A deformation detection method and system for roadbed antifreeze test

Abstract

The present invention relates to the technical field of roadbed deformation detection, and discloses a deformation detection method and system for roadbed antifreeze test, the method comprising: obtaining the roadbed to be detected after freezing the roadbed based on a preset temperature; collecting the real-time temperature of the roadbed to be detected, the roadbed material, and the settlement data, expansion data, roadbed anti-shear data and ground displacement data of the roadbed to be detected, and constructing the deformation data of the roadbed to be detected; obtaining the preset deformation data of the roadbed to be detected based on the relationship between the real-time temperature, the roadbed material and the preset roadbed freezing model established in advance; evaluating the deformation score of the roadbed to be detected according to the relationship between the deformation data and the preset deformation data; and determining the deformation detection level of the roadbed to be detected according to the deformation score of the roadbed to be detected. The present invention accurately evaluates the antifreeze performance and deformation level of the roadbed through multi-dimensional data collection and analysis, combined with the preset freezing model, and effectively improves the detection accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of roadbed deformation detection, and in particular to a deformation detection method and system for roadbed antifreeze test. Background Art

[0002] In recent years, with the rapid development of transportation infrastructure construction, the stability and deformation control of roadbed, as an important part of pavement structure, directly affect the safety and service life of the project. Especially in cold areas, the roadbed is significantly affected by temperature changes, and freeze-thaw cycles can easily lead to material performance degradation and roadbed deformation. Therefore, accurate detection and evaluation of the deformation behavior of roadbed under freezing conditions has become an important technical problem that needs to be solved in the engineering field.

[0003] At present, traditional roadbed detection methods are mainly based on surface settlement and crack observation, but such methods cannot fully reflect the internal deformation characteristics of the roadbed under complex environmental conditions. At the same time, the research on the influence of temperature is relatively limited, and there is a lack of accurate models based on the temperature-material relationship, making it difficult to accurately predict the deformation of the roadbed under different materials and temperature conditions. In addition, in the existing technology, the evaluation criteria for roadbed deformation are also relatively simple and cannot meet the diverse engineering needs.

[0004] Therefore, there is an urgent need to invent a roadbed material detection technology to solve the problems that traditional roadbed detection methods cannot fully reflect the internal deformation characteristics of the roadbed in complex environments, and lack accurate models and diversified evaluation criteria based on the temperature-material relationship. Summary of the invention

[0005] In view of this, the present invention proposes a deformation detection method and system for roadbed antifreeze experiments, aiming to solve the problems that traditional roadbed detection methods cannot fully reflect the internal deformation characteristics of the roadbed under complex environments, and lack accurate models and diversified evaluation criteria based on temperature-material relationships.

[0006] The present invention proposes a deformation detection method for roadbed antifreeze test, comprising:

[0007] After freezing the roadbed based on a preset temperature, the roadbed to be inspected is obtained;

[0008] Collecting the real-time temperature of the roadbed to be detected, the roadbed material and the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed anti-shear data and the ground displacement data of the roadbed material, and constructing the deformation data of the roadbed to be detected according to the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed anti-shear data and the ground displacement data of the roadbed material;

[0009] Based on the relationship between the real-time temperature, the roadbed material and the preset roadbed freezing model established in advance, obtaining preset deformation data of the roadbed to be detected;

[0010] According to the relationship between the deformation data and the preset deformation data, evaluating the deformation score of the roadbed to be detected;

[0011] The deformation detection level to be outputted of the roadbed to be detected is determined according to the magnitude relationship between the deformation score of the roadbed to be detected and each pre-configured preset deformation score.

[0012] Furthermore, the pre-established roadbed freezing model includes:

[0013] Obtaining deformation data of each of the roadbed materials at each freezing temperature, wherein the deformation data includes settlement data of the roadbed material, expansion data of the roadbed material, anti-shear data of the roadbed and ground displacement data of the roadbed material;

[0014] Establishing a temperature correlation equation based on the settlement data of the roadbed material, the expansion data of the roadbed material, the anti-shear data of the roadbed and the ground displacement data of the roadbed material at each of the freezing temperatures;

[0015] Obtaining a distance metric between each of the temperature correlation equations, and establishing a distance matrix according to the distance metric;

[0016] Iteratively merging the temperature correlation equations according to the distance matrix, and determining the freezing type of the roadbed material according to the merged temperature correlation equations;

[0017] Clustering the temperature correlation equations according to the freezing type, and obtaining the temperature linear relationship between the temperature correlation equations under the freezing type after clustering;

[0018] The roadbed freezing model is established according to the linear relationship of the temperature of each roadbed material.

[0019] Furthermore, the pre-established roadbed freezing model also includes:

[0020] Obtaining deformation data of any of the roadbed materials at various freezing temperatures, and substituting the roadbed material and the freezing temperature into the roadbed freezing model to obtain the preset deformation data;

[0021] Determining whether to modify the roadbed freezing model according to the relationship between the deformation data and the preset deformation data;

[0022] When the deformation data is consistent with the preset deformation data, it is determined that the roadbed freezing model is not to be corrected;

[0023] When the deformation data is inconsistent with the preset deformation data, the distance matrix is ​​iteratively corrected until the deformation data is consistent with the preset deformation data.

