the following expression: ； Wherein, is the sine function, is the dynamic stress amplitude, and ranges from 50 to 200 kPa, is the pi, is the frequency, and , with the unit of Hz, represents the loading time.

5. The method for identifying diseases of a dirty roadbed during the spring thaw period according to claim 4, wherein: The specific logic for monitoring the cementitious bond fracture rate, the non-uniformity of force chains and the settlement rate is as follows: During the simulation process, count the number of cementitious bonds broken in each calculation step, and calculate the ratio with the total number of initial cementitious bonds as the cementitious bond fracture rate, that is: ； In the formula, is the bonding bond fracture rate, is the number of fractured bonding bonds. When the sinusoidal fluctuating load exceeds the contact force , it is determined to be fractured. is the total number of bonding bonds generated during model initialization; the calculation step size is the time increment for each iteration. Calculate the force chain non-uniformity according to the following formula :[[]]END]] ； Wherein, is the average contact force, is the total number of cementitious bonds generated during model initialization, is the contact force at the th contact point, is the index of the contact point, is the standard deviation of the contact force, used to characterize the discreteness of the force chain distribution, is the non-uniformity of the force chain; Select in the central area at the top of the model The particle array is used as the representative particle. Calculate the sedimentation rate of each particle, and take the average of the sedimentation rates of all representative particles to characterize the overall sedimentation rate; Among them, the formula relied on for calculating the settlement rate is as follows: ； In the formula, is the settlement rate, is the dynamic stress amplitude, is the critical dynamic strain, is the proportionality coefficient, which is determined according to the material properties, and .

6. The method for identifying diseases of a dirty roadbed during the spring thaw period according to claim 1, characterized in that: Conduct a correlation analysis on the cementitious bond fracture rate, the non-uniformity of force chains and the settlement rate, and generate a disease assessment index for characterizing the occurrence risk of the ballast bed pumping and mud boiling disease. The formula relied on is as follows: ； In the formula, is the disease assessment index, is the bonding bond fracture rate, is the force chain non-uniformity, is the settlement rate, is the natural constant, , and are preset weights determined according to the analytic hierarchy process.

7. The method for identifying diseases of a dirty roadbed during the spring thaw period according to claim 6, wherein: Determined according to the analytic hierarchy process , and , the specific logic is as follows: Mark the three indicators of the cementitious bond fracture rate, the force chain non-uniformity, and the settlement rate. Determine the relative importance values between each pair through the nine-scale method, and construct a judgment matrix. Among them, mark the cementitious bond fracture rate as 1, the force chain non-uniformity as 2, and the settlement rate as 3. The constructed judgment matrix is as follows: ； Among them, and both represent the indices of exponents, and , , represent that the exponent with index is more important than the exponent with index . The importance is measured using a 1-9 scale, and the larger the value, the more important the exponent with index is compared to the exponent with index , and , ; Divide each element value in the judgment matrix by the sum of its column to obtain the normalized judgment matrix. Calculate the mean value of each row element value in the normalized judgment matrix. Take the mean value of the first row element value as the weight of the cementitious bond fracture rate, the mean value of the second row element value as the weight of the force chain non-uniformity, and the mean value of the third row element value as the weight of the settlement rate. With the constraint that the sum of the scaled values is equal to 1, scale the three weights proportionally, and use the scaled weights as the proportional coefficients of the corresponding exponents.

8. A method for identifying diseases of dirty roadbeds during the spring thaw period according to claim 1, characterized in that: Compare the disease assessment index with a preset threshold value according to the following specific logic: When it is judged as a low-risk level, indicating that the current ballast bed state is relatively good, and the bonding key fracture rate, force chain non-uniformity, and settlement rate are all within the acceptable range; When it is judged as a medium risk level, indicating that there is a certain degree of disease risk in the current roadbed, and monitoring and assessment measures should be taken, and maintenance should be implemented if necessary; When it is judged as a high-risk level, indicating that there is a relatively high risk of mud pumping and gushing disease in the current roadbed. The fracture rate of the bonding keys, the non-uniformity of the force chains, and the settlement rate have all reached dangerous levels, and on-site evaluation and inspection should be carried out immediately, and emergency maintenance measures should be taken.

9. The method for identifying diseases of a dirty roadbed during the spring thaw period according to claim 8, wherein: Preset threshold The calibration method is as follows: ； Wherein, is the basic risk threshold, determined by the expert evaluation method, is the real-time temperature of the environment where the ballast bed is located, is the moisture content; and are step functions. When , , otherwise . When , , otherwise .

10. A device for identifying diseases of dirty ballast beds during the spring thaw period, characterized in that: The described spring thaw period dirty ballast bed disease identification device is used to execute the spring thaw period dirty ballast bed disease identification method according to any one of claims 1-9, and includes: A discontinuous grading aggregate cementitious contact model construction module, based on the discrete element simulation method, establishes a discontinuous grading aggregate cementitious contact model, and introduces a temperature-stiffness coupling factor and a temperature-strength coupling factor into the model. Respectively characterize the dynamic influence of the melting effect on the cementitious bond stiffness and the cementitious bond strength through the temperature-stiffness coupling factor and the temperature-strength coupling factor; A critical dynamic strain calculation model construction module, which is used to obtain the mechanical response data of dirty ballast under different temperature gradients, dirty content, and dynamic loads through a partitioned stepped temperature-controlled large triaxial test device, and construct a critical dynamic strain calculation model based on the plastic shakedown theory using the mechanical response data; A ballast bed structure simulation model construction module, which is used to establish a ballast bed structure simulation model by combining the cementitious contact model and the critical dynamic strain calculation model, simulate the microscopic contact behavior of the ballast bed under the dynamic-melting coupling action during the spring thaw period, and obtain simulation data including the cementitious bond fracture rate, the force chain non-uniformity, and the settlement rate; A disease risk assessment module, which is used to generate a disease assessment index of the ballast bed by performing a correlation analysis on the cementitious bond fracture rate, the force chain non-uniformity, and the settlement rate, compare the disease assessment index with a preset threshold, and evaluate the occurrence risk of the ballast bed pumping and mud boiling disease according to the comparison result.

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