Sampling method for dynamic modulus of asphalt pavement structure based on measured temperature field

Abstract

The application discloses a kind of asphalt pavement structure dynamic modulus sampling method based on measured temperature field, it is related to road engineering technical field.The method of the present application is based on the measured temperature history data of active asphalt pavement structure measured by asphalt pavement long-term performance observation network according to the design scheme of the asphalt pavement structure to be laid, the temperature field history data set is constructed, the temperature field history data set is updated by numbering and reassigning to measured temperature history data, the frequency of the same group temperature history data is counted, the cumulative probability distribution curve is generated, the temperature distribution characteristics inside active asphalt pavement structure are obtained, then temperature data sample is generated using inverse function sampling and regression fitting analysis, the internal temperature field of the asphalt pavement structure to be laid is calculated with dynamic modulus master curve to obtain the dynamic modulus of the asphalt pavement structure to be laid.The present application carries out temperature field sampling based on actual physical conditions, and realizes the accurate restoration of the mechanical characteristics of asphalt pavement structure under complex temperature environment.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and specifically to a method for sampling the dynamic modulus of asphalt pavement structure based on measured temperature fields. Background Technology

[0002] The design of pavement structure is crucial in the construction of asphalt pavements for highways, as its lifespan and performance are influenced by various factors such as temperature changes, material properties, and load effects. Among numerous design parameters, the dynamic modulus, as an important indicator characterizing the viscoelastic properties of asphalt mixtures, directly affects the fatigue cracking, rutting deformation, and overall durability of the pavement structure under long-term loads. With the development of numerical simulation and reliability design concepts, pavement structure design based on Monte Carlo simulation has gradually gained attention. Monte Carlo simulation uses random sampling methods to generate a large number of possible states to predict the performance of pavement structures under long-term loads. The sampling method of design parameters is key to improving the accuracy of Monte Carlo simulations, making the sampling method of the dynamic modulus of asphalt mixtures directly determine the reliability and accuracy of asphalt pavement structure design.

[0003] In practical applications, the sampling of the dynamic modulus of asphalt mixtures in pavement structures is often based on a simple processing of the coefficient of variation. That is, by giving the average value and the coefficient of variation, the distribution of the data is assumed, and random sampling is generally performed using a log-normal distribution. This method is simple and easy to implement, but it ignores the influence of spatial temperature gradients and fails to consider the impact of temperature differences at different depths and times on the dynamic modulus changes of asphalt pavement structures. The physical significance of the sampling is not strong. In other words, the simple statistical sampling currently used is only based on empirical statistical data and fails to fully combine with actual physical conditions, thus failing to capture the fluctuations of actual asphalt pavement performance parameters.

[0004] Because the structure of asphalt pavement is significantly affected by changes in ambient temperature, especially the diurnal temperature variation and seasonal changes, the dynamic modulus of the asphalt pavement structure is more likely to change. Although the traditional single statistical sampling can theoretically reflect randomness, it cannot fully reflect the influence of temperature on the dynamic modulus of the asphalt pavement structure in practical applications, and it is difficult to accurately reproduce the mechanical characteristics of the asphalt pavement structure under complex temperature environments. Summary of the Invention

[0005] This invention aims to solve the above problems and provides a sampling method for the dynamic modulus of asphalt pavement structure based on measured temperature field. It uses historical measured temperature data from the long-term performance observation network of asphalt pavement to sample the temperature field, which breaks through the limitations of traditional single statistical sampling. It provides technical support for the accurate restoration of the mechanical characteristics of asphalt pavement structure under complex temperature environment and improves the reliability of asphalt pavement structure design.

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

[0007] The sampling method for dynamic modulus of asphalt pavement structure based on measured temperature field includes the following steps:

[0008] Step 1: Obtain the design scheme of the asphalt pavement structure to be paved, use the long-term performance monitoring network of asphalt pavement to obtain the measured historical temperature field data of the existing asphalt pavement structure, extract the historical temperature values ​​at a specified depth inside the existing asphalt pavement structure according to the preset time points, obtain multiple sets of historical temperature field data, and construct a set of historical temperature field data.