[0024] Furthermore, obtaining the ground displacement data of each roadbed material at each freezing temperature includes:

[0025] The elastic modulus of the roadbed material is obtained, and the ground displacement data of the roadbed material is determined according to the settlement data of the roadbed material, the expansion data of the roadbed material, the roadbed shear resistance data and the elastic modulus of the roadbed material and formula I, wherein formula I is as follows:

[0026] ;

[0027] Wherein, ΔD(T) is the ground displacement data of the roadbed material, ΔS(T) is the settlement data of the roadbed material, ΔV(T) is the expansion data of the roadbed material, τ(T) is the anti-shear data of the roadbed, and E is the elastic modulus of the roadbed material. The step of obtaining the anti-shear data τ(T) of the roadbed includes:

[0028] Obtaining a change in the shear strength of the roadbed material between various freezing temperatures, and determining a change coefficient of the roadbed material according to the change in the shear strength;

[0029] According to the variation coefficient, freezing temperature and formula II, the roadbed anti-shear data τ(T) is determined, wherein formula II is as follows:

[0030] ;

[0031] Wherein, τ0 is the initial shear strength at zero temperature, α is the coefficient of variation of the roadbed material based on the freezing temperature, and ΔT is the temperature difference between the freezing temperature and the zero temperature.

[0032] Furthermore, when determining the variation coefficient of the roadbed material according to the variation of the shear strength, it includes:

[0033] Obtaining a strength difference between the shear strengths of any two adjacent roadbed materials, and determining the variation coefficient according to a relationship between the strength difference and a pre-configured first preset strength difference and a second preset strength difference;

[0034] When the intensity difference is less than the first preset intensity difference, the variation coefficient is determined to be L1;

[0035] When the intensity difference is greater than or equal to the first preset intensity difference, and the intensity difference is less than the second preset intensity difference, determining the variation coefficient to be L2;

[0036] When the intensity difference is greater than or equal to the second preset intensity difference, determining the variation coefficient to be L3;

[0037] Among them, the first preset intensity difference is smaller than the second preset intensity difference; α=L1, L2, L3…Li, and L1<L2<L3<…<Li.

[0038] Further, when evaluating the deformation score of the roadbed to be detected according to the relationship between the deformation data and the preset deformation data, it includes:

[0039] Acquire real-time settlement data in the deformation data and preset settlement data in the preset deformation data, and determine the deformation basic score of the roadbed to be detected according to the settlement difference between the real-time settlement data and the preset settlement data;

[0040] Wherein, a first preset settlement difference value and a second preset settlement difference value are preconfigured, wherein the first preset settlement difference value is smaller than the second preset settlement difference value;

[0041] When the settlement difference is less than the first preset settlement difference, the deformation base score is determined to be A1;

[0042] When the settlement difference is greater than or equal to the first preset settlement difference, and the settlement difference is less than the second preset settlement difference, the deformation base score is determined to be A2;

[0043] When the settlement difference is greater than or equal to the second preset settlement difference, the deformation base score is determined to be A3;

[0044] Among them, A1<A2<A3.

[0045] Further, when the deformation basic score is determined to be Ai, i=1,2,3, including:

[0046] Acquire real-time expansion data in the deformation data and preset expansion data in the preset deformation data, and determine whether to adjust the deformation basic score according to a relationship between the real-time expansion data and the preset expansion data;

[0047] When the real-time expansion data is lower than or equal to the preset expansion data, the deformation basic score is not adjusted;

[0048] When the real-time expansion data is higher than the preset expansion data, an adjustment coefficient is determined according to the expansion data difference between the real-time expansion data and the preset expansion data, and the deformation basic score Ai adjusted according to the adjustment coefficient is determined as the deformation score of the roadbed to be detected, wherein the value range of the adjustment coefficient is (0.5, 1).

[0049] Furthermore, when determining the adjustment coefficient according to the expansion data difference between the real-time expansion data and the preset expansion data, it includes:

[0050] Acquire real-time displacement data in the deformation data and preset displacement data in the preset deformation data, and determine whether to correct the adjustment coefficient according to the relationship between the real-time displacement data and the preset displacement data;

[0051] When the real-time displacement data is lower than or equal to the preset displacement data, the adjustment coefficient is not corrected;

[0052] When the real-time displacement data is higher than the preset displacement data, a correction coefficient is determined according to the displacement difference between the real-time displacement data and the preset displacement data, and the adjustment coefficient is corrected according to the correction coefficient, wherein the value range of the correction coefficient is (0.5, 1).

[0053] Further, when determining the deformation detection level of the roadbed to be detected to be output according to the magnitude relationship between the deformation score of the roadbed to be detected and each pre-configured preset deformation score, it includes:

[0054] Pre-configuring a first preset deformation score and a second preset deformation score, and determining the deformation grade of the roadbed to be detected according to the relationship between the deformation score and the first preset deformation score and the second preset deformation score:

[0055] When the deformation score is lower than the first preset deformation score, it is determined that the deformation level of the roadbed to be detected is a low level;

[0056] When the deformation score is higher than or equal to the first preset deformation score, and the deformation score is lower than the second preset deformation score, it is determined that the deformation level of the roadbed to be detected is a medium level;

[0057] When the deformation score is higher than or equal to the second preset deformation score, it is determined that the deformation level of the roadbed to be detected is a high level;

[0058] Wherein, the first preset deformation score is lower than the second preset deformation score.

