Railway dirty track bed slurry disease evaluation method

Abstract

The present application provides a kind of railway dirty way bed slurry and mud disease evaluation method, it is related to geotechnical engineering technical field, the present application obtains the continuous dynamic profile of railway bed in the process of vehicle passing, and the static profile before the vehicle passing, based on Sobel operator processing profile, to determine slurry and mud area and normal area, determine the void pore pixel point and mud jam pore pixel point of slurry and mud area;And judge the slurry and mud disease stage of way bed, according to the edge profile of slurry and mud area to judge the static instability degree of way bed;Compare adjacent time dynamic profile to determine the dynamic expansion of slurry and mud area and the situation of pore being dyed mud, combine static instability degree, dynamic expansion and pore being dyed mud situation, and the slurry and mud disease of way bed is evaluated as mild, moderate and severe.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering technology, specifically to a method and system for evaluating mud pumping and frost damage in dirty railway trackbeds. Background Technology

[0002] Ground-penetrating radar profiles are an important tool for analyzing track bed defects. The profiles can directly reflect the voiding and blockage of internal pores in the track bed. The main cause of track bed mud pumping and frost heave is that various loads cause a large number of pores in the track bed, leading to voiding. In spring, due to snowmelt and abundant rainfall, a large amount of water seeps into the track bed. The water combines with fine particles in the track bed to form mud, which remains in the pores. This mud trapped in the pores is the main cause of track bed mud pumping and frost heave. Currently, most assessments of track bed defects are limited to the study of static pores, and the evolution of defects in pores under dynamic loads is rarely mentioned.

[0003] In the prior art, application number CN202411152971.8 discloses a detection method and device for predicting railway frost heave and mudslide. This method obtains the railway subgrade filler coefficient, cyclic load coefficient, and radar detection coefficient; calculates the risk value of railway frost heave and mudslide based on these coefficients; sets a risk value threshold; compares the frost heave and mudslide risk value with the threshold; and predicts frost heave and mudslide if the risk value is greater than or equal to the threshold. However, this prior art still has shortcomings. Although the application analyzes radar signals, it is limited to analyzing and calculating the free moisture content of the track bed, without deeply analyzing the evolution of moisture in the pores, thus failing to analyze and determine the evolution of frost heave and mudslide damage in the track bed.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for evaluating mud pumping and frost damage in railway ballast beds, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for evaluating mud pumping and frost damage in dirty railway track beds, comprising the following steps:

[0008] Step 1: Obtain the continuous dynamic profile of the railway track bed during the passage of vehicles, as well as the static profile before the passage of vehicles. Process the profile based on the Sobel operator to determine the mud-blowing area and the normal area. Based on the gray values ​​of the mud-blowing area and the normal area, determine the delamination pore pixels and mud-blocking pore pixels in the mud-blowing area.

[0009] Step 2: Based on the pixel values ​​of voided pores and mud-blocked pores in the static profile, determine the stage of mud pumping and mud spillage in the track bed. Based on the continuity and smoothness of the edge contour of the mud pumping and mud spillage area, determine the static instability of mud pumping and mud spillage in the track bed.

[0010] Step 3: Compare the pixels of vacuoled pores and mud-blocked pores in the dynamic profile at adjacent time points to determine the expansion of the mud-blowing area and the mud-contamination of pores, so as to characterize the deterioration trend of mud-blowing disease.

[0011] Step 4: Combining the static instability of the track bed frost heave and the deterioration trend of the frost heave disease, construct a disease occurrence index that reflects the probability of frost heave disease occurring in the delaminated track bed. Based on the disease stage and the disease occurrence index, the frost heave disease of the track bed is rated as mild, moderate and severe.

[0012] Furthermore, the Sobel operator is used to calculate the horizontal and vertical gradient values ​​of each pixel in the railway track bed profile, and the error rate between the gradient magnitude of the current pixel and the gradient magnitude of the adjacent pixels is determined. Pixels with an error rate greater than the error rate threshold are defined as edge pixels. Edge contours are extracted based on the edge pixels to determine the mud-blowing area and the normal area. Closed areas are defined as mud-blowing areas, and unclosed areas are defined as normal areas.

