A control method for road load of ARA equipment in freezing-thawing period

Abstract

The application discloses a control method for ARA equipment road surface load in the freezing and thawing period, which is as follows: 1, corresponding sensors are arranged on each structural layer of the asphalt pavement; 2, the temperature and humidity at different depths are studied in relation to the freezing and thawing time; 3, the strain of each layer and the deflection of the road mark are studied in relation to the freezing and thawing time; 4, an empirical model is established based on the evolution law of the mechanical response of the asphalt pavement under freezing and thawing; 5, the relationship between the independent variables in the empirical model and the pavement structure performance is analyzed to determine the related parameters in the empirical model; 6, the error of the empirical model is determined, the change of the mechanical response caused by the overload during the freezing period is summarized, the damage characteristics of the road surface caused by the winter load are determined according to the test and modeling results, and the load limitation in the freezing and thawing process in winter is proposed. The method can explore the change of the asphalt pavement structure performance during the freezing period, and provides a reference for the proposal of the dynamic load limitation range in the cold climate area.

Description

Technical Field

[0001] This invention belongs to the technical field of highway asphalt pavement structure loading application, and relates to a method for controlling pavement load during the freezing period, specifically a method for controlling pavement load based on ARA equipment during the freeze-thaw period. Background Technology

[0002] After long-term exposure to loads and environmental conditions, asphalt pavement maintenance and repair become paramount. Statistics show that in recent years, the annual mileage of major and medium-scale maintenance and repairs on highways in my country has reached 8,000 kilometers. This problem is particularly severe in the Northeast region, where winters are long and cold and heavy vehicle traffic is frequent. In these cold regions, it is generally believed that environmental factors and traffic loads affect the performance of flexible pavements. Therefore, the rational control of load conditions is of utmost importance.

[0003] During freeze-thaw cycles, changes in moisture and temperature play a crucial role in altering pavement performance. First, pore water in the pavement structure forms ice lenses upon freezing. These ice lenses attract water from the freezing front through capillary action, increasing ice accumulation and leading to frost heave. During spring thawing, the accumulated ice in the pavement structure melts back into water, increasing the moisture content of the unbonded layer. This excessive moisture content in the unbonded material reduces its elastic modulus and increases the likelihood of permanent strain and fatigue cracking in flexible pavements under traffic loads. All these effects accelerate pavement degradation in cold regions. Therefore, studying the mechanical laws and structural performance changes during the freeze-thaw process of pavements in cold regions, and subsequently proposing reasonable maintenance and operation policies, has become a key direction for mitigating pavement degradation and extending road service life.

[0004] Current research on the mechanical response and evolution of roads mainly focuses on two aspects: field observation and laboratory testing. While analyzing data from long-term field observations and tests throughout a road's lifespan can yield high accuracy and reliability for studying pavement structure, material composition, failure mechanisms, and construction techniques, the data collection period is too long, experimental results are severely delayed, and enormous human, material, and financial resources are consumed. Furthermore, road environments and traffic loads change constantly, making it difficult to accurately evaluate pavement structural performance and meet the needs of current road construction and rapid transportation development.

[0005] Traditional indoor sampling tests are widely used due to their low cost and short testing time. However, the stress state of small specimens in high-temperature deformation and fatigue tests differs significantly from the actual service condition of pavements during operation. This makes them unsuitable for accurately reflecting the damage process of asphalt pavements under real-world conditions, leading to significant deviations in evaluating the high-temperature deformation and fatigue performance of asphalt mixtures. Furthermore, the test results differ greatly from actual pavement service life, with vastly different correction factors proposed by different researchers, making direct comparison impossible. Given these limitations of small-specimen testing, it is necessary to find a loading method that more closely approximates the actual operating conditions of pavements to investigate their high and low temperature performance and fatigue characteristics under real-world conditions.

[0006] Many researchers have quantitatively determined the impact of freeze-thaw cycles on the performance of flexible pavement structures. A study by Salour et al. showed that back-calculation of FWD measurements revealed a 63% loss in subgrade soil stiffness and a 48% decrease in stiffness of the granular base course and subbase course during spring thawing, compared to summer values. Furthermore, many countries have investigated pavement performance over the course of a year and developed dynamic load limitation policies for cold climates. These policies help manage road conditions during spring thawing and winter frosts. To implement these policies, it is necessary to further understand the structural performance changes of flexible pavement structures during freeze-thaw processes, thereby establishing a basis for pavement load limitations. Summary of the Invention

[0007] The purpose of this invention is to provide a method for controlling pavement load based on ARA equipment during the freeze-thaw period. This method can explore the changes in the structural performance of asphalt pavement during freezing and provide a reference for proposing dynamic load limits in cold climate regions.