[0059] Compared with the prior art, the beneficial effect of the present invention is that by conducting experiments under preset freezing conditions, the deformation characteristic data of the roadbed in a low temperature environment can be accurately obtained. Secondly, by real-time collection of multi-dimensional data such as temperature, settlement, expansion, shear resistance, ground displacement, etc. of the roadbed, and combining the deformation model constructed with these data, the internal structure change characteristics of the roadbed under a frozen state can be fully reflected. This comprehensive analysis of multi-dimensional data improves the comprehensiveness and accuracy of the test results, and makes up for the shortcomings of the traditional method that is limited to surface phenomena. And through the pre-established roadbed freezing model, by comparing and analyzing the actual test data with the model prediction data, the antifreeze ability and deformation trend of the roadbed under different temperature conditions can be accurately evaluated. Compared with the traditional qualitative analysis method, this method relies on accurate temperature-material relationship modeling to make the deformation prediction results more scientific and credible. This modeling idea is not only applicable to a single material, but can also be extended to different types of roadbed materials and applications under a variety of complex geological conditions, which improves the universality and practicality of the method. In addition, by introducing a deformation scoring and grade evaluation system, the antifreeze performance of the roadbed can be quantified, providing a quantifiable indicator basis for engineering design. This can intuitively reflect the quality status of the roadbed in a low-temperature environment, and the detection level classification provides an important reference for the formulation of roadbed reconstruction, optimization and maintenance strategies. Compared with the existing single evaluation standard, this scoring-based dynamic evaluation method is more flexible and can meet diverse engineering needs, significantly improving the scope of application and actual engineering value of roadbed deformation detection technology.

[0060] On the other hand, the present application also provides a deformation detection system for roadbed antifreeze test, comprising:

[0061] A freezing module is configured to freeze the roadbed based on a preset temperature to obtain the roadbed to be detected;

[0062] A data acquisition module is configured to collect the real-time temperature of the roadbed to be detected, the settlement data of the roadbed material and the roadbed to be detected, the expansion data of the roadbed material, the roadbed anti-shear data and the ground displacement data of the roadbed material, and construct the deformation data of the roadbed to be detected according to the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed anti-shear data and the ground displacement data of the roadbed material;

[0063] A data acquisition module, electrically connected to the data acquisition module, wherein the data acquisition module is configured to acquire preset deformation data of the roadbed to be detected based on the relationship between the real-time temperature, the roadbed material and the preset roadbed freezing model established in advance;

[0064] A central control module is electrically connected to the data acquisition module, and is configured to evaluate the deformation score of the roadbed to be detected based on the relationship between the deformation data and the preset deformation data; the central control module is also configured to determine the deformation detection level to be output for the roadbed to be detected based on the size relationship between the deformation score of the roadbed to be detected and each pre-configured preset deformation score.

[0065] It can be understood that the deformation detection method and system for roadbed antifreeze test in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0067] Figure 1 A flowchart of a deformation detection method for roadbed antifreeze test provided by an embodiment of the present invention;

[0068] Figure 2 A functional block diagram of a deformation detection system for roadbed antifreeze testing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0070] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a deformation detection method for roadbed antifreeze test, including:

[0071] Step S100: After freezing the roadbed based on a preset temperature, obtain the roadbed to be detected.

[0072] Step S200, collecting the real-time temperature of the roadbed to be detected, the roadbed material and the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed shear resistance data and the ground displacement data of the roadbed material, and constructing the deformation data of the roadbed to be detected based on the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed shear resistance data and the ground displacement data of the roadbed material.

[0073] Step S300: based on the relationship between the real-time temperature, the roadbed material and the preset roadbed freezing model, obtain the preset deformation data of the roadbed to be tested.

[0074] Specifically, the pre-established roadbed freezing model includes: obtaining deformation data of each roadbed material at each freezing temperature, wherein the deformation data includes settlement data of the roadbed material, expansion data of the roadbed material, anti-shear data of the roadbed material, and ground displacement data of the roadbed material. A temperature correlation equation is established based on the settlement data, expansion data, anti-shear data of the roadbed material, and ground displacement data of the roadbed material at each freezing temperature. A distance metric between each temperature correlation equation is obtained, and a distance matrix is ​​established based on the distance metric. Each temperature correlation equation is iteratively merged based on the distance matrix, and the freezing type of the roadbed material is determined based on each merged temperature correlation equation. Each temperature correlation equation is clustered based on the freezing type, and the temperature linear relationship between each temperature correlation equation under each freezing type after clustering is obtained. A roadbed freezing model is established based on the temperature linear relationship of each roadbed material.

[0075] It can be seen that the deformation data of settlement, expansion, shear resistance and ground displacement of roadbed materials under different freezing temperatures are obtained to form the correlation between temperature and deformation data. Then, the temperature correlation formula is established using these data, and the distance matrix is ​​constructed through the distance measurement method, and then the temperature correlation formula is iteratively merged to identify different freezing types. Through cluster analysis, each temperature correlation formula is classified according to the freezing type, and the temperature linear relationship under each type is extracted. Finally, based on these temperature linear relationships, a complete roadbed freezing model is established, which can accurately reflect the deformation characteristics of roadbed materials at different temperatures and provide a scientific basis for the evaluation and early warning of the roadbed antifreeze performance.