[0013] Furthermore, when calculating the horizontal and vertical gradient values ​​of each pixel, the gray value of the pixel and its neighboring pixels are multiplied by the horizontal gradient template of the Sobel operator, and the results of all multiplications are summed to obtain the horizontal gradient value of the pixel. Similarly, the gray value of the pixel and its neighboring pixels are multiplied by the vertical gradient template of the Sobel operator, and the results of all multiplications are summed to obtain the vertical gradient value of the pixel. The formulas used to calculate the horizontal and vertical gradient values ​​are as follows:

[0014]

[0015]

[0016] in, and The first line, number The horizontal and vertical gradient values ​​of the column pixels; For the first line, number The grayscale values ​​of the column pixels, , This is the index of the pixels in the railway track bed cross-section.

[0017] The formula used to generate the gradient magnitude of a pixel is:

[0018]

[0019] in, For the first line, number Gradient magnitude of column pixels;

[0020] The formula used to calculate the error rate between the gradient magnitude of the current pixel and the gradient magnitudes of its neighboring pixels is as follows:

[0021]

[0022] in, In order to be with the first line, number The average gradient magnitude of all adjacent pixels of a column pixel. For the first line, number Error rate of column pixels.

[0023] Furthermore, the maximum and minimum gray values ​​of the normal area are obtained. Pixels with gray values ​​greater than the maximum gray value in the mud-blowing area are called mud-blocked pore pixels, and pixels with gray values ​​less than the minimum gray value in the mud-blowing area are called vacuolated pore pixels. The ratio of vacuolated pore pixels to mud-blocked pore pixels is calculated as the vacuolation-blocking ratio. A vacuolation-blocking threshold is preset. If the vacuolation-blocking ratio is greater than the vacuolation-blocking threshold, the mud-blowing disease is judged to be in the vacuolation stage. If the vacuolation-blocking ratio is not greater than the vacuolation-blocking threshold, the mud-blowing disease is judged to be in the mud-blowing stage.

[0024] Furthermore, the grayscale values ​​of the pixels of each edge region's edge contour are first processed by variance to obtain the stable variance of each region of each edge contour. Then, the stable variance of the edge contour of each of all edge regions is averaged to obtain the average stable variance of the track bed.

[0025] Perform curvature analysis on each edge contour to obtain the average absolute curvature of each edge contour; perform average value analysis on the average absolute curvature of all edge contours to obtain the comprehensive average absolute curvature.

[0026] The average stability variance and the comprehensive average absolute curvature of the track bed are weighted to obtain the static instability assessment coefficient, which is used to quantify the degree of static instability.

[0027] Furthermore, during the vehicle's passage, dynamic profile images are collected at equal time intervals at each moment, and the first... The pixel set of vacuoles and the pixel set of mud-blocked pores at each time point are respectively labeled as follows: , ;

[0028] Indicators characterizing the worsening and evolution trend of mudslide disease include: average conversion rate, average growth rate of devoted pore pixels, and average year-on-year growth rate of mud-blocked pore pixels; among them, the average growth rate of devoted pore pixels and the average year-on-year growth rate of mud-blocked pore pixels are used to characterize the expansion of the mudslide area, and the average conversion rate is used to reflect the conversion of newly generated devoted pore pixels into mud-blocked pore pixels, so as to characterize the pores being contaminated by mud.

[0029] The specific formula is as follows:

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] in, For the first The growth rate of the number of pixels with voids at each time point For the first Year-on-year growth rate of pixels in mud-clogged pores at any given time. Indicates the number of elements in the set. In set operations, belonging to But not belonging to Elements in; For the first The conversion rate of newly generated voided pore pixels to mud-clogged pore pixels at a given time point. The average growth rate of pixels with voids. The average year-on-year growth rate of pixels in the pores blocked by mud. This represents the average conversion rate. For the first The set of vacuolated pixels at each time point For the first The set of vacuolated pixels at each time point For the first The set of vacuolated pixels at each time point For the first The set of pixels representing mud clogging pores at a given time. For the first The set of pixels representing mud clogging pores at a given time. For the first The set of pixels representing mud clogging pores at a given time. For indexing time, This represents the total number of moments.

