A roadbed improvement optimization method based on rubber particles and cement content

Abstract

The present invention discloses a roadbed improvement optimization method for the amount of rubber particles and cement, which belongs to the technical field of roadbed improvement and solves the problem that the existing technology cannot optimize the amount of rubber particles and cement in the roadbed; the present invention uses digital speckle testing to accurately measure the micro-deformation of the sample surface, which is convenient for determining the local deformation and failure process of the sample under a uniaxial compression test, provides more accurate sample compressive strength data, reduces the error of the compressive strength prediction formula, and can predict the optimal amount of rubber particles and cement through the compressive strength prediction formula, and further through the particle flow PFC 2D The model can simulate the contact and interaction between particles and effectively predict the performance of the constructed roadbed during actual use, thereby guiding construction design and optimization, improving construction efficiency and roadbed durability, and providing a systematic and reliable method for the improvement and optimization of the constructed roadbed.

Description

Technical Field

[0001] The invention relates to the technical field of roadbed improvement, and in particular to a roadbed improvement optimization method for rubber particles and cement content. Background Art

[0002] Due to ageing and damage after use, rubber tires are discarded in large quantities. This large-scale waste tire accumulation leads to land occupation and environmental pollution. Therefore, how to deal with this ever-increasing "black pollution" has become a global challenge. To facilitate the recycling of waste tires, existing methods have been developed to reprocess them into rubber pellets to replace sand in concrete pavements. Using rubber pellets made from waste tires in road construction can improve economic efficiency and has significant environmental benefits.

[0003] Rubber particles have excellent thermal insulation and resilience properties, making them suitable for use as roadbed fillers, effectively improving the quality and service life of roadbed pavement. In his paper "Research on the Micromechanical Properties of Rubber-Sand Composite Soils," He Zhimin noted that incorporating rubber particles into aeolian sand can improve the shear strength of the resulting composite. Furthermore, extensive and in-depth research on the static and dynamic properties of rubber particle-modified soils has been conducted by domestic and international scholars, confirming the feasibility of using rubber particle-modified soils as roadbed fillers.

[0004] The existing technology lacks the influence of different rubber particles and cement content on the uniaxial compressive strength of the roadbed, and cannot predict the strength of the improved roadbed, thereby failing to optimize the rubber particles and cement content in the roadbed. Summary of the Invention

[0005] In view of the above problems in the prior art, the present invention provides a roadbed improvement optimization method for rubber particles and cement content, which solves the problem that the prior art cannot optimize the rubber particles and cement content in the roadbed.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A method for optimizing the amount of rubber particles and cement added to improve a roadbed is provided, comprising the following steps:

[0008] S1. Prepare samples with different rubber particle and cement content, and randomly apply multiple dots on the surface of each sample as an artificial speckle field;

[0009] S2. Perform a uniaxial compression test on each sample, and use multiple cameras to record the three-dimensional deformation image of the artificial speckle field of each sample that continuously changes during the uniaxial compression test;

[0010] S3. Process the three-dimensional deformation image using a digital speckle test calculation method to obtain a uniaxial compressive strength curve for each specimen;

[0011] S4. Obtaining a compressive strength prediction formula for the improved roadbed containing rubber particles by fitting the uniaxial compressive strength curves of multiple specimens;

[0012] S5. Establish and calibrate the PFC of the roadbed based on the compressive strength prediction formula 2D Model;

[0013] S6, using granular flow PFC 2D The model simulates the construction roadbed and determines the rubber particle and cement content according to the strength requirements of the construction roadbed.

[0014] In this scheme, the digital speckle test can accurately measure the micro deformation of the sample surface, which is convenient for determining the local deformation and failure process of the sample under the uniaxial compression test, providing more accurate sample compressive strength data, reducing the error of the compressive strength prediction formula. The compressive strength prediction formula can already predict the optimal dosage of rubber particles and cement. This scheme further uses particle flow PFC to 2D The model can simulate the contact and interaction between particles and effectively predict the performance of the constructed roadbed during actual use, thereby guiding construction design and optimization, improving construction efficiency and roadbed durability, and providing a systematic and reliable method for the improvement and optimization of the constructed roadbed.