[0076] It is understandable that by obtaining the deformation data of roadbed materials under different freezing temperatures, including parameters such as settlement, expansion, shear resistance, and ground displacement, we can fully understand the various deformations of roadbed materials during the freezing and thawing process. These data reflect the deformation behavior of roadbed materials under different temperature conditions and are the basis for establishing the relationship between temperature and roadbed deformation. Next, by associating the above deformation data with the corresponding temperature data, a temperature correlation is established. This correlation provides a quantitative description of various deformation data of roadbed materials under different temperature conditions. By calculating the distance metric between each temperature correlation, the system can further analyze the similarity of roadbed material deformation under different temperature conditions. Using the distance matrix, different temperature correlations are quantified and compared, and iteratively merged according to similarity to identify different freezing types. This step helps to understand the deformation laws of different roadbed materials at different temperatures, and then reasonably classify them, providing a basis for subsequent analysis. Through cluster analysis, all temperature correlations are classified according to freezing types to ensure that the temperature relationship under each freezing type has strong internal consistency. The clustered temperature correlation provides a more accurate description of the deformation characteristics under each type of freezing, and can further extract the temperature linear relationship under different types. These temperature linear relationships provide key data support for the establishment of the roadbed freezing model. Finally, according to the temperature linear relationship under each temperature type, a complete roadbed freezing model can be established to accurately predict the deformation behavior of roadbed materials under different temperature conditions. This freezing model can effectively reflect the complex changes in the freeze-thaw cycle and provide a scientific basis for the evaluation of the antifreeze performance and deformation prediction of roadbed engineering.

[0077] Specifically, when calculating the distance metric between each temperature correlation, the choice of distance metric is crucial for subsequent analysis. Common metric methods include Euclidean distance, Manhattan distance, or cosine similarity. Euclidean distance can reflect the straight-line distance between two data points in multidimensional space, while Manhattan distance takes into account the "absolute" difference between data points and is suitable for situations where deformation data has a certain uneven distribution. By selecting a suitable distance metric, the similarity and difference of roadbed material deformation under different temperature conditions can be effectively quantified. After obtaining the distance metric, these metric results are organized into a distance matrix. The distance matrix is ​​a two-dimensional array in which each element represents the distance or similarity between two sets of temperature correlations, and the rows and columns of the matrix correspond to different temperature conditions. This matrix provides a basis for subsequent clustering analysis and pattern recognition, which can help researchers identify similar deformation patterns, thereby grouping and classifying the freeze-thaw behavior of roadbed materials. By analyzing the distance matrix, the regularity of material deformation under different freezing temperatures can be automatically discovered, and support for further freezing type classification and model optimization. In addition, the distance matrix not only provides a data basis for cluster analysis, but also helps reveal the deformation evolution trend of roadbed materials under different temperature conditions. For example, some roadbed materials may show significant expansion behavior at lower temperatures, while at higher temperatures they mainly show settlement or a decrease in shear resistance. By constructing the distance matrix, the impact of these temperature changes on material properties can be effectively captured, providing key deformation characteristic data for the establishment of roadbed freezing models.

[0078] Specifically, when the roadbed freezing model is pre-established, it also includes: obtaining deformation data of any roadbed material at various freezing temperatures, and substituting the roadbed material and the freezing temperature into the roadbed freezing model to obtain preset deformation data. According to the relationship between the deformation data and the preset deformation data, it is determined whether to modify the roadbed freezing model: when the deformation data is consistent with the preset deformation data, it is determined not to modify the roadbed freezing model. When the deformation data is inconsistent with the preset deformation data, the distance matrix is ​​iteratively modified until the deformation data is consistent with the preset deformation data.

[0079] It can be understood that by obtaining the actual deformation data of any roadbed material under different freezing temperatures, and comparing these data with the preset model deformation data. If the actual measured deformation data is consistent with the preset data, it means that the prediction result of the model is accurate. At this time, there is no need to correct the model, and the existing freezing model can continue to be used. However, in practical applications, due to the diversity of roadbed materials and the complexity of the freeze-thaw process, the deformation data may have a certain deviation from the preset data. At this time, it is necessary to start the iterative correction mechanism to ensure that the model can adapt to these changes and provide accurate predictions. When the deformation data is inconsistent with the preset deformation data, the model will be adjusted by iteratively correcting the distance matrix. Specifically, each element in the distance matrix represents the difference between the deformation data under different temperature conditions. By calculating the distance metric and analyzing the deviation, the preset temperature correlation can be adjusted. This correction process is continuously iterated, so that the relationship between temperature and deformation data gradually converges until the model prediction value completely matches the actual deformation data. Through this dynamic correction, the freezing model can be calibrated in time according to the actual detection data, so that it can more accurately reflect the real behavior of roadbed materials under different freezing environments.