[0008] The objective of this invention is achieved through the following technical solution:

[0009] A method for controlling road surface load based on ARA equipment during the freeze-thaw period includes the following steps:

[0010] Step 1: Install corresponding sensors in each structural layer of the asphalt pavement. The sensors include strain sensors, pressure sensors, tire pressure sensors, temperature sensors, humidity sensors, and suction sensors.

[0011] Step 2: Use ARA equipment to simulate actual traffic loads and collect temperature and humidity field data during the freezing and thawing process of asphalt pavement to study the changes in temperature and humidity at different depths with freezing and thawing time.

[0012] Step 3: Track and collect temperature structural mechanical response data of asphalt pavement during freezing and thawing, and study the changes in strain of each layer and deflection of road signs with freezing and thawing time;

[0013] Step 4: Based on the evolution law of the mechanical response of asphalt pavement under freeze-thaw conditions, the following empirical model is established:

[0014]

[0015] In the formula, m i The pavement mechanical response is given after i hours of freezing; m0 is the initial pavement mechanical response; fr base For the ratio of freezing depth at the grassroots level; fr subbase The ratio of freezing depth to subgrade depth; fr subgrade The ratio of frost depth to roadbed depth, 0≤f i ≤100; w base For the unfrozen moisture content at the grassroots level; w subbase The unfrozen moisture content of the base layer; w subgrade The unfrozen moisture content of the roadbed; A, B, C, D, E, and F are model correction parameters; when 0 <fr base When <100, w base =b1fr base +b2; when 0 <fr subbase <100, When 0 <fr subbase When <100, w subbase =b6fr subbase +b7; when 0 <fr subgrade <100, When 0 <fr subgrade <100, a1~a 19 b1~b 14 These are model parameters;

[0016] Step 5: By analyzing the relationship between the independent variables in the empirical model and the pavement structure performance, determine the relevant parameters in the empirical model;

[0017] Step 6: Compare the calculated values ​​of the empirical model with the test response values ​​to determine the error of the empirical model, summarize the changes in mechanical response caused by overload during the freezing period, clarify the damage characteristics of winter loads on the pavement based on these test and modeling results, and propose load limits during the winter freezing and thawing process.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] 1. Accelerated loading pavement testing allows for rapid experimental research on pavement conditions and performance under near-real-world usage conditions. Results are obtained in the shortest possible time to guide practical work. It can be applied to empirical comparisons of different pavement types, road materials, and load types, as well as to verify theoretical models of pavement response and material conditions. Compared to traditional long-term field observation data throughout the road's lifespan, this invention features high precision and a short cycle, enabling real-time observation of changes in the road environment and traffic loads, thereby clarifying changes in pavement structural performance. It also offers economic advantages such as low manpower and material consumption and convenient observation and control. Compared to traditional small-sample indoor tests, the test conditions of this invention are closer to the actual operating conditions of the pavement, and the load and temperature conditions can be adjusted as needed, resulting in closer resemblance to actual pavement usage and less data discrepancy.

[0020] 2. This invention utilizes an ARA (Advanced Radiation Protection) controlled low-temperature accelerated loading device to conduct accelerated loading tests on asphalt pavement structures under different temperature and load conditions. This effectively simulates the actual working state of the pavement under traffic loads and the effects of environmental factors, simulating long-term field service loads in a short time and establishing the variation patterns of pavement structure performance indicators. This allows for the evaluation of the long-term performance of the asphalt pavement structure. Based on a clear understanding of the evolution of pavement structure performance, it enables refined and scientific control of pavement loads during freeze-thaw cycles. Attached Figure Description

[0021] Figure 1 Temperature fields at different times during the freezing process;

[0022] Figure 2 The relationship between isotherm depth and time;

[0023] Figure 3 This shows the temperature field distribution at different depths during the freezing process.