[0015] Furthermore, the preparation method of each sample is as follows:

[0016] S11, cutting the waste tires, removing the metal, crushing them, and sieving them to obtain rubber particles with a diameter of 0.09 mm to 0.85 mm;

[0017] S12, mixing aeolian soil particles, cement, and rubber particles according to a set ratio to obtain a mixture, and configuring the mixture into a cylindrical structure sample according to an optimal moisture content;

[0018] S13. Place the sample in a constant temperature and humidity chamber for 7 days, and spray a thin layer of white spray paint on the surface of the sample as the base color. After the sample is naturally air-dried, randomly dot the surface with uniform size and shape as an artificial speckle field.

[0019] In this approach, waste tires are physically cut and sieved to obtain standardized rubber particles. The ratio of cement to rubber particles is precisely controlled during specimen preparation to ensure sample consistency and reliability. Constant temperature and humidity curing and the use of an artificial speckle field enhance the accuracy of surface deformation measurements.

[0020] Furthermore, the method for determining the optimal moisture content of each sample is:

[0021] S121. The mixtures in each sample are divided into groups and prepared according to different moisture contents. A compaction test is performed on each group of mixtures, and the wet density after compaction is recorded.

[0022] S122. Calculate the dry density of each mixture in step S121 based on the wet density:

[0023]

[0024] in, is the wet density, is the dry density;

[0025] S123. Numerical fitting is performed using multiple sets of moisture content and dry density to obtain the fitting formula of moisture content and dry density:

[0026]

[0027] in, is the dry density, is the moisture content, 、 and is a constant, is the correction factor, ;

[0028] S124, according to the fitting curve of moisture content and dry density, The maximum value corresponds to .

[0029] In this scheme, the optimal moisture content of the sample can be accurately determined through compaction tests and quadratic polynomial fitting, thereby ensuring that the performance of each sample in the uniaxial compression test is representative and improving the reliability and accuracy of the test results.

[0030] Furthermore, the testing machine loading rate during the uniaxial compression test was set at 0.5 mm / min, and each camera captured images at a rate of 0.7 frames / s. These settings ensured standardized testing procedures and consistent image recording, providing high-quality image data for subsequent digital speckle pattern calculations.

[0031] Furthermore, step S3 further includes:

[0032] S31. 3D initial image obtained without uniaxial compression test on the sample and the three-dimensional deformation image obtained by uniaxial compression test Grayscale normalization is performed to adjust the grayscale value ranges of different images to the same standard range, thereby eliminating image differences caused by factors such as brightness and contrast.

[0033] S32, pairing the three-dimensional initial image with the multiple dots on the three-dimensional deformed image, and setting the initial displacement of each dot by fast Fourier transform ( ). Fast Fourier transform makes the initial matching more accurate and faster, reducing the workload of subsequent iterative calculations.

[0034] S33. Calculate the error function E of the center point displacement of each dot spot:

[0035]

[0036]

[0037]

[0038]

[0039] in, is the scale factor; is a variable; For the The weight parameter of each dot; is the initial three-dimensional image middle The gray value at For the dots in the 3D initial image The coordinates in ; 3D deformed image middle The gray value at For the Dots in the 3D deformed image The coordinates in ; and They are the three-dimensional initial images and 3D deformed images Gray mean value; and They are the three-dimensional initial images and 3D deformed images Grayscale standard deviation; For the The distance from the dot to the center of the sample.

[0040] S34, Judgment Is it less than the set value? If so, go to step S35, otherwise go to step S37.

[0041] S35, according to the displacement of each dot spot ( )、 and Gray value, and the three-dimensional initial image and 3D deformed images Grayscale standard deviation and , calculate the Jacobian matrix , residual vector , calculate the gradient and the Hessian matrix The Jacobian matrix represents the partial derivative of the image grayscale value with respect to the displacement, and the residual vector represents the difference between the actual grayscale value and the estimated value, which is optimized by the gradient and Hessian matrix.

[0042]

[0043]

[0044]

[0045]

[0046] S36, update the displacement ( ) , and returns to step S33; wherein, is the parameter update amount, The iterative update of the displacement makes the matching process gradually approach the optimal solution, thereby improving the accuracy of the displacement.

[0047] S37. Based on the final displacements of the multiple spots, the strain field of the sample is calculated and a uniaxial compressive strength curve is obtained.

[0048] Furthermore, the compressive strength prediction formula is expressed as:

[0049]

[0050] in, is the 7-day uniaxial compressive strength, in kPa; CS is the percentage of cement content; PRC is the percentage of rubber particles.