[0080] Specifically, when obtaining the ground displacement data of the roadbed materials at each freezing temperature, it includes: obtaining the elastic modulus of the roadbed material, and determining the ground displacement data of the roadbed material according to the settlement data of the roadbed material, the expansion data of the roadbed material, the roadbed shear resistance data and the elastic modulus of the roadbed material and formula I, wherein formula I is as follows: . Wherein, ΔD(T) is the ground displacement data of the roadbed material, ΔS(T) is the settlement data of the roadbed material, ΔV(T) is the expansion data of the roadbed material, τ(T) is the roadbed shear resistance data, and E is the elastic modulus of the roadbed material. Wherein, obtaining the roadbed shear resistance data τ(T) includes: obtaining the change in the shear strength of the roadbed material between various freezing temperatures, and determining the change coefficient of the roadbed material according to the change in the shear strength. Determine the roadbed shear resistance data τ(T) according to the change coefficient, freezing temperature and formula II, where formula II is as follows: Among them, τ0 is the initial shear strength at zero temperature, α is the coefficient of variation of the roadbed material based on the freezing temperature, and ΔT is the temperature difference between the freezing temperature and zero temperature.

[0081] Specifically, when determining the variation coefficient of the roadbed material according to the variation of the shear strength, it includes: obtaining the strength difference between the shear strength of any two adjacent roadbed materials, and determining the variation coefficient according to the relationship between the strength difference and the pre-configured first preset strength difference and the second preset strength difference. When the strength difference is less than the first preset strength difference, the variation coefficient is determined to be L1. When the strength difference is greater than or equal to the first preset strength difference, and the strength difference is less than the second preset strength difference, the variation coefficient is determined to be L2. When the strength difference is greater than or equal to the second preset strength difference, the variation coefficient is determined to be L3. Among them, the first preset strength difference is less than the second preset strength difference. α=L1, L2, L3…Li, and L1<L2<L3<…<Li.

[0082] It can be understood that by collecting the settlement, expansion, shear resistance data and elastic modulus of the roadbed material at different freezing temperatures, these data are substituted into formula I to calculate the ground displacement ΔD(T) of the roadbed material. This calculation process takes into account the interaction between multiple variables, among which the settlement data ΔS(T) and the expansion data ΔV(T) reflect the volume change of the roadbed material during the temperature change, while the shear resistance data τ(T) and the elastic modulus E describe the mechanical response of the material. Through the combination of these data, formula I can comprehensively evaluate the overall deformation of the roadbed material under the action of freeze-thaw, and ensure the accuracy and reliability of the ground displacement data. In addition, in order to further improve the accuracy of the model, this technical solution also specifically considers the change in the shear strength of the roadbed material. During the freeze-thaw process, shear strength is an important factor affecting the stability of the roadbed, which is directly related to the compressive and tensile resistance of the roadbed material under the action of freeze-thaw. By obtaining the change in shear strength at different freezing temperatures, the relationship between the change and temperature is determined, and the roadbed shear resistance data τ(T) is calculated by formula II. In order to more accurately capture the change in shear strength, the difference in shear strength between two adjacent roadbed materials is used to determine whether the difference exceeds the preset threshold, and the variation coefficient α is determined accordingly. In this way, the variation coefficient can be dynamically adjusted to reflect the real change in the shear strength of the roadbed material at different temperatures. In order to ensure the accuracy of the variation coefficient, three preset strength differences are defined to achieve more refined adjustments. The step-by-step variation coefficient setting can effectively capture the impact of different strength differences on the mechanical properties of the material, thereby ensuring a more refined dynamic response to the change in the shear strength of the roadbed material. Ultimately, through this series of calculations and corrections, the deformation and strength changes of the roadbed material at different freezing temperatures can be evaluated in real time and accurately. The comprehensive analysis of multiple physical quantities such as ground displacement, settlement, expansion, and shear strength can more comprehensively reflect the behavioral changes of the roadbed material during the freeze-thaw process and provide a more accurate evaluation of the antifreeze performance of the roadbed.

[0083] Step S400: Evaluate the deformation score of the roadbed to be inspected according to the relationship between the deformation data and the preset deformation data.

[0084] Specifically, when evaluating the deformation score of the roadbed to be detected based on the relationship between the deformation data and the preset deformation data, it includes: obtaining the real-time settlement data in the deformation data and the preset settlement data in the preset deformation data, and determining the deformation basic score of the roadbed to be detected based on the settlement difference between the real-time settlement data and the preset settlement data. Among them, a first preset settlement difference and a second preset settlement difference are pre-configured, wherein the first preset settlement difference is less than the second preset settlement difference. When the settlement difference is less than the first preset settlement difference, the deformation basic score is determined to be A1. When the settlement difference is greater than or equal to the first preset settlement difference, and the settlement difference is less than the second preset settlement difference, the deformation basic score is determined to be A2. When the settlement difference is greater than or equal to the second preset settlement difference, the deformation basic score is determined to be A3. Among them, A1<A2<A3.

[0085] Specifically, when the deformation basic score is determined to be Ai, i=1,2,3, including: obtaining the real-time expansion data in the deformation data and the preset expansion data in the preset deformation data, and determining whether to adjust the deformation basic score according to the relationship between the real-time expansion data and the preset expansion data: when the real-time expansion data is lower than or equal to the preset expansion data, the deformation basic score is not adjusted. When the real-time expansion data is higher than the preset expansion data, the adjustment coefficient is determined according to the expansion data difference between the real-time expansion data and the preset expansion data, and the deformation basic score Ai adjusted according to the adjustment coefficient is determined as the deformation score of the roadbed to be detected, wherein the value range of the adjustment coefficient is (0.5, 1).