[0037] The specific formula for constructing disease occurrence indicators is as follows:

[0038]

[0039] in, As an indicator of disease occurrence, This is the static instability assessment coefficient. , and These are the weighting coefficients, and ;

[0040] A preset threshold for the occurrence of diseases is set. If the track bed is in the voiding stage and the disease occurrence index is not greater than the disease occurrence threshold, the track bed mud pumping disease is rated as mild. If the track bed is in the voiding stage and the disease occurrence index is greater than the disease occurrence threshold, the track bed mud pumping disease is rated as moderate. If the track bed is in the mud pumping stage, the track bed mud pumping disease is rated as severe.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] This invention first identifies the mud-blowing and sluice-filled areas of the track bed, and judges the static instability of the mud-blowing and sluice-filled areas based on the continuity and smoothness of the edges of the mud-blowing and sluice-filled areas. It also obtains the expansion of the mud-blowing and sluice-filled areas and the mud-filled pores by observing the changes in track bed porosity under train load. Combining the static instability and dynamic evolution of track bed defects, an effective assessment of the track bed is completed. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0044] Figure 2 This is a fitted curve of the average growth rate of the present invention and the disease occurrence index;

[0045] Figure 3This is a fitted curve of the average year-on-year growth rate of the present invention and the disease occurrence index;

[0046] Figure 4 This is a fitted curve of the average conversion rate and disease occurrence index of the present invention;

[0047] Figure 5 This is the fitting curve between the static instability evaluation coefficient and the disease occurrence index of this invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0049] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0050] Example:

[0051] Please see Figure 1 The present invention provides a technical solution:

[0052] A method for evaluating mud pumping and frost damage in dirty railway track beds, comprising the following steps:

[0053] Step 1: Obtain the continuous dynamic profile of the railway track bed during the passage of vehicles, as well as the static profile before the passage of vehicles. Process the profile based on the Sobel operator to determine the mud-blowing area and the normal area. Based on the gray values ​​of the mud-blowing area and the normal area, determine the delamination pore pixels and mud-blocking pore pixels in the mud-blowing area.

[0054] The profile image was obtained using ground penetrating radar. The reflection intensity of the image depends on the difference in dielectric constant of the medium. In areas of mud heave and sluice filling, the intrusion of mud leads to a large difference in dielectric constant between the mud and the ballast, resulting in a strong reflection signal.

[0055] The Sobel operator is used to calculate the horizontal and vertical gradient values ​​of each pixel in the railway track bed profile. The error rate between the gradient magnitude of the current pixel and its adjacent pixels is determined. Pixels with an error rate greater than a threshold are defined as edge pixels. Edge contours are extracted based on these edge pixels to identify areas prone to mudslides and frost, and normal areas. Closed areas are designated as mudslide areas, while open areas are designated as normal areas. The error rate threshold is determined by experts in the field based on specific circumstances. The error rate of the railway track bed profile is calculated using the following method, and then experts in the field are invited to evaluate the error rate to determine the threshold. This is existing technology and will not be elaborated upon here.

[0056] Mud pumping is a manifestation of roadbed defects, typically caused by softening of the subgrade soil and water infiltration leading to mud slurry rising to the surface. In images, these areas appear as: mud accumulation forming a continuous boundary separated from the surrounding roadbed material. Due to limited mud diffusion, the edges are often closed; therefore, closed areas are defined as mud pumping zones. Conversely, normal roadbeds usually exhibit a uniform texture or locally loose particles. Their edge characteristics include: discontinuous or open boundaries: such as gaps between gravel, localized wear, or natural undulations. These edges may be detected due to noise or minor gradient changes, but they do not form closed structures. The gradient amplitude in normal areas may be due to texture, but there is a lack of continuity between adjacent edge points. Therefore, non-closed areas are defined as normal areas.