[0051] Furthermore, PFC 2D The shape of the model is the same as that of the specimen, and the particle flow PFC 2D The contact mode in the model is parallel bonded contact.

[0052] Furthermore, PFC 2D The model further includes the following methods for simulating the construction roadbed:

[0053] S61. Select a typical roadbed section in the construction roadbed and record the actual stress distribution data of the roadbed section under different load conditions;

[0054] S62, using granular flow PFC 2D The model simulates the roadbed section under different load conditions to obtain simulated stress distribution data;

[0055] S63, calculate the error between the actual stress distribution data and the simulated stress distribution data, if the error is less than 5%, proceed to step S64, otherwise adjust the particle flow PFC 2D The parameters of the model and return to step S62;

[0056] S64, determine the particle flow PFC 2D The model is accurate.

[0057] Furthermore, the sample also includes aeolian soil particles mixed with a percentage of Nano-TiO2 particles are obtained, and the response surface formula of nano-TiO2 particles and rubber particles is obtained according to the response surface method:

[0058]

[0059] in, is the compressive strength, in MPa, is the percentage of nano-TiO2 particles, PRC is the percentage of rubber particles.

[0060] In this scheme, in order to further improve the performance of the construction roadbed, nano-TiO2 particles are added to the roadbed to make it have higher bending strength. Nano-TiO2 particles make the roadbed microstructure better, with fewer holes, more compact microcracks, smoother, and improved durability. The response surface equation of nano-TiO2 and rubber particles is used to describe the effect of nano-TiO2 on the compressive strength of the rubber particle improved roadbed, which is used to optimize the particle flow PFC. 2D The model provides a theoretical basis for further improving the mechanical properties and durability of the constructed roadbed.

[0061] The present invention discloses a roadbed improvement optimization method for rubber particles and cement content, which has the following beneficial effects:

[0062] The present invention uses digital speckle testing to accurately measure the micro-deformation of the sample surface, which is convenient for determining the local deformation and failure process of the sample under uniaxial compression test, providing more accurate sample compressive strength data, reducing the error of the compressive strength prediction formula, and predicting the optimal dosage of rubber particles and cement through the compressive strength prediction formula, and further through the particle flow PFC 2DThe model can simulate the contact and interaction between particles and effectively predict the performance of the constructed roadbed during actual use, thereby guiding construction design and optimization, improving construction efficiency and roadbed durability, and providing a systematic and reliable method for the improvement and optimization of the constructed roadbed. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flow chart of the roadbed improvement optimization method for rubber particles and cement content;

[0064] Figure 2 is the particle gradation curve of aeolian soil particles and rubber particles;

[0065] Figure 3 This is the uniaxial compression strength curve of different rubber particle contents when the cement content is 3%;

[0066] Figure 4 This is the uniaxial compression strength curve of different rubber particle contents when the cement content is 5%;

[0067] Figure 5 This is the uniaxial compression strength curve of different rubber particle contents when the cement content is 7%;

[0068] Figure 6 PFC 2D Stress-strain curve of the model;

[0069] Figure 7 The crack extension forms on the specimen surface in the compaction stage, elastic deformation stage, peak value and softening stage;

[0070] Figure 8 The micro crack growth process diagram of the specimen;

[0071] Figure 9 is the peak uniaxial compressive strength curve of the sample under different dosage conditions;

[0072] Figure 10 for k ( PRC ) and the fitting curve of rubber particle content;

[0073] Figure 11 for m ( PRC ) and the fitting curve of rubber particle content;

[0074] Figure 12 The figure is a comparison between the test results and the calculation results;

[0075] Figure 13 is the error analysis diagram;

[0076] Figure 14 This is the vertical displacement cloud map of the unimproved roadbed;

[0077] Figure 15 This is the vertical displacement cloud map of the improved roadbed with the optimal ratio; DETAILED DESCRIPTION

[0078] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0079] Example 1

[0080] refer to Figure 1 This embodiment provides a method for optimizing roadbed improvement based on rubber particles and cement content, comprising the following steps:

[0081] S1. Prepare samples with different rubber particle and cement content, and randomly apply multiple dots on the surface of each sample as an artificial speckle field.