[0024] Figure 4 This represents the longitudinal strain variation of the asphalt layer;

[0025] Figure 5 To compare the test response value with the model response value;

[0026] Figure 6 The mechanical response caused by a 10% overload during freezing;

[0027] Figure 7 The mechanical response caused by a 20% load limitation during freezing;

[0028] Figure 8 This represents the tensile strain at the bottom of the asphalt layer.

[0029] Figure 9 These represent the strain values ​​for each structural layer. Detailed Implementation

[0030] The technical solution of the present invention will be further described below with reference to the accompanying drawings, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention that do not depart from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention.

[0031] This invention studies the structural performance evolution of flexible pavements during freeze-thaw cycles. Different types of sensors are installed in each pavement layer, and an ATLAS22 accelerated transport loading system manufactured by ARA Corporation is used to simulate traffic loads on the pavement. Cooling air is applied to the pavement surface to monitor changes in mechanical and environmental factors within the pavement structure. This provides a method for controlling pavement load during the freeze-thaw period based on ARA equipment, maximizing pavement protection. The method includes the following steps:

[0032] Step 1: Install corresponding sensors in each structural layer of the asphalt pavement. The sensors include strain sensors, pressure sensors, tire pressure sensors, temperature sensors, humidity sensors, and suction sensors.

[0033] Step 2: Use ARA equipment to simulate actual traffic loads and collect temperature and humidity field data during the freezing and thawing process of asphalt pavement to study the changes in temperature and humidity at different depths with freezing and thawing time.

[0034] Step 3: Track and collect temperature structural mechanical response data of asphalt pavement during freezing and thawing, and study the changes in strain of each layer and deflection of road signs with freezing and thawing time.

[0035] Step 4: Based on the evolution of the mechanical response of asphalt pavement under freeze-thaw cycles, the role of each layer in the change of pavement performance is studied. The freezing state of the material is represented by frost depth and moisture content, describing its mechanical properties. When plotting the curves of mechanical response versus frost depth or moisture content, a strong power function and exponential function relationship is observed. Therefore, after studying the relationship between the change in mechanical response and the frost depth ratio and moisture content, a generalized empirical model is proposed:

[0036]

[0037] In the formula, m i The pavement mechanical response (stress, strain) after i hours of freezing; m0 is the initial pavement mechanical response; fr base For the ratio of freezing depth at the grassroots level; fr subbase The ratio of freezing depth to subgrade depth; fr subgrade The ratio of frost depth to roadbed depth, 0≤f i ≤100; w base For the unfrozen moisture content at the grassroots level; wsubbase The unfrozen moisture content of the base layer; w subgrade The unfrozen moisture content of the roadbed; A, B, C, D, E, and F are model correction parameters;

[0038] When 0 <fr base When <100, w base =b1fr base +b2; when 0 <fr subbase <100, When 0 <fr subbase When <100, w subbase =b6fr subbase +b7; when 0 <fr subgrade <100, When 0 <fr subgrade <100, a1~a 19 b1~b 14 These are the model parameters.

[0039] Step 5: By analyzing the relationship between the independent variables (freezing depth ratio, mass moisture content, etc.) in the empirical model and the pavement structure performance, determine the relevant parameters in the empirical model. The specific steps are as follows:

[0040] Step 51: During the modeling process, by analyzing the relationship between independent variables (freezing depth ratio, mass moisture content, etc.) in the empirical model and the pavement structure performance, determine the model parameters a1 to a2. 19 b1~b 14 By determining the relationship between the horizontal longitudinal strain of the asphalt concrete layer and the variation of the independent variable, the parameters a1 to a1 in the empirical model can be determined one by one. 19 Since strain is one of the most commonly used indicators in pavement design and performance prediction, model parameters were determined by strain variation. Parameters b1~b 14 It is a material coefficient related to freezing and does not depend on the type of mechanical response. Therefore, it can be determined by analyzing data on the longitudinal strain variation of the asphalt layer. This data analysis method is also applicable to other mechanical response values ​​of the pavement.

[0041] Step 5.2: After determining all model parameters a1 to a2... 19 b1~b 14 Then, the model correction parameters A, B, C, D, E, and F are set to initial values. Using the least squares error method, the error between the empirical model and the experimental mechanical behavior is calculated and minimized by repeatedly adjusting the model correction parameters A, B, C, D, E, and F, thereby obtaining all model correction parameters.