[0057] To calculate the horizontal and vertical gradient values ​​for each pixel, the grayscale value of the pixel and its neighboring pixels are multiplied by the horizontal gradient template of the Sobel operator, and the results of all multiplications are summed to obtain the horizontal gradient value of the pixel. Similarly, the grayscale value of the pixel and its neighboring pixels are multiplied by the vertical gradient template of the Sobel operator, and the results of all multiplications are summed to obtain the vertical gradient value of the pixel. The formulas used to calculate the horizontal and vertical gradient values ​​are as follows:

[0058]

[0059]

[0060] in, and The first line, number The horizontal and vertical gradient values ​​of the column pixels; For the first line, number The grayscale values ​​of the column pixels, , This is the index of the pixels in the railway track bed cross-section.

[0061] The formula used to generate the gradient magnitude of a pixel is:

[0062]

[0063] in, For the first line, number Gradient magnitude of column pixels;

[0064] The formula used to calculate the error rate between the gradient magnitude of the current pixel and the gradient magnitudes of its neighboring pixels is as follows:

[0065]

[0066] in, In order to be with the first line, number The average gradient magnitude of all adjacent pixels of a column pixel. For the first line, number Error rate of column pixels.

[0067] Step 2: Based on the pixel values ​​of voided pores and mud-blocked pores in the static profile, determine the stage of mud pumping and mud spillage in the track bed. Based on the continuity and smoothness of the edge contour of the mud pumping and mud spillage area, determine the static instability of mud pumping and mud spillage in the track bed.

[0068] The maximum and minimum gray values ​​of the normal area are obtained. Pixels with gray values ​​greater than the maximum gray value in the mud-blowing area are called mud-blocked pore pixels, and pixels with gray values ​​less than the minimum gray value in the mud-blowing area are called vacuolated pore pixels. The density or composition difference of mud makes it brighter in the image. After mud invades the pores of the track bed, it may form a dense and moist blockage, which appears as high reflectivity in the image. For vacuolated pores, there is no material filling the cavity, which appears as low reflectivity in the image.

[0069] The ratio of vacuolated pore pixels to mud-blocked pore pixels is calculated as the vacuolation / blocking ratio. A vacuolation / blocking threshold is preset. If the vacuolation / blocking ratio is greater than the threshold, the mud pumping disease is judged to be in the vacuolation stage. In the early stage of mud pumping disease, a large number of vacuolated pixels are generated, and most of them are not blocked by mud. Usually, mud pumping problems will not occur. However, under train load, the suction effect caused by the train load may cause mud pumping. If the vacuolation / blocking ratio is not greater than the threshold, the mud pumping disease is judged to be in the mud pumping stage. At this stage, there are already a large number of pixels in the track bed blocked by mud. Under the action of a train, mud pumping will occur. Even without train load, mud pumping may occur under slightly violent geological activity.

[0070] The de-cavitation and blockage threshold is determined by experts based on local conditions. For example, the de-cavitation and blockage ratio of the dirty roadbed can be calculated using the above method, and then experts in the field can be invited to demonstrate the mud-blowing and mud-shedding disease of the dirty roadbed and the de-cavitation and blockage ratio to determine the de-cavitation and blockage threshold. This is existing technology and will not be elaborated here.

[0071] The grayscale values ​​of each edge contour pixel are first processed to obtain the stable variance of each edge contour. Then, the stable variance of all edge contours is averaged to obtain the average stable variance of the track bed.

[0072] Perform curvature analysis on each edge contour to obtain the average absolute curvature of each edge contour; perform average value analysis on the average absolute curvature of all edge contours to obtain the comprehensive average absolute curvature; before performing the average value calculation, first standardize the average absolute curvature.

[0073] The static instability assessment coefficient is obtained by weighting the mean stability variance and the comprehensive mean absolute curvature of the track bed, and is used to quantify the degree of static instability. The formula is as follows:

[0074]

[0075] The static instability assessment coefficient is... The average stability variance of the track bed, To achieve the average absolute curvature, , These are the weighting coefficients, and ,and .

[0076] The average stability variance of the track bed reflects the ambiguity of the edges. The smaller the value, the clearer the edges. Under train load, blurred edges are more prone to porosity due to uneven distribution of load disturbance. Porosity is the basis of defects, and defects are more likely to expand. Conversely, the clearer the boundary, the less likely the defect will expand. The comprehensive average absolute curvature reflects the smoothness of the boundary. The larger the value, the less smooth the boundary. Less smooth boundaries are more prone to mud adhesion, which in turn exacerbates mud pumping defects. Since the instability in this embodiment refers to mud pumping defects, the instability brought by the average stability variance of the track bed only provides the basis for mud pumping defects, while the instability brought by the comprehensive average absolute curvature directly causes mud pumping defects. Therefore, when setting the weights, a weight is set. .