[0082] Specifically, the preparation method of each sample is as follows:

[0083] S11, cutting the waste tires, removing the metal and crushing them, and sieving them to obtain rubber particles with a diameter of 0.09mm~0.85mm. The density of the rubber particles in this embodiment is 350~500kg / m 3 By physically cutting and sieving waste tires, standardized rubber particles are obtained. During the sample preparation process, the ratio of cement and rubber particles is precisely controlled to ensure the consistency and reliability of the samples.

[0084] S12, aeolian soil particles, cement and rubber particles are mixed according to the set ratio to obtain a mixture, and the mixture is configured into a cylindrical structure sample according to the optimal moisture content. Cement is 425 # Ordinary Portland cement will undergo strong hydrolysis and hydration reactions when it comes into contact with water, and will produce hydration products mainly composed of Ca(OH)2 and CaSiO3. The hydration products are used to bond the aeolian soil particles and cement together to form a more complete skeleton and thus improve the strength. The particle size of the aeolian soil particles is between 0.075mm and 1mm. The particle size distribution curve of the aeolian soil particles and rubber particles in this embodiment is referenced. Figure 2 .

[0085] S13. Place the sample in a constant temperature and humidity chamber for 7 days, and spray the surface of the sample with a thin layer of white spray paint as the base color. After the sample is naturally dried, randomly point uniformly sized and shaped dots on the surface as the artificial speckle field. φ 39.1mm×H h 80 mm cylindrical structure. Constant temperature and humidity curing and the use of an artificial speckle field improve the accuracy of sample surface deformation measurements. The amount of rubber particles in the various samples in this example is shown in Table 1.

[0086] Table 1

[0087]

[0088] S2. Uniaxial compression tests were performed on each specimen, and multiple cameras were used to record the continuously changing three-dimensional deformation images of the artificial speckle field of each specimen during the uniaxial compression test. The loading rate of the testing machine during the uniaxial compression test was 0.5 mm / min, and the capture rate of each camera was 0.7 frames / s. These standardized loading and camera capture rates ensured a standardized test process and consistent image recording, providing high-quality image data for subsequent digital speckle pattern calculations.

[0089] S3. The three-dimensional deformation image is processed using the digital speckle test calculation method to obtain the uniaxial compressive strength curve of each sample.

[0090] Reference of uniaxial compressive strength curves of multiple specimens Figures 3 to 5 ,in ε is strain, expressed in percentage. At the initial stage of loading, the sample is rapidly compacted due to the sudden external force applied to the sample. The stress first increases linearly and slowly with the increase of strain, which is the compaction stage. Afterwards, the stress growth rate increases, and the stress-strain curve shows a linear increase, which is the elastic deformation stage. With continued loading, the stress-strain curve of the sample increases nonlinearly and is in the elastic-plastic stage. After reaching the peak point, the stress of the sample decreases rapidly, and the post-peak samples all show certain residual strength characteristics. The uniaxial compression process of rubber particle-cement improved soil includes four stages: compaction stage, elastic deformation, plastic deformation and softening stage. From Figures 3 to 5 It can also be seen that with the increase of rubber particles, its uniaxial compressive strength shows a trend of first increasing and then decreasing; with the increase of cement content, its uniaxial compressive strength increases to varying degrees. The evolution of maximum shear strain can intuitively reflect the crack expansion and deformation characteristics of the sample surface during the entire loading process. The evolution of maximum shear strain at the compaction stage, elastic deformation stage, peak point and softening stage, as well as the crack expansion form on the sample surface, can be referred to Figure 6 .

[0091] S4. The compressive strength prediction formula of the improved roadbed containing rubber particles is obtained by fitting the uniaxial compressive strength curves of multiple samples.

[0092] S5. Establish and calibrate the PFC of the roadbed based on the compressive strength prediction formula 2D Model. PFC 2D ParticleFlow Code in 2 Dimensions (PFC) is a numerical simulation tool based on the Discrete Element Method (DEM) for simulating the mechanical behavior of granular materials. This example calibrates the microscopic parameters of the model. By adjusting the microscopic parameters of the PFC model, the numerical simulation is basically consistent with the macroscopic properties of the specimen, such as the stress-strain curve and failure mode. This is considered the end of the calibration. 2D Model stress-strain curve reference Figure 7 The particle flow PFC model can well demonstrate the development process of the sample microcracks and the contact force between particles. The microcrack propagation process of the sample is shown in Figure 2. Figure 8 .