[0042] Step Six: Compare the calculated values ​​from the empirical model with the test response values ​​to determine the error of the empirical model. Correct and modify most of the considered mechanical response parameters, summarize the changes in mechanical response caused by overloading during the freezing period, and clarify the damage characteristics of winter loads on the pavement based on these test and modeling results. Propose load limits during the winter freezing and thawing process. Through model establishment, the mechanical response of each pavement layer under different load limit conditions (e.g., a 20% load limit) can be calculated, thus providing a basis for road load limits (based on the mechanical characteristics between layers, propose reasonable pavement load limit conditions, such as load limits and flow restrictions).

[0043] Example:

[0044] Taking the mechanical response of asphalt pavement under unidirectional long-term freeze-thaw cycles as an example, the specific implementation steps of the method for controlling pavement load during the freeze-thaw period based on the ARA accelerated loading device of the present invention are explained:

[0045] Step 1: Install strain sensors, pressure sensors, tire pressure sensors, temperature sensors, humidity sensors, and suction sensors in each structural layer of the asphalt pavement.

[0046] Table 1. Material and sensor information used for testing road surface structures.

[0047]

[0048] Step 2: Collect temperature and humidity field data during the freezing and thawing process of asphalt pavement, and study the changes in temperature and humidity at different depths with freezing and thawing time.

[0049] Based on existing research findings, there is a linear relationship between the square root of freezing time and freezing depth. Therefore, the independent variable on the horizontal axis is the square root of freezing time, and the dependent variables are volumetric water content and matrix suction.

[0050] Step 3: Track and collect temperature structural mechanical response data of asphalt pavement during freezing and thawing, and study the changes in strain of each layer and deflection of road signs with freezing and thawing time.

[0051] The freezing process can be divided into five separate stages. In the first and second stages, the temperature in the asphalt layers gradually decreases until all asphalt layers freeze. The decrease in temperature in the asphalt concrete leads to an increase in the stiffness of the asphalt layers, resulting in a decrease in tensile strain and vertical strain in the bottom and lower layers, respectively. The third stage relates to the freezing of the base course. During this process, the tensile strain in the asphalt concrete and the vertical strain in the base course decrease more rapidly than the vertical strain in the subbase and subgrade. For the fourth stage, under complete freezing, the strain in the asphalt concrete and base course remains almost constant. As the subbase gradually freezes, the strain in the subbase and subgrade also gradually decreases. A similar strain change occurs in the fifth stage. Although the strain in the base course changes smoothly, the strain in the subgrade continues to decrease. Over time, the strain changes between layers and the changes in pavement deflection are studied.

[0052] Step 4: Based on the evolution of the mechanical response of asphalt pavement under freeze-thaw cycles, an empirical model was established, using frost depth and moisture content to represent the freezing state of the material and describe its mechanical properties. Therefore, after studying the relationship between changes in mechanical response and frost depth ratio and moisture content, a generalized empirical model was proposed. Although both frost depth ratio and the moisture content of the base course affect the mechanical response of the pavement structure, their effects on changes in pavement mechanical properties differ. Therefore, six model parameters A to F were introduced into the empirical model.

[0053] Step 5: By analyzing the relationship between the independent variables (freezing depth ratio, mass moisture content, etc.) in the model and the pavement structure performance, determine the relevant parameters in the model.

[0054] By observing the longitudinal strain changes in the asphalt layer, the parameters a1 to a in the model can be determined one by one. 19 This data analysis method is also applicable to other mechanical response values ​​of the pavement. Since strain is one of the most commonly used indicators in pavement design and performance prediction, model parameters are determined by strain variation, as shown in Table 2. The zero value indicates that the strain in the model is independent of the independent variables.

[0055] Table 2 Strain Change Model Parameters

[0056]

[0057] Parameters b1~b 14 It is a material coefficient related to freezing and does not depend on the type of mechanical response. Therefore, it can be determined by analyzing data on the longitudinal strain variation of the asphalt layer, as shown in Table 3.