[0077] The static instability assessment coefficient analyzes the continuity and smoothness of the edge contour of the mud-blowing area, reflects the degree of static instability of the diseased area, and reveals the static synergistic destructive nature of pore formation and mud retention. The smaller the value, the more stable the track bed is and the less likely it is to expand and be blocked. The static instability assessment coefficient is used to quantify the degree of static instability of the diseased area, transforming the fuzzy edge physical characteristics into actionable engineering decision values, and providing an important theoretical basis for the assessment of mud-blowing disease of track bed.

[0078] Step 3: Compare the pixels of vacuoled pores and mud-blocked pores in the dynamic profile at adjacent time points to determine the expansion of the mud-blowing area and the mud-contamination of pores, so as to characterize the deterioration trend of mud-blowing disease.

[0079] During the vehicle's passage, dynamic profile images are collected at equal time intervals at each moment, and the first... The pixel set of vacuoles and the pixel set of mud-blocked pores at each time point are respectively labeled as follows: , ;

[0080] Since it usually takes 1-2 minutes for a train to completely pass through a position, the time interval is usually set between 1-3 seconds in order to observe the changes in the aperture and to facilitate calculations. In this embodiment, the time interval is 2 seconds.

[0081] Indicators characterizing the worsening and evolution trend of mudslide disease include: average conversion rate, average growth rate of devoted pore pixels, and average year-on-year growth rate of mud-blocked pore pixels; among them, the average growth rate of devoted pore pixels and the average year-on-year growth rate of mud-blocked pore pixels are used to characterize the expansion of the mudslide area, and the average conversion rate is used to reflect the conversion of newly generated devoted pore pixels into mud-blocked pore pixels, so as to characterize the pores being contaminated by mud.

[0082] The specific formula is as follows:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] in, For the first The growth rate of the number of pixels with voids at each time point For the first Year-on-year growth rate of pixels in mud-clogged pores at any given time. Indicates the number of elements in the set. In set operations, belonging to But not belonging to Elements in; For the first The conversion rate of newly generated voided pore pixels to mud-clogged pore pixels at a given time point. The average growth rate of pixels with voids. The average year-on-year growth rate of pixels in the pores blocked by mud. This represents the average conversion rate. For the first The set of vacuolated pixels at each time point For the first The set of vacuolated pixels at each time point For the first The set of vacuolated pixels at each time point For the first The set of pixels representing mud clogging pores at a given time. For the first The set of pixels representing mud clogging pores at a given time. For the first The set of pixels representing mud clogging pores at a given time. For indexing time, This represents the total number of moments.

[0090] The growth rate of voided pore pixels reflects the instantaneous net growth rate of voided pores. The average growth rate of voided pore pixels analyzes the instantaneous growth at each moment, comprehensively reflecting the pore growth during train passage. Train vibration combined with geological settlement causes the track bed to separate from the subgrade, creating pores. Moisture can pass through or remain in these pores. Moisture remaining in the pores forms mud, and mud remaining in cracks is the main cause of frost heave and mudslide. Train vibration is the main cause of pore formation. Pore growth provides more conditions for mud in frost heave. The more significant the pore growth under train action, the more likely it is to cause frost heave and mudslide. The average growth rate of voided pore pixels can be used as a quantitative indicator to assess the basic conditions provided by train load for inducing frost heave and mudslide. This represents the porosity increase in the image at a later time interval relative to the image at the previous time interval. Here, we use... This is to avoid the absurd situation where, in cases of severe mud pumping and sludge overflow, the pores in the previous time interval image are more contaminated by mud than new pores are formed, leading to negative pore growth. For normalization, since the analysis is performed on images with adjacent time intervals, and the time intervals are all short, the difference between the quantities is used directly to analyze the growth. The subsequent staining is also analyzed directly using the difference in quantities.