[0009] Step 2: Number each group of historical temperature data in the historical temperature field dataset;

[0010] Step 3: Set the temperature range and reassign the historical temperature values ​​in the historical temperature data set to obtain the reassigned historical temperature data set.

[0011] Step 4: Calculate the frequency of occurrence of the same group of historical temperature data in the reassigned historical temperature data set, update the number of each group of historical temperature data, and generate a cumulative probability distribution curve.

[0012] Step 5: Randomly generate a random number in [0,1]. Based on the generated random number, perform inverse function sampling on the cumulative probability distribution curve to determine the number of the historical temperature data corresponding to the random number, and extract the historical temperature data corresponding to the random number.

[0013] Step 6: Use cubic spline smoothing curves along the depth direction to perform data analysis on the historical temperature data extracted in Step 5, and fit the regression equation of the historical temperature data.

[0014] Step 7: Determine the depth of the middle position of each structural layer based on the thickness of each structural layer of the asphalt pavement structure to be laid, obtain the middle depth of each structural layer of the asphalt pavement structure to be laid, and substitute it into the temperature history data regression equation determined in Step 6 to calculate the temperature at the middle position of each structural layer of the asphalt pavement structure to be laid, and obtain the internal temperature of each structural layer of the asphalt pavement structure to be laid.

[0015] Step 8: Based on the design scheme of the asphalt pavement structure to be paved, obtain the master curve of the dynamic modulus of the asphalt mixture used in the asphalt pavement structure to be paved. Combined with the load frequency on the asphalt pavement structure to be paved, substitute the load frequency of the asphalt pavement structure to be paved and the internal temperature of each structural layer into the master curve of the dynamic modulus of the asphalt mixture used in the asphalt pavement structure to be paved, and obtain the dynamic modulus sampling data to complete the dynamic modulus sampling of the asphalt pavement structure to be paved.

[0016] Preferably, the design scheme includes structural parameters and material parameters of the asphalt pavement structure to be paved. The structural parameters include the thickness of each structural layer in the asphalt pavement structure to be paved, and the material parameters include the material properties of each structural layer in the asphalt pavement structure to be paved.

[0017] Preferably, in step 1, at least two years of measured temperature field historical data of the existing asphalt pavement structure are obtained using the long-term performance monitoring network for asphalt pavement.

[0018] Preferably, in step 3, temperature ranges are set with an interval of 5°C, and each historical temperature value in the historical temperature field data set is rounded down and reassigned within the corresponding temperature range.

[0019] Preferably, in step 4, the historical temperature values ​​at all specified depths in the same group of historical temperature data are the same, and the historical data with the same temperature are numbered the same.

[0020] Preferably, the random number in step 5 is generated using a random number generator in Matlab software.

[0021] Preferably, the regression equation for the historical temperature data is:

[0022] y(x)=A×(xH) 3 +B×(xH) 2 +C×(xH)+D

[0023] In the formula, y(·) is the internal temperature of the structural layer, in °C; x is the depth of the middle position of the structural layer, in m; H is the depth of the middle position of the top structural layer of the asphalt pavement structure, in m; A, B, C, and D are all fitting coefficients.

[0024] Preferably, the load frequency on the asphalt pavement structure to be paved is determined based on the vehicle speed on the asphalt pavement structure to be paved, resulting in:

[0025]

[0026] In the formula, f is the load frequency in Hz; d is the thickness of the asphalt layer in the asphalt pavement structure to be paved in meters; and v is the vehicle speed on the asphalt pavement structure to be paved in kilometers per hour.