[0086] Specifically, when determining the adjustment coefficient according to the expansion data difference between the real-time expansion data and the preset expansion data, it includes: obtaining the real-time displacement data in the deformation data and the preset displacement data in the preset deformation data, and determining whether to correct the adjustment coefficient according to the relationship between the real-time displacement data and the preset displacement data: when the real-time displacement data is lower than or equal to the preset displacement data, the adjustment coefficient is not corrected. When the real-time displacement data is higher than the preset displacement data, the correction coefficient is determined according to the displacement difference between the real-time displacement data and the preset displacement data, and the adjustment coefficient is corrected according to the correction coefficient, wherein the value range of the correction coefficient is (0.5, 1).

[0087] It is understandable that the deformation of the roadbed can be preliminarily evaluated by the difference between the real-time settlement data and the preset settlement data. This process divides the roadbed deformation into different levels by setting different settlement difference thresholds. Specifically, when the settlement difference is small, it is judged as a relatively slight deformation, indicating that the stability of the roadbed is good; when the settlement difference is large but still within a certain range, it means that the roadbed deformation has increased; when the settlement difference exceeds the preset second threshold, it means that the deformation of the roadbed may reach a more serious level and requires special attention. However, relying solely on settlement data is not enough to fully evaluate the deformation of the roadbed, because the roadbed will not only settle during the freezing process, but also may undergo other complex deformation behaviors such as expansion. Therefore, after evaluating the preliminary deformation score, it is necessary to further consider the difference between the real-time expansion data and the preset expansion data. If the real-time expansion data is higher than the preset expansion data, it means that the expansion degree of the roadbed material under the action of freezing exceeds expectations, which may aggravate the deformation of the roadbed. In this case, the deformation basic score is adjusted accordingly by calculating the expansion difference and determining the adjustment coefficient. The range of the adjustment coefficient is set to 0.5 to 1, which means that under different expansion degrees, the deformation score can be dynamically adjusted according to the actual changes to make it closer to the actual performance of the roadbed material. A further correction mechanism refines the correction of the adjustment coefficient by the difference between the real-time displacement data and the preset displacement data. When the real-time displacement data is higher than the preset value, it means that the deformation of the roadbed in terms of horizontal displacement exceeds the expected range, which may be caused by non-uniform deformation caused by temperature changes, humidity changes or other factors. At this time, the correction coefficient will be calculated based on the displacement difference, and the adjustment coefficient generated by the expansion data difference will be adjusted by the correction coefficient to further optimize the deformation score. This can more accurately reflect the overall deformation behavior of the roadbed in different directions and different deformation modes. Through this multi-level scoring and dynamic adjustment mechanism, the present application can flexibly respond to different types of roadbed deformations, comprehensively consider multiple factors such as settlement, expansion and displacement, and provide a more accurate assessment of roadbed deformation.

[0088] Step S500: determining a deformation detection level of the roadbed to be detected to be output according to a magnitude relationship between the deformation score of the roadbed to be detected and each pre-configured preset deformation score.

[0089] Specifically, when determining the deformation level of the roadbed to be detected based on the relationship between the deformation score and the preset deformation score, it includes: pre-configuring a first preset deformation score and a second preset deformation score, and determining the deformation level of the roadbed to be detected based on the relationship between the deformation score and the first preset deformation score and the second preset deformation score: when the deformation score is lower than the first preset deformation score, the deformation level of the roadbed to be detected is determined to be a low level. When the deformation score is higher than or equal to the first preset deformation score, and the deformation score is lower than the second preset deformation score, the deformation level of the roadbed to be detected is determined to be a medium level. When the deformation score is higher than or equal to the second preset deformation score, the deformation level of the roadbed to be detected is determined to be a high level. Among them, the first preset deformation score is lower than the second preset deformation score.

[0090] In the above embodiment, by conducting experiments under preset freezing conditions, the deformation characteristic data of the roadbed in a low temperature environment can be accurately obtained. Secondly, by real-time collection of multi-dimensional data such as temperature, settlement, expansion, shear resistance, ground displacement, etc. of the roadbed, and combining the deformation model constructed with these data, the internal structure change characteristics of the roadbed under a frozen state can be fully reflected. This comprehensive analysis of multi-dimensional data improves the comprehensiveness and accuracy of the test results, and makes up for the shortcomings of the traditional method that is limited to surface phenomena. And through the pre-established roadbed freezing model, by comparing and analyzing the actual test data with the model prediction data, the antifreeze ability and deformation trend of the roadbed under different temperature conditions can be accurately evaluated. Compared with the traditional qualitative analysis method, this method relies on accurate temperature-material relationship modeling to make the deformation prediction results more scientific and credible. This modeling idea is not only applicable to a single material, but can also be extended to different types of roadbed materials and applications under a variety of complex geological conditions, which improves the universality and practicality of the method. In addition, by introducing a deformation scoring and grade evaluation system, the antifreeze performance of the roadbed can be quantified, providing a quantifiable indicator basis for engineering design. This can intuitively reflect the quality status of the roadbed in a low-temperature environment, and the detection level classification provides an important reference for the formulation of roadbed reconstruction, optimization and maintenance strategies. Compared with the existing single evaluation standard, this scoring-based dynamic evaluation method is more flexible and can meet diverse engineering needs, significantly improving the scope of application and actual engineering value of roadbed deformation detection technology.