[0091] and This indicates the increase in mud blockage pores in the image at the next time interval relative to the image at the previous time interval, reflecting the growth rate of mud blockage pores. This is used to represent the acceleration of the growth of pores blocked by mud. Used for normalization, Reflects the instantaneous acceleration of mud blockage in pores. This study comprehensively reflects the accelerated growth of mud clogging pores during train passage. Because it uses an average analysis of the entire train passage process, and the train's passage significantly promotes pore contamination, therefore... The value will not be less than 0. Mud clogging the pores is the most direct cause of mud pumping disease. It reflects the accelerated growth of mud clogging the pores and can further reflect the situation of the pores being about to be contaminated. The larger the value, the more serious the situation of the pores being about to be contaminated. The speed of pore contamination can directly reflect the evolution of mud pumping disease and can provide a direct theoretical basis for the assessment of mud pumping disease.

[0092] This indicates the number of newly formed pores that were blocked by mud. This represents the conversion rate of newly generated voided pore pixels to mud-clogged pore pixels in a transient state. right Average values ​​were applied to directly quantify the global efficiency of rapid mud infiltration into newly formed voids under train loads. A higher value indicates a faster rate of mud infiltration into the newly formed voids. The infiltration of newly formed voids indicates two scenarios: either multiple mud-infiltrated voids exist around the newly formed voids, or a significant suction effect occurs under the combined action of train loads, leading to intense water and mud migration in the railway track bed foundation. Both scenarios indicate a worsening of the track bed frost heave and mud leakage disease. Typically, if frost heave and mud leakage do not occur directly, the average conversion rate is relatively small, even approaching zero. Therefore, the magnitude of the average conversion rate provides an important basis for assessing track bed frost heave and mud leakage diseases.

[0093] Step 4: Combining the static instability of the track bed frost heave and the deterioration trend of the frost heave disease, construct a disease occurrence index that reflects the probability of frost heave disease occurring in the delaminated track bed. Based on the disease stage and the disease occurrence index, the frost heave disease of the track bed is rated as mild, moderate and severe.

[0094] The specific formula for constructing disease occurrence indicators is as follows:

[0095]

[0096] in, As an indicator of disease occurrence, This is the static instability assessment coefficient. , and These are the weighting coefficients, and ;

[0097] Table 1. Statistical Table of Disease Occurrence Indicators

[0098]

[0099] Please see Figure 2-5 According to the statistical data in Table 1, the disease occurrence indicators show a significant positive correlation with the average growth rate, average year-on-year growth rate, average conversion rate, and static instability assessment coefficient, verifying the rationality of the formula design. The specific analysis is as follows: the static instability assessment coefficient, as the basic coefficient, determines the baseline level of AD, and its numerical range (0.079-0.984) is related to... The values ​​(0.034-2.688) showed a significant positive correlation; among the three dynamic indicators, and right The sum of the effects and Quite a lot, of which The impact is slightly greater; the conversion rate of the mud has a slightly greater effect. The impact of this factor is most pronounced, with its numerical variation (0.1-1.0) directly dominating the growth trend of AD values, especially in... The AD value showed an exponential increase, which perfectly matches the sudden nature of frost heave and mudslide disease; data patterns show that... and hour, The value growth rate accelerated significantly, verifying the rationality of the nonlinear weighted design in the formula. It can accurately reflect the disease development mechanism of "static instability accumulation → dynamic pore evolution → mud concentrating infiltration", which is in line with the understanding of the evolution process of mud pumping disease in geotechnical engineering theory.

[0100] The static instability assessment coefficient analyzes the continuity and smoothness of the edge contour of the mud-blowing area, reflects the degree of static instability of the diseased area, and reveals the static synergistic disease-causing nature of porosity formation and mud retention. The smaller the value, the more stable the track bed is and the less likely it is to expand and be blocked. , and The dynamic evolution of frost heave disease was assessed from the perspectives of the evolution of basic conditions, the evolution of direct inducing conditions, and the evolution of disease severity. Since these three factors are progressive, relatively speaking, for frost heave disease, if... The larger the size, the more severe the disease, therefore the higher the weighting. A larger value indicates a higher probability of frost heave and mudslide, and that the phenomenon is likely to escalate further. A relatively large number of cases only indicates the presence of the underlying conditions for frost heave and mudslide disease. Other conditions are needed for these conditions to develop into frost heave and mudslide disease. Therefore, [the following is a separate, unrelated sentence:] Setting [the appropriate conditions]... .