[0027] The beneficial technical effects brought about by this invention are as follows:

[0028] This invention proposes a sampling method for the dynamic modulus of asphalt pavement structure based on measured temperature fields. Based on measured temperature data from a long-term performance monitoring network for asphalt pavements, cumulative probability distribution curves are plotted using historical measured temperature field data of each structural layer of the existing asphalt pavement structure to obtain the internal temperature distribution characteristics. Then, temperature data samples are generated using inverse function sampling and regression fitting analysis is performed to estimate the temperature field characteristics within each structural layer of the asphalt pavement structure to be paved. Finally, the dynamic modulus of each structural layer in the asphalt pavement structure to be paved is obtained by combining the dynamic modulus master curve.

[0029] This invention overcomes the limitations of traditional single statistical sampling, fully considering the dynamic changes in the internal temperature of each structural layer of the asphalt pavement at different times. It can accurately reflect the influence of diurnal temperature difference and seasonal changes on the dynamic modulus of the asphalt pavement structure. By accurately calculating the temperature field characteristics and changes at different layers of the asphalt pavement structure and sampling the temperature field based on actual physical conditions, it achieves accurate restoration of the mechanical characteristics of the asphalt pavement structure to be laid under complex temperature conditions, thereby improving the reliability and engineering applicability of pavement structure design. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the pavement structure of a newly built highway.

[0031] Figure 2 This is a cumulative probability distribution curve. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0033] This embodiment takes a newly constructed highway to be paved in a certain area as an example. The newly constructed highway is the asphalt pavement structure to be paved, and its design scheme is as follows: Figure 1 As shown, the pavement structure consists of a top layer, an intermediate layer, a bottom layer, and a base layer, from top to bottom. The top layer comprises three layers: SMA-13, HAC-20, and AC-25. The SMA-13 ​​layer is 4cm thick and is constructed using asphalt mastic aggregate, with a nominal maximum aggregate size of 13mm, classifying it as a fine-grained asphalt mixture. The HAC-20 layer is 8cm thick and is constructed using high-performance asphalt concrete, with a nominal maximum aggregate size of 20mm, classifying it as a medium-grained asphalt mixture. The ATB-25 layer is 24cm thick and is constructed using asphalt-stabilized crushed stone, with a maximum nominal aggregate size of 25mm. The AC-13 layer is 4cm thick and is constructed using functional asphalt concrete, with a maximum nominal aggregate size of 13mm.

[0034] The existing full-thickness pavement structure near the newly built highway is equipped with a long-term performance monitoring network for asphalt pavement. The network can be used to measure the internal temperature of the existing asphalt pavement structure in real time and obtain historical data of the measured temperature field of the existing asphalt pavement structure.

[0035] In this embodiment, based on the historical measured temperature field data of existing full-thickness pavement structures, a dynamic modulus sampling method for asphalt pavement structures based on the measured temperature field proposed in this invention is adopted, specifically including the following steps:

[0036] Step 1: Obtain the design scheme of the asphalt pavement structure to be paved, determine the thickness and material properties of each structural layer in the asphalt pavement structure to be paved, obtain the master curve of the dynamic modulus of each structural layer in the asphalt pavement structure to be paved, use the long-term performance observation network of asphalt pavement to obtain two years of measured temperature field historical data of the existing full-thickness pavement structure near the asphalt pavement structure to be paved, extract the historical temperature values ​​at multiple specified depths inside the existing full-thickness pavement structure in a top-to-bottom order according to the preset time points, and obtain multiple sets of temperature field historical data, as shown in Table 1, and construct a temperature field historical data set.

[0037] Table 1 shows the temperatures at each hour on August 19, 2023, within the full-thickness pavement structure at distances of 0.02m, 0.04m, 0.07m, 0.10m, 0.15m, 0.20m, 0.25m, and 0.34m from the pavement surface, as recorded by the long-term performance monitoring network for asphalt pavement.

[0038] Table 1. Internal Temperature Statistics of Existing Full-Thickness Pavement Structures

[0039]

[0040]

[0041] Step 2: Number each group of historical temperature data in the historical temperature field dataset. The historical temperature data in the historical temperature field dataset are numbered from 1 to 17520.