[0093] S6, using granular flow PFC 2D The model simulates the construction roadbed and determines the rubber particle and cement content according to the strength requirements of the construction roadbed. 2D The shape of the model is the same as that of the specimen, and the particle flow PFC 2D The contact mode in the model is parallel bonded contact.

[0094] Specifically, particle flow PFC 2D The model further includes the following methods for simulating the construction roadbed:

[0095] S61. Select a typical roadbed section in the construction roadbed and record the actual stress distribution data of the roadbed section under different load conditions;

[0096] S62, using granular flow PFC 2D The model simulates the roadbed section under different load conditions to obtain simulated stress distribution data;

[0097] S63, calculate the error between the actual stress distribution data and the simulated stress distribution data, if the error is less than 5%, proceed to step S64, otherwise adjust the particle flow PFC 2D The parameters of the model and return to step S62;

[0098] S64, determine the particle flow PFC 2D The model is accurate.

[0099] Specifically, the method for determining the optimal moisture content of each sample is:

[0100] S121. The mixtures in each sample are divided into groups and prepared according to different moisture contents. A compaction test is performed on each group of mixtures, and the wet density after compaction is recorded.

[0101] S122. Calculate the dry density of each mixture in step S121 based on the wet density:

[0102]

[0103] in, is the wet density, is the dry density;

[0104] S123. Numerical fitting is performed using multiple sets of moisture content and dry density to obtain the fitting formula of moisture content and dry density:

[0105]

[0106] in, is the dry density, is the moisture content, 、 and is a constant, is the correction factor, ;

[0107] S124, according to the fitting curve of moisture content and dry density, The maximum value corresponds to .

[0108] Through compaction tests and quadratic polynomial fitting, the optimal moisture content of the sample can be accurately determined, thereby ensuring that the performance of each sample in the uniaxial compression test is representative and improving the reliability and accuracy of the test results.

[0109] Specifically, step S3 further includes:

[0110] S31. 3D initial image obtained without uniaxial compression test on the sample and the three-dimensional deformation image obtained by uniaxial compression test Grayscale normalization is performed to adjust the grayscale value ranges of different images to the same standard range, thereby eliminating image differences caused by factors such as brightness and contrast.

[0111] S32, pairing the three-dimensional initial image with the multiple dots on the three-dimensional deformed image, and setting the initial displacement of each dot by fast Fourier transform ( ). Fast Fourier transform makes the initial matching more accurate and faster, reducing the workload of subsequent iterative calculations.

[0112] S33. Calculate the error function E of the center point displacement of each dot spot:

[0113]

[0114]

[0115]

[0116]

[0117] in, is the scale factor; is a variable; For the The weight parameter of each dot; is the initial three-dimensional image middle The gray value at For the dots in the 3D initial image The coordinates in ; 3D deformed image middle The gray value at For the Dots in the 3D deformed image The coordinates in ; and They are the three-dimensional initial images and 3D deformed images Gray mean value; and They are the three-dimensional initial images and 3D deformed images Grayscale standard deviation; Indicates the The distance from each dot to the center of the sample can be further accurately calculated based on the distribution of each dot.

[0118] S34, Judgment Is it less than the set value? If so, the process proceeds to step S35, otherwise, the process proceeds to step S37. The set value in this embodiment is 0.001.

[0119] S35, according to the displacement of each dot spot ( )、 and Gray value, and the three-dimensional initial image and 3D deformed images Grayscale standard deviation and , calculate the Jacobian matrix , residual vector , calculate the gradient and the Hessian matrix The Jacobian matrix represents the partial derivative of the image grayscale value with respect to the displacement, and the residual vector represents the difference between the actual grayscale value and the estimated value, which is optimized by the gradient and Hessian matrix.

[0120]

[0121]

[0122]

[0123]

[0124] S36, update the displacement ( ) , and returns to step S33; wherein, is the parameter update amount, The iterative update of the displacement makes the matching process gradually approach the optimal solution, thereby improving the accuracy of the displacement.

[0125] S37. According to the final displacement of multiple spots, the strain field of the sample is calculated and the uniaxial compressive strength curve is obtained. Figures 3 to 5 .

[0126] In this embodiment, the compressive strength prediction formula of the improved roadbed of rubber particles is:

[0127]

[0128] in, is the 7-day uniaxial compressive strength in kPa. The compressive strength prediction formula is a numerical fit and the unit does not need to be considered. CS is the percentage of cement content; PRC is the percentage of rubber particles.