[0101] The disease occurrence index combines dynamic and static factors to comprehensively quantify the severity of slurry heave and mud leakage during the track bed delamination stage. A higher value indicates a more severe slurry heave and mud leakage. During the track bed delamination stage, if the delamination is simple or only a few pores are occupied by mud, and the deterioration under train load is not severe, slurry heave and mud leakage will not occur. However, if many pores are occupied by mud or the suction effect caused by train load is significant, slurry heave and mud leakage will occur under train load. The significance of this suction effect is related to vehicle activity and the static properties of the affected area. The disease occurrence index, which combines dynamic and static factors, can effectively determine whether slurry heave and mud leakage will occur during the delamination stage under train load.

[0102] A preset threshold for the occurrence of track defects is set. If the track bed is in the voiding stage and the defect occurrence index is not greater than the threshold, the track bed mud pumping defect is rated as mild, and mud pumping will not occur. If the track bed is in the voiding stage and the defect occurrence index is greater than the threshold, the track bed mud pumping defect is rated as moderate, and mud pumping will occur under train load. If the track bed is in the mud pumping stage, the track bed mud pumping defect is rated as severe, and mud pumping will occur even without vehicle load under slightly intense geological activity. Under train load, the mud pumping will be more significant.

[0103] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0104] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0105] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that cannot be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for evaluating mud pumping and frost damage in dirty railway track beds, characterized in that, The specific steps include: Step 1: Obtain the continuous dynamic profile of the railway track bed during the passage of vehicles, as well as the static profile before the passage of vehicles. Process the profile based on the Sobel operator to determine the mud-blowing area and the normal area. Based on the gray values ​​of the mud-blowing area and the normal area, determine the delamination pore pixels and mud-blocking pore pixels in the mud-blowing area. Step 2: Based on the pixel values ​​of voided pores and mud-blocked pores in the static profile, determine the stage of mud pumping and mud spillage in the track bed. Based on the continuity and smoothness of the edge contour of the mud pumping and mud spillage area, determine the static instability of mud pumping and mud spillage in the track bed. Step 3: Compare the pixels of vacuoled pores and mud-blocked pores in the dynamic profile at adjacent time points to determine the expansion of the mud-blowing area and the mud-contamination of pores, so as to characterize the deterioration trend of mud-blowing disease. Step 4: Combining the static instability of the track bed frost heave and the deterioration trend of the frost heave disease, construct a disease occurrence index that reflects the probability of frost heave disease occurring in the delaminated track bed. Based on the disease stage and the disease occurrence index, the frost heave disease of the track bed is rated as mild, moderate and severe. Obtain the maximum and minimum gray values ​​of the normal area. Pixels with gray values ​​greater than the maximum gray value in the mud-blowing area are called mud-blocked pore pixels, and pixels with gray values ​​less than the minimum gray value in the mud-blowing area are called vacuolated pore pixels. Calculate the ratio of vacuolated pore pixels to mud-blocked pore pixels as the vacuolation / blocking ratio. Set a vacuolation / blocking threshold. If the vacuolation / blocking ratio is greater than the vacuolation / blocking threshold, the mud-blowing disease is determined to be in the vacuolation stage. If the vacuolation / blocking ratio is not greater than the vacuolation / blocking threshold, the mud-blowing disease is determined to be in the mud-blowing / mud-blowing stage. The grayscale values ​​of each edge contour pixel are first processed to obtain the stable variance of each edge contour. Then, the stable variance of all edge contours is averaged to obtain the average stable variance of the track bed. Perform curvature analysis on each edge contour to obtain the average absolute curvature of each edge contour; perform average value analysis on the average absolute curvature of all edge contours to obtain the comprehensive average absolute curvature. The average stability variance and the comprehensive average absolute curvature of the track bed are weighted to obtain the static instability assessment coefficient, which is used to quantify the degree of static instability.