[0042] Step 3: Set the temperature range according to the interval temperature of 5℃, determine the temperature range to which each historical temperature value in the historical temperature data set belongs, and reassign each historical temperature value in the historical temperature data set to the corresponding temperature range by rounding down. Table 2 shows the reassigned historical temperature data set.

[0043] Table 2. Internal Temperature Statistics of Existing Full-Thickness Pavement Structures After Reassignment.

[0044]

[0045] Step 4: Two sets of historical temperature data with identical historical temperature values ​​at all specified depths are considered as the same set of historical temperature data. Historical temperature data within the same set have the same identifier. The frequency of occurrence of historical temperature data within the same set is counted in the reassigned historical temperature data set. The identifiers of each set of historical temperature data are then updated, generating a dataset as follows: Figure 2 The cumulative probability distribution curve shown is shown.

[0046] Step 5: Use the random number generator in Matlab software to generate random numbers in [0,1]. Based on the generated random numbers, perform inverse function sampling on the cumulative probability distribution curve to determine the number of the historical temperature data corresponding to the random numbers, and extract the historical temperature data corresponding to the random numbers.

[0047] In this embodiment, the randomly generated random number is 0.526. The cumulative probability distribution curve is used for inverse function sampling. Based on the number of the historical temperature data corresponding to the random number 0.526, a set of historical temperature data is extracted as 40.62℃, 38.20℃, 36.43℃, 35.42℃, 34.75℃, 34.83℃, 35.10℃ and 35.67℃.

[0048] Step 6: Use a cubic spline smoothing curve along the depth direction to perform data analysis on the historical temperature data extracted in Step 5, and fit the regression equation for the historical temperature data as follows:

[0049] y(x) = -4773.0 × (x - 0.02) 3 +1029.4×(x-0.02) 2 +97.90×(x-0.02)+40.306

[0050] In the formula, y(·) is the internal temperature of the structural layer; x is the depth of the middle position of the structural layer; d is the depth of the middle position of the top layer of the asphalt pavement structure; A, B, C, and D are all fitting coefficients.

[0051] Step 7: In this embodiment, the depths of the middle positions of each structural layer of the asphalt pavement structure to be laid are 0.02m, 0.08m, 0.18m, 0.30m, and 0.38m, respectively. Substituting the middle depths of each structural layer of the asphalt pavement structure to be laid into the temperature history data regression equation determined in Step 6, the temperatures at the middle positions of each structural layer from top to bottom in the asphalt pavement structure to be laid are calculated to be 40.31℃, 36.11℃, 34.56℃, 35.78℃, and 34.65℃, respectively, thereby determining the internal temperature of each structural layer of the asphalt pavement structure to be laid.

[0052] Step 8: Based on the design scheme of the asphalt pavement structure to be paved, obtain the master curve of the dynamic modulus of the asphalt mixture used in the asphalt pavement structure to be paved. Combine the vehicle speed on the asphalt pavement structure to be paved to determine its load frequency. Substitute the internal temperature and load frequency of each structural layer of the asphalt pavement structure to be paved into the master curve of the dynamic modulus of the asphalt mixture used in the asphalt pavement structure to be paved. The dynamic modulus sampling data under this set of temperature data conditions are 1681.82 MPa, 4435.18 MPa, 4165.19 MPa, 3772.82 MPa, and 4213.89 MPa, respectively, thus completing the dynamic modulus sampling of the asphalt pavement structure to be paved.

[0053] Therefore, the method of the present invention overcomes the limitations of traditional single statistical sampling methods applied to the sampling of dynamic modulus of asphalt pavement structure. It fully considers the influence of diurnal temperature variation, seasonal changes, and depth on the dynamic modulus of asphalt pavement structure, and achieves accurate restoration of the mechanical characteristics of asphalt pavement structure under complex temperature conditions. It is fully combined with actual physical conditions, can capture the fluctuation of actual asphalt pavement performance parameters, and effectively ensures the reliability of asphalt pavement structure design.