[0058] Table 3 w i -fr i Model parameters

[0059]

[0060] After determining all model parameters a1~a 19 and b1~b 14 Then, the model correction parameters (A to F) were set to initial values. Next, the least squares error method was used to calculate and minimize the error between the model and the changes in experimental mechanical behavior by repeatedly adjusting the model calibration parameters. Through this calculation process, the optimized set of the final model calibration parameters was obtained.

[0061] Table 4 Correction parameters for strain change model

[0062]

[0063]

[0064] Step 6: Compare the calculated values ​​of the model with the test response values ​​to determine the model error, summarize the changes in mechanical response caused by overloading during the freezing period, clarify the damage characteristics of winter loads on the road surface based on these test and modeling results, and propose overloading restrictions or traffic flow restrictions.

[0065] The modeling process can also be applied to describe stress variations in structures. The small discrepancy between the calculated and tested response values ​​indicates that the model can predict and interpret most experimental values ​​well. The final root mean square error (RMSE) was determined to be 2.9, demonstrating the model's good accuracy.

[0066] For most of the mechanical response parameters considered in the model, a 10% overload will cause an approximately 10% increase in strain and stress in the pavement structure. During the spring thawing process, loading was performed with a reduced standard load. Loading during the thawing process with 50kN and 40kN loads respectively showed that reducing the load by 20% could reduce the stress and strain in the pavement structure by 10–20%.

[0067] Therefore, for asphalt pavements in cold regions, to reduce structural fatigue damage caused by traffic loads at various stages, improve road transport efficiency and value, and ensure the service durability of each structural layer of the asphalt pavement, dynamic load control methods under different temperature conditions can be adopted. For example, in winter, considering the principle of damage equivalence, the allowable traffic axle load can be appropriately increased. During the spring thaw, because the damage caused by unit load is greater, it is necessary to impose greater restrictions on the existing standard axle load to ensure the service life of the asphalt pavement.

Claims

1. A method for controlling road surface load based on ARA equipment during the freeze-thaw period, characterized in that... The method includes the following steps: Step 1: Install corresponding sensors in each structural layer of the asphalt pavement; Step 2: Use ARA equipment to simulate actual traffic loads and collect temperature and humidity field data during the freezing and thawing process of asphalt pavement to study the changes in temperature and humidity at different depths with freezing and thawing time. Step 3: Track and collect temperature structural mechanical response data of asphalt pavement during freezing and thawing, and study the changes in strain of each layer and deflection of road signs with freezing and thawing time; Step 4: Based on the evolution law of the mechanical response of asphalt pavement under freeze-thaw conditions, the following empirical model is established: In the formula, For freezing Road surface mechanical response after hours; This represents the initial pavement mechanical response. For the ratio of freezing depth at the grassroots level; The ratio of freezing depth to subgrade depth; The ratio of the frost depth to the roadbed; For the unfrozen moisture content at the grassroots level; The moisture content of the unfrozen base layer; This refers to the unfrozen moisture content of the roadbed. , , , , , Adjust parameters for the model; when 0 < <100, When 0 < <100, When 0 < <100, When 0 < <100, When 0 < <100, ; ~ , ~ These are model parameters; Step 5: By analyzing the relationship between the independent variables in the empirical model and the pavement structure performance, determine the relevant parameters in the empirical model. The specific steps are as follows: Step 51: During the modeling process, by determining the relationship between the horizontal and longitudinal strains of the asphalt concrete layer and the changes in independent variables, the parameters in the empirical model are determined one by one. ~ Parameters were determined by analyzing data on the longitudinal strain variation of the asphalt layer. ~ ; Step 52: After determining all model parameters ~ , ~ Then, the model parameters are adjusted. , , , , , The parameters are corrected by repeatedly adjusting the model and setting the initial values ​​using the least squares error method. , , , , , The error between the empirical model and the experimental mechanical behavior is calculated and minimized, thereby obtaining all model correction parameters; Step 6: Compare the calculated values ​​of the empirical model with the test response values ​​to determine the error of the empirical model, summarize the changes in mechanical response caused by overload during the freezing period, clarify the damage characteristics of winter loads on the road surface based on the test and modeling results, and propose load limits during the winter freezing and thawing process.

2. The method for controlling road surface load based on ARA equipment during the freeze-thaw period according to claim 1, characterized in that... The sensors include strain sensors, pressure sensors, tire pressure sensors, temperature sensors, humidity sensors, and suction sensors.

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