[0129] The specific calculation process is as follows: the uniaxial compressive strength fitting curve under different dosage conditions is shown in Figure 9. The power function is used to fit the uniaxial compressive strength of rubber particle-cement improved soil. ,in, k (PRC) 、 m(PRC) is a parameter related to the amount of rubber particles. Using the method of successive elimination, the parameters in the fitting formula are k (PRC) 、 m(PRC) Perform successive elimination to obtain the model parameters k(PRC) 、 m(PRC) The relationship with the change of rubber particle content, such as Figure 10 and Figure 11 As shown. Thus the model parameters k(PRC) 、 m(PRC) The relationship between the content of rubber particles is:

[0130]

[0131]

[0132] in, The coefficient of determination of the fitting relationship formula is obtained by substituting the relationship formula into the power function to obtain the compressive strength prediction formula of the improved roadbed of rubber particles in this embodiment. Figure 12 and Figure 13 The uniaxial compressive strength of the rubber particle-cement improved roadbed gradually increased with increasing cement content, with the most significant improvement at 5% cement content. With increasing rubber particle content, the uniaxial compressive strength of the improved roadbed first increased and then decreased, with a significant improvement at 1% rubber particle content. Error analysis between the calculated and measured results revealed that the relative error under different test conditions was less than ±6%, demonstrating the high reliability and accuracy of the compressive strength prediction formula and indicating that the model can accurately describe the development of the uniaxial compressive strength of rubber particle-cement improved soil.

[0133] As a further solution of this embodiment, in order to further improve the performance of the construction roadbed, this embodiment adds nano-TiO2 particles or nano-SiO2 particles to the aeolian soil particles in the construction roadbed, and calibrates the particle flow PFC by using the response surface equation of nano-TiO2 particles or nano-SiO2 particles and rubber particles. 2D Model. Incorporating nano-TiO2 particles into the roadbed improves its flexural strength. Nano-TiO2 particles also enhance the roadbed's microstructure, reducing pores and making microcracks more compact and smooth, thereby improving durability. Nano-SiO2 particles, on the other hand, increase the compressive and shear strengths of the mixed material, enhancing the overall stability of the roadbed.

[0134] Preferably, in this embodiment, nano-TiO2 particles are added to the roadbed during construction. In order to obtain the relationship between the compressive strength of the nano-TiO2 particles and the rubber particles, aeolian soil particles are added to the sample at a percentage of Nano-TiO2 particles are obtained, and the response surface formula of nano-TiO2 particles and rubber particles is obtained according to the response surface method:

[0135]

[0136] in, is the compressive strength, in MPa. The response surface formula is a numerical fit and the unit does not need to be considered; is the percentage of nano-TiO2 particles, PRC is the percentage of rubber particles.

[0137] The response surface equation of nano-TiO2 and rubber particles is used to accurately describe the effect of nano-TiO2 on the compressive strength of rubber particle improved roadbed, which is used to optimize the particle flow PFC. 2D The model provides a theoretical basis for further improving the mechanical properties and durability of the roadbed. Compared to full factorial design, Response Surface Methodology (RSM) can obtain valuable information with fewer experiments through methods such as central composite design (CCD) or Box-Behnken design, saving time and cost.

[0138] In summary, its beneficial effects are:

[0139] The present invention uses digital speckle testing to accurately measure the micro-deformation of the sample surface, which is convenient for determining the local deformation and failure process of the sample under uniaxial compression test, providing more accurate sample compressive strength data, reducing the error of the compressive strength prediction formula, and the compressive strength prediction formula can be used to predict the optimal dosage of rubber particles and cement, and further through the particle flow PFC 2D The model can simulate the contact and interaction between particles and effectively predict the performance of the constructed roadbed during actual use, thereby guiding construction design and optimization, improving construction efficiency and roadbed durability, and providing a systematic and reliable method for the improvement and optimization of the constructed roadbed.

[0140] Example 2

[0141] This embodiment is a further limitation made on the basis of Example 1. The main improvement lies in the method for optimizing the amount of rubber particles and cement in the roadbed under freeze-thaw cycles in cold areas. For other parts not mentioned, refer to Example 1 or the prior art.