[0091] In another preferred embodiment based on the above embodiment, Figure 2 The embodiment shown provides a deformation detection system for roadbed antifreeze test, including: a freezing module, a data collection module, a data acquisition module and a central control module.

[0092] Specifically, the freezing module is configured to obtain the roadbed to be detected after freezing the roadbed based on a preset temperature; the data acquisition module is configured to collect the real-time temperature of the roadbed to be detected, the roadbed material and the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed shear resistance data and the ground displacement data of the roadbed material, and construct the deformation data of the roadbed to be detected according to the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed shear resistance data and the ground displacement data of the roadbed material; the data acquisition module is electrically connected to the data acquisition module, and the data acquisition module is configured to obtain the preset deformation data of the roadbed to be detected based on the real-time temperature, the roadbed material and the relationship between the preset roadbed freezing model established in advance; the central control module is electrically connected to the data acquisition module, and the central control module is configured to evaluate the deformation score of the roadbed to be detected according to the relationship between the deformation data and the preset deformation data; the central control module is also configured to determine the deformation detection level to be output of the roadbed to be detected according to the size relationship between the deformation score of the roadbed to be detected and each pre-configured preset deformation score.

[0093] It can be understood that the deformation detection method and system for roadbed antifreeze test in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A deformation detection method for roadbed antifreeze test, characterized in that: include: After freezing the roadbed based on a preset temperature, the roadbed to be inspected is obtained; Collecting the real-time temperature of the roadbed to be detected, the roadbed material and the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed anti-shear data and the ground displacement data of the roadbed material, and constructing the deformation data of the roadbed to be detected according to the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed anti-shear data and the ground displacement data of the roadbed material; Based on the relationship between the real-time temperature, the roadbed material and the preset roadbed freezing model established in advance, obtaining preset deformation data of the roadbed to be detected; According to the relationship between the deformation data and the preset deformation data, evaluating the deformation score of the roadbed to be detected; Determining a deformation detection level to be output for the roadbed to be detected according to a magnitude relationship between the deformation score of the roadbed to be detected and each pre-configured preset deformation score; Pre-built subgrade freezing models include: Obtaining deformation data of each of the roadbed materials at each freezing temperature, wherein the deformation data includes settlement data of the roadbed material, expansion data of the roadbed material, anti-shear data of the roadbed and ground displacement data of the roadbed material; Establishing a temperature correlation equation based on the settlement data of the roadbed material, the expansion data of the roadbed material, the anti-shear data of the roadbed and the ground displacement data of the roadbed material at each of the freezing temperatures; Obtaining a distance metric between each of the temperature correlation equations, and establishing a distance matrix according to the distance metric; Iteratively merging the temperature correlation equations according to the distance matrix, and determining the freezing type of the roadbed material according to the merged temperature correlation equations; Clustering the temperature correlation equations according to the freezing type, and obtaining the temperature linear relationship between the temperature correlation equations under the freezing type after clustering; The roadbed freezing model is established according to the linear relationship of the temperature of each roadbed material.

2. A deformation detection method for roadbed antifreeze test according to claim 1, characterized in that: Pre-built subgrade icing models also include: Obtaining deformation data of any of the roadbed materials at various freezing temperatures, and substituting the roadbed material and the freezing temperature into the roadbed freezing model to obtain the preset deformation data; Determining whether to modify the roadbed freezing model according to the relationship between the deformation data and the preset deformation data; When the deformation data is consistent with the preset deformation data, it is determined that the roadbed freezing model is not to be corrected; When the deformation data is inconsistent with the preset deformation data, the distance matrix is ​​iteratively corrected until the deformation data is consistent with the preset deformation data.

3. A deformation detection method for roadbed antifreeze test according to claim 1, characterized in that: When obtaining the ground displacement data of the roadbed materials at each freezing temperature, it includes: The elastic modulus of the roadbed material is obtained, and the ground displacement data of the roadbed material is determined according to the settlement data of the roadbed material, the expansion data of the roadbed material, the roadbed shear resistance data and the elastic modulus of the roadbed material and formula I, wherein formula I is as follows: ； Wherein, ΔD(T) is the ground displacement data of the roadbed material, ΔS(T) is the settlement data of the roadbed material, ΔV(T) is the expansion data of the roadbed material, τ(T) is the anti-shear data of the roadbed, and E is the elastic modulus of the roadbed material. The step of obtaining the anti-shear data τ(T) of the roadbed includes: Obtaining a change in the shear strength of the roadbed material between various freezing temperatures, and determining a change coefficient of the roadbed material according to the change in the shear strength; According to the variation coefficient, freezing temperature and formula II, the roadbed anti-shear data τ(T) is determined, wherein formula II is as follows: ； Wherein, τ0 is the initial shear strength at zero temperature, α is the coefficient of variation of the roadbed material based on the freezing temperature, and ΔT is the temperature difference between the freezing temperature and the zero temperature.