[0054] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A sampling method for the dynamic modulus of asphalt pavement structure based on measured temperature field, characterized in that, Specifically, the following steps are included: Step 1: Obtain the design scheme of the asphalt pavement structure to be paved, use the long-term performance monitoring network of asphalt pavement to obtain the measured historical temperature field data of the existing asphalt pavement structure, extract the historical temperature values ​​at a specified depth inside the existing asphalt pavement structure according to the preset time points, obtain multiple sets of historical temperature field data, and construct a set of historical temperature field data. Step 2: Number each group of historical temperature data in the historical temperature field dataset; Step 3: Set the temperature range and reassign the historical temperature values ​​in the historical temperature data set to obtain the reassigned historical temperature data set. Step 4: Take two sets of historical temperature data with the same historical temperature value at all specified depths as the same set of historical temperature data. The historical temperature data in the same set have the same number. Count the frequency of the historical temperature data in the same set in the reassigned temperature field historical data set, update the number of each set of historical temperature data, and generate a cumulative probability distribution curve. Step 5: Randomly generate a random number in [0,1]. Based on the generated random number, perform inverse function sampling on the cumulative probability distribution curve to determine the number of the historical temperature data corresponding to the random number, and extract the historical temperature data corresponding to the random number. Step 6: Use cubic spline smoothing curves along the depth direction to perform data analysis on the historical temperature data extracted in Step 5, and fit the regression equation of the historical temperature data. Step 7: Determine the depth of the middle position of each structural layer based on the thickness of each structural layer of the asphalt pavement structure to be laid, obtain the middle depth of each structural layer of the asphalt pavement structure to be laid, and substitute it into the temperature history data regression equation determined in Step 6 to calculate the temperature at the middle position of each structural layer of the asphalt pavement structure to be laid, and obtain the internal temperature of each structural layer of the asphalt pavement structure to be laid. Step 8: Based on the design scheme of the asphalt pavement structure to be paved, obtain the master curve of the dynamic modulus of the asphalt mixture used in the asphalt pavement structure to be paved. Combined with the load frequency on the asphalt pavement structure to be paved, substitute the load frequency of the asphalt pavement structure to be paved and the internal temperature of each structural layer into the master curve of the dynamic modulus of the asphalt mixture used in the asphalt pavement structure to be paved, and obtain the dynamic modulus sampling data to complete the dynamic modulus sampling of the asphalt pavement structure to be paved. In step 3, temperature ranges are set with an interval of 5°C, and each historical temperature value in the historical temperature field data set is rounded down and reassigned within the corresponding temperature range. The regression equation for the historical temperature data is: In the formula, This refers to the internal temperature of the structural layer, in °C. This represents the depth of the middle position of the structural layer, in meters (m). The depth of the middle position of the topmost structural layer of the asphalt pavement structure, in meters; , , , All are fitting coefficients; The load frequency on the asphalt pavement structure to be paved is determined based on the vehicle speed on the asphalt pavement structure, resulting in: In the formula, The load frequency is expressed in Hz. The thickness of the asphalt layer in the asphalt pavement structure to be paved is expressed in meters (m). The speed of vehicles on the asphalt pavement structure to be paved is expressed in km / h.

2. The method for sampling the dynamic modulus of asphalt pavement structure based on the measured temperature field according to claim 1, characterized in that, The design scheme includes structural parameters and material parameters of the asphalt pavement structure to be paved. The structural parameters include the thickness of each structural layer in the asphalt pavement structure to be paved, and the material parameters include the material properties of each structural layer in the asphalt pavement structure to be paved.

3. The method for sampling the dynamic modulus of asphalt pavement structure based on the measured temperature field according to claim 1, characterized in that, In step 1, the historical temperature field data of the existing asphalt pavement structure for at least two years are obtained using the long-term performance monitoring network of asphalt pavement.

4. The method for sampling the dynamic modulus of asphalt pavement structure based on the measured temperature field according to claim 1, characterized in that, In step 5, the random number is generated using the random number generator in Matlab software.

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