[0142] Freeze-thaw cycles refer to the number of times soil or rock freezes and thaws within a certain period of time. In railway engineering, freeze-thaw cycles have a significant impact on the stability of the roadbed, especially in railways in cold regions. Freeze-thaw can cause damage to the soil structure and reduce the mechanical properties of the soil, such as elastic modulus, cohesion, and internal friction angle, thereby affecting the bearing capacity and stability of the roadbed. Example 1 only considers the roadbed in a normal temperature environment and does not consider the impact of freeze-thaw cycles on the roadbed in cold regions.

[0143] This embodiment provides a method for optimizing the amount of rubber particles and cement in a roadbed subjected to freeze-thaw cycles in cold regions:

[0144] A1. Prepare multiple specimens containing the same rubber particle content and cement content, and randomly apply multiple dots on the surface of each specimen as an artificial speckle field.

[0145] A2. Repeat freeze-thaw cycles for different numbers of times on multiple samples to complete the freeze-thaw cycle experiment.

[0146] In this embodiment, four samples were prepared, and the freeze-thaw cycle numbers of the four samples were 0, 1, 5, and 10, respectively.

[0147] A3. Perform a uniaxial compression test on each sample, and use multiple cameras to record the three-dimensional deformation image of the artificial speckle field of each sample that continuously changes during the uniaxial compression test.

[0148] A4. The three-dimensional deformation image is processed using the digital speckle test calculation method to obtain the uniaxial compressive strength curve of each sample.

[0149] A5. Through the uniaxial compressive strength curves of multiple specimens, the damage characteristics and mechanical property attenuation laws of the specimens after experiencing different numbers of freeze-thaw cycles are determined.

[0150] In order to obtain the variation law of the displacement field of the roadbed containing rubber particles and cement during freeze-thaw cycles, the optimization method of the rubber particle and cement content also includes:

[0151] A6. Based on the structure of the roadbed in cold regions and the mechanical parameters of different soil layers in the roadbed, a three-dimensional model of the roadbed before and after improvement is established.

[0152] A7. Set the boundary conditions of the roadbed and the train load, and use numerical analysis software to create a three-dimensional model of the roadbed before and after improvement to simulate the vertical displacement that occurs when a train passes.

[0153] A8. Based on different vertical displacements, obtain the displacement field variation law of the roadbed containing rubber particles and cement under the same number of freeze-thaw cycles. In this embodiment, the simulated train passes from beginning to end once, and the vertical displacements of the roadbed before and after improvement are respectively referenced to Figure 14 and Figure 15 Compared with the unimproved roadbed, the displacement of the roadbed improved with rubber particles-cement was reduced. This shows that using rubber particles-cement to improve the roadbed can reduce roadbed settlement to a certain extent and effectively improve the strength and frost resistance of the roadbed.

[0154] A9. Based on the damage characteristics and mechanical property attenuation patterns of samples after freeze-thaw cycles, as well as the displacement field variation patterns of roadbeds containing rubber particles and cement under the same vertical load, the rubber particle and cement content of roadbeds in cold regions are optimized.

[0155] This embodiment takes into account the impact of freeze-thaw cycles on the stability of the roadbed by subjecting the sample to freeze-thaw cycles, fills the gap in the research of roadbeds in normal temperature environments, facilitates further optimization of material ratios, improves the stability and durability of roadbeds in cold areas, and reduces maintenance costs.

[0156] Although the specific embodiments of the invention are described in detail in conjunction with the accompanying drawings, this should not be construed as limiting the scope of protection of this patent. Within the scope described by the claims, various modifications and variations that can be made by those skilled in the art without creative work still fall within the scope of protection of this patent.