2. The method for evaluating mud pumping and frost damage in dirty railway track beds according to claim 1, characterized in that: The Sobel operator is used to calculate the horizontal and vertical gradient values ​​of each pixel in the railway track bed profile. The error rate between the gradient magnitude of the current pixel and the gradient magnitude of the adjacent pixels is determined. Pixels with an error rate greater than the error rate threshold are defined as edge pixels. Edge contours are extracted based on the edge pixels to determine the mud-blowing and mud-flooding areas and normal areas. Closed areas are defined as mud-blowing and mud-flooding areas, and open areas are defined as normal areas.

3. The method for evaluating mud pumping and frost damage in a dirty railway track bed according to claim 2, characterized in that: To calculate the horizontal and vertical gradient values ​​for each pixel, the grayscale value of the pixel and its neighboring pixels are multiplied by the horizontal gradient template of the Sobel operator, and the results of all multiplications are summed to obtain the horizontal gradient value of the pixel. Similarly, the grayscale value of the pixel and its neighboring pixels are multiplied by the vertical gradient template of the Sobel operator, and the results of all multiplications are summed to obtain the vertical gradient value of the pixel. The formulas used to calculate the horizontal and vertical gradient values ​​are as follows: in, and The first line, number The horizontal and vertical gradient values ​​of the column pixels; For the first line, number The grayscale values ​​of the column pixels, , This is the index of the pixels in the railway track bed cross-section. The formula used to generate the gradient magnitude of a pixel is: in, For the first line, number Gradient magnitude of column pixels; The formula used to calculate the error rate between the gradient magnitude of the current pixel and the gradient magnitudes of its neighboring pixels is as follows: in, In order to be with the first line, number The average gradient magnitude of all adjacent pixels of a column pixel. For the first line, number Error rate of column pixels.

4. The method for evaluating mud pumping and frost damage in dirty railway track beds according to claim 1, characterized in that: During the vehicle's passage, dynamic profile images are collected at equal time intervals at each moment, and the first... The pixel set of vacuoles and the pixel set of mud-blocked pores at each time point are respectively labeled as follows: , ; Indicators characterizing the worsening and evolution trend of mudslide disease include: average conversion rate, average growth rate of devoted pore pixels, and average year-on-year growth rate of mud-blocked pore pixels; among them, the average growth rate of devoted pore pixels and the average year-on-year growth rate of mud-blocked pore pixels are used to characterize the expansion of the mudslide area, and the average conversion rate is used to reflect the conversion of newly generated devoted pore pixels into mud-blocked pore pixels, so as to characterize the pores being contaminated by mud. The specific formula is as follows: in, For the first The growth rate of the number of pixels with voids at each time point For the first Year-on-year growth rate of pixels in mud-clogged pores at any given time. Indicates the number of elements in the set. In set operations, belonging to But not belonging to Elements in; For the first The conversion rate of newly generated voided pore pixels to mud-clogged pore pixels at a given time point. The average growth rate of pixels with voids. The average year-on-year growth rate of pixels in the pores blocked by mud. This represents the average conversion rate. For the first The set of vacuolated pixels at each time point For the first The set of vacuolated pixels at each time point For the first The set of vacuolated pixels at each time point For the first The set of pixels representing mud clogging pores at a given time. For the first The set of pixels representing mud clogging pores at a given time. For the first The set of pixels representing mud clogging pores at a given time. For indexing time, This represents the total number of moments.

5. The method for evaluating mud pumping and frost damage in dirty railway track beds according to claim 4, characterized in that: The specific formula for constructing disease occurrence indicators is as follows: in, As an indicator of disease occurrence, This is the static instability assessment coefficient. , and These are the weighting coefficients, and ; A preset threshold for the occurrence of diseases is set. If the track bed is in the voiding stage and the disease occurrence index is not greater than the disease occurrence threshold, the track bed mud pumping disease is rated as mild. If the track bed is in the voiding stage and the disease occurrence index is greater than the disease occurrence threshold, the track bed mud pumping disease is rated as moderate. If the track bed is in the mud pumping stage, the track bed mud pumping disease is rated as severe.

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