4. A deformation detection method for roadbed antifreeze test as claimed in claim 3, characterized in that: Determining the variation coefficient of the roadbed material according to the variation of the shear strength includes: Obtaining a strength difference between the shear strengths of any two adjacent roadbed materials, and determining the variation coefficient according to a relationship between the strength difference and a pre-configured first preset strength difference and a second preset strength difference; When the intensity difference is less than the first preset intensity difference, the variation coefficient is determined to be L1; When the intensity difference is greater than or equal to the first preset intensity difference, and the intensity difference is less than the second preset intensity difference, determining the variation coefficient to be L2; When the intensity difference is greater than or equal to the second preset intensity difference, determining the variation coefficient to be L3; Among them, the first preset intensity difference is smaller than the second preset intensity difference; α=L1, L2, L3, …, Li, and L1<L2<L3<…<Li.

5. A deformation detection method for roadbed antifreeze test according to claim 1, characterized in that: When evaluating the deformation score of the roadbed to be detected according to the relationship between the deformation data and the preset deformation data, the method includes: Acquire real-time settlement data in the deformation data and preset settlement data in the preset deformation data, and determine the deformation basic score of the roadbed to be detected according to the settlement difference between the real-time settlement data and the preset settlement data; Wherein, a first preset settlement difference value and a second preset settlement difference value are preconfigured, wherein the first preset settlement difference value is smaller than the second preset settlement difference value; When the settlement difference is less than the first preset settlement difference, the deformation base score is determined to be A1; When the settlement difference is greater than or equal to the first preset settlement difference, and the settlement difference is less than the second preset settlement difference, the deformation base score is determined to be A2; When the settlement difference is greater than or equal to the second preset settlement difference, the deformation base score is determined to be A3; Among them, A1<A2<A3.

6. A deformation detection method for roadbed antifreeze test according to claim 5, characterized in that: When the deformation base score is determined to be Ai, i=1,2,3, including: Acquire real-time expansion data in the deformation data and preset expansion data in the preset deformation data, and determine whether to adjust the deformation basic score according to a relationship between the real-time expansion data and the preset expansion data; When the real-time expansion data is lower than or equal to the preset expansion data, the deformation basic score is not adjusted; When the real-time expansion data is higher than the preset expansion data, an adjustment coefficient is determined according to the expansion data difference between the real-time expansion data and the preset expansion data, and the deformation basic score Ai adjusted according to the adjustment coefficient is determined as the deformation score of the roadbed to be detected, wherein the value range of the adjustment coefficient is (0.5, 1).

7. A deformation detection method for roadbed antifreeze test according to claim 6, characterized in that: When determining the adjustment coefficient according to the expansion data difference between the real-time expansion data and the preset expansion data, it includes: Acquire real-time displacement data in the deformation data and preset displacement data in the preset deformation data, and determine whether to correct the adjustment coefficient according to the relationship between the real-time displacement data and the preset displacement data; When the real-time displacement data is lower than or equal to the preset displacement data, the adjustment coefficient is not corrected; When the real-time displacement data is higher than the preset displacement data, a correction coefficient is determined according to the displacement difference between the real-time displacement data and the preset displacement data, and the adjustment coefficient is corrected according to the correction coefficient, wherein the value range of the correction coefficient is (0.5, 1).

8. A deformation detection method for roadbed antifreeze test according to claim 1, characterized in that: Determining the deformation detection level of the roadbed to be detected to be output according to the magnitude relationship between the deformation score of the roadbed to be detected and each pre-configured preset deformation score includes: Pre-configuring a first preset deformation score and a second preset deformation score, and determining the deformation grade of the roadbed to be detected according to the relationship between the deformation score and the first preset deformation score and the second preset deformation score: When the deformation score is lower than the first preset deformation score, it is determined that the deformation level of the roadbed to be detected is a low level; When the deformation score is higher than or equal to the first preset deformation score, and the deformation score is lower than the second preset deformation score, it is determined that the deformation level of the roadbed to be detected is a medium level; When the deformation score is higher than or equal to the second preset deformation score, it is determined that the deformation level of the roadbed to be detected is a high level; Wherein, the first preset deformation score is lower than the second preset deformation score.

9. A deformation detection system for roadbed antifreeze test, adopting a deformation detection method for roadbed antifreeze test according to any one of claims 1 to 8, characterized in that: include: A freezing module is configured to freeze the roadbed based on a preset temperature to obtain the roadbed to be detected; A data acquisition module is configured to collect the real-time temperature of the roadbed to be detected, the settlement data of the roadbed material and the roadbed to be detected, the expansion data of the roadbed material, the roadbed anti-shear data and the ground displacement data of the roadbed material, and construct the deformation data of the roadbed to be detected according to the settlement data of the roadbed to be detected, the expansion data of the roadbed material, the roadbed anti-shear data and the ground displacement data of the roadbed material; A data acquisition module, electrically connected to the data acquisition module, wherein the data acquisition module is configured to acquire preset deformation data of the roadbed to be detected based on the relationship between the real-time temperature, the roadbed material and the preset roadbed freezing model established in advance; A central control module is electrically connected to the data acquisition module, and is configured to evaluate the deformation score of the roadbed to be detected based on the relationship between the deformation data and the preset deformation data; the central control module is also configured to determine the deformation detection level to be output for the roadbed to be detected based on the size relationship between the deformation score of the roadbed to be detected and each pre-configured preset deformation score.

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