Claims

1. A method for optimizing roadbed improvement by rubber particles and cement content, characterized in that: The following steps are involved: S1. Prepare samples with different rubber particle and cement content, and randomly apply multiple dots on the surface of each sample as an artificial speckle field; S2. Perform a uniaxial compression test on each sample, and use multiple cameras to record the three-dimensional deformation image of the artificial speckle field of each sample that continuously changes during the uniaxial compression test; S3. Process the three-dimensional deformation image using a digital speckle test calculation method to obtain a uniaxial compressive strength curve for each specimen; Step S3 further comprises: S31. 3D initial image obtained without uniaxial compression test on the sample and the three-dimensional deformation image obtained by uniaxial compression test Perform grayscale value normalization processing; S32, pairing the three-dimensional initial image with the multiple dots on the three-dimensional deformed image, and setting the initial displacement of each dot by fast Fourier transform ( ); S33. Calculate the error function E of the center point displacement of each dot spot: in, is the scale factor; is a variable; For the The weight parameter of each dot; is the initial three-dimensional image middle The gray value at For the dots in the initial image The coordinates in ; 3D deformed image middle The gray value at For the Dots in the 3D deformed image The coordinates in ; and They are the three-dimensional initial images and 3D deformed images Gray mean value; and They are the three-dimensional initial images and 3D deformed images Grayscale standard deviation; For the The distance from the dot to the center of the sample; S34, Judgment Is it less than the set value? If so, go to step S35; otherwise, go to step S37. S35, according to the displacement of each dot spot ( )、 and Gray value, and the three-dimensional initial image and 3D deformed images Grayscale standard deviation and , calculate the Jacobian matrix , residual vector , calculate the gradient and the Hessian matrix : S36, update the displacement ( ) , and returns to step S33; wherein, is the parameter update amount, ; S37. Calculate the strain field of the sample based on the final displacements of the multiple spots and obtain a uniaxial compressive strength curve; S4. Obtaining a compressive strength prediction formula for the improved roadbed containing rubber particles by fitting the uniaxial compressive strength curves of multiple specimens; S5. Establish and calibrate the PFC of the roadbed based on the compressive strength prediction formula 2D Model; S6, using granular flow PFC 2D The model simulates the construction roadbed and determines the rubber particle and cement content according to the strength requirements of the construction roadbed; Particle Flow 2D The shape of the model is the same as that of the specimen, and the particle flow PFC 2D The contact mode within the model is parallel bonded contact; Particle Flow (PFC) 2D The model further includes the following methods for simulating the construction roadbed: S61. Select a typical roadbed section in the construction roadbed and record the actual stress distribution data of the roadbed section under different load conditions; S62, using granular flow PFC 2D The model simulates the roadbed section under different load conditions to obtain simulated stress distribution data; S63, calculate the error between the actual stress distribution data and the simulated stress distribution data, if the error is less than 5%, proceed to step S64, otherwise adjust the particle flow PFC 2D The parameters of the model and return to step S62; S64, determine the particle flow PFC 2D The model is accurate.

2. The roadbed improvement optimization method of rubber particles and cement content according to claim 1, characterized in that: The preparation method of each sample is as follows: S11, cutting the waste tires, removing the metal, crushing them, and sieving them to obtain rubber particles with a diameter of 0.09 mm to 0.85 mm; S12, mixing aeolian soil particles, cement, and rubber particles according to a set ratio to obtain a mixture, and configuring the mixture into a cylindrical structure sample according to an optimal moisture content; S13. Place the sample in a constant temperature and humidity chamber for 7 days, and spray a thin layer of white spray paint on the surface of the sample as the base color. After the sample is naturally air-dried, randomly dot the surface with uniform size and shape as an artificial speckle field.

3. The roadbed improvement optimization method of rubber particles and cement content according to claim 2, characterized in that: The method for determining the optimal moisture content of each sample is: S121. The mixtures in each sample are divided into groups and prepared according to different moisture contents. A compaction test is performed on each group of mixtures, and the wet density after compaction is recorded. S122. Calculate the dry density of each mixture in step S121 based on the wet density: in, is the wet density, is the dry density; S123. Numerical fitting is performed using multiple sets of moisture content and dry density to obtain the fitting formula of moisture content and dry density: in, is the dry density, is the moisture content, 、 and is a constant, is the correction factor, ; S124, according to the fitting curve of moisture content and dry density, The maximum value corresponds to .

4. The method for optimizing roadbed improvement by using rubber particles and cement content according to claim 1, wherein: The loading rate of the testing machine in the uniaxial compression test was 0.5 mm / min, and the capture rate of each camera was 0.7 frame / s.

5. The method for optimizing roadbed improvement by using rubber particles and cement content according to claim 1, characterized in that: The compressive strength prediction formula is: in, is the 7-day uniaxial compressive strength, in kPa; CS is the percentage of cement content; PRC is the percentage of rubber particles.

6. The method for optimizing roadbed improvement by using rubber particles and cement content according to claim 1, characterized in that: The sample also includes aeolian soil particles mixed with a percentage of Nano-TiO2 particles are obtained, and the response surface formula of nano-TiO2 particles and rubber particles is obtained according to the response surface method: in, is the compressive strength, in MPa, is the percentage of nano-TiO2 particles, PRC is the percentage of rubber particles.

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