A method for calculating reliability of fatigue life of asphalt pavement

Abstract

The application discloses a kind of calculation methods of asphalt pavement fatigue life reliability, it is related to road engineering technical field.The method of the present application obtains the measured temperature field historical data and traffic axle load data of active asphalt pavement structure using asphalt pavement long-term performance observation network, extracts measured temperature field historical data to construct temperature field historical data set, generates temperature cumulative probability distribution curve, then generates axle type cumulative probability density curve and axle load interval cumulative probability density curve using traffic axle load data, based on Monte Carlo simulation, inverse function sampling is carried out in each cumulative probability distribution curve to obtain the structural response of asphalt pavement structure to determine standard fatigue life each time simulation, the probability that standard fatigue life obtained by statistical Monte Carlo simulation is greater than the standard cumulative action times in design period is determined, to determine fatigue life reliability.The present application realizes the accurate estimation of asphalt pavement structure fatigue life, effectively guarantees the reliability of asphalt pavement structure design.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and specifically to a method for calculating the fatigue life reliability of asphalt pavement. Background Technology

[0002] Currently, in the design of asphalt pavement structures, the representative value of the material modulus at a fixed temperature is commonly used for fatigue life assessment, and traffic loads are uniformly converted into equivalent standard axle loads (ESAL). Furthermore, the assessment of fatigue failure reliability is mostly qualitative, substituting fatigue life results into predetermined reliability indices. This method ignores the random variations in temperature-load combinations experienced by asphalt pavements during actual service, resulting in highly idealized fatigue life values, which in turn affects the accuracy and adaptability of asphalt pavement structure design. Asphalt mixtures are typical temperature-sensitive materials, and their modulus and fatigue characteristics change significantly with ambient temperature and loading rate; simultaneously, the axle loads generated by different types of vehicles in traffic flow have considerable uncertainty. Therefore, the traditional method of combining representative values, equivalent loads, and uniform reliability in asphalt pavement structure design cannot accurately reflect the statistical distribution of asphalt pavement fatigue life, thus hindering the realization of high-reliability asphalt pavement structure design.

[0003] In contrast, the Monte Carlo simulation method for assessing the fatigue life of asphalt pavement structures can fully utilize measured temperature field data and traffic load spectra. By constructing multi-time-period and multi-combination service state models, it can randomly sample and fit the fatigue life to obtain the complete probability distribution curve of the pavement structure's fatigue life, and then conduct accurate fatigue reliability analysis based on this. However, at present, there is still a lack of a unified and systematic implementation path in the calculation method of asphalt pavement fatigue life reliability, resulting in relatively large calculation errors. Summary of the Invention

[0004] This invention aims to solve the above problems and provides a method for calculating the fatigue life reliability of asphalt pavement. This method can accurately reflect the statistical distribution law of fatigue life of asphalt pavement, realize the accurate acquisition of fatigue life reliability of asphalt pavement, improve the reliability of asphalt pavement structure design, and provide a basis for the formulation of high-reliability asphalt pavement structure design schemes.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for calculating the fatigue life reliability of asphalt pavement includes the following steps:

[0007] 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.

[0008] Step 2: After numbering each group of historical temperature data in the historical temperature field dataset, set temperature intervals and assign values, count the frequency of occurrence of the same group of historical temperature data, and generate a cumulative temperature probability distribution curve.

[0009] Step 3: Use the long-term performance monitoring network of asphalt pavement to obtain traffic axle load data of adjacent asphalt pavement structures, determine the cumulative probability and axle load spectrum of each axle type, and draw the cumulative probability density curve of axle type and the cumulative probability density curve of axle load interval.

[0010] Step 4: Perform Monte Carlo simulation to generate random numbers [0,1]. Perform inverse function sampling on the temperature cumulative probability distribution curve, extract the historical temperature data corresponding to the random numbers, fit the temperature regression equation, determine the internal temperature of each structural layer in the asphalt pavement structure, and calculate the dynamic modulus sampling data of each structural layer by combining the vehicle speed on the asphalt pavement structure and the dynamic modulus master curve of the asphalt mixture of each structural layer.

[0011] Step 5: Regenerate [0,1] random numbers, perform inverse function sampling on the axis type cumulative probability density curve, and extract the axis type corresponding to the random numbers;

[0012] Step 6: Regenerate random numbers in the range [0,1]. Perform inverse function sampling on the cumulative probability density curve of the axle load interval in step 5, extract the axle load interval corresponding to the random number, and take the midpoint of the interval as the traffic axle load P. ijx Calculate the strain at the bottom of the asphalt fatigue layer, determine the fatigue life of the asphalt pavement structure, and convert it into the standard fatigue life.

[0013] Step 7: Repeat steps 4 through 6 until the preset number of simulations is reached. Obtain the standard fatigue life determined by each Monte Carlo simulation. Based on the fact that the standard fatigue life in all Monte Carlo simulations is greater than the standard cumulative number of actions N within the design period... e The probability is used to determine the fatigue life reliability R.

[0014] Preferably, the design scheme includes the structural form of the asphalt pavement structure, the thickness of each structural layer, the design life, and the cumulative number of standard axle loads within the design life.

[0015] Preferably, the existing asphalt pavement structure is of the same type as the asphalt pavement structure to be paved, and 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 of asphalt pavement.

[0016] Preferably, the axle type includes single axle single tire, single axle dual tire, dual axle and triple axle.

[0017] Preferably, in step 3, the axial cumulative probability density curve is plotted as shown in formula (1):

[0018]

[0019] In the formula, i is the shaft type number; ALDF i Let NA be the cumulative probability density of the i-th axis type; i NA represents the total number of axes for the i-th axis type. t This represents the total number of axles for all vehicles.

[0020] Calculate the axle load spectrum for each axle type and plot the cumulative probability density curve of the axle load interval, as shown in formula (2):

[0021]

[0022] In the formula, ALDF ij NA represents the percentage of the i-th axle type in the j-th axle load range across all vehicles. ij This represents the total number of axles of type i in all vehicles within the j-th axle load range.

[0023] Preferably, in step 4, the internal temperature of the structural layer in the asphalt pavement structure is the temperature at the middle position of the structural layer.

[0024] Preferably, the preset number of simulations is at least 8000.

[0025] Preferably, the formula for converting fatigue life to standard fatigue life is:

[0026]

[0027] In the formula, N sfx N represents the standard fatigue life obtained from the x-th Monte Carlo simulation; fx C1 represents the fatigue life obtained from the x-th Monte Carlo simulation; C2 represents the coefficient of the first shaft group; P represents the coefficient of the second shaft group. ijx P represents the fatigue layer bottom strain obtained from the xth Monte Carlo simulation; s For designing axle loads.

[0028] Preferably, the fatigue life reliability R is:

[0029] R = P(N) sfx >N e (4)

[0030] In the formula, P is a statistical function used to count the number of times the standard fatigue life exceeds the standard cumulative action within the design period in the Monte Carlo simulation; N sfx The standard fatigue life determined for the xth Monte Carlo simulation.

[0031] The beneficial technical effects brought about by this invention are as follows:

[0032] This invention proposes a method for calculating the fatigue life reliability of asphalt pavement. It fully utilizes measured temperature field data and traffic axle load spectrum data of existing asphalt pavement structures from a long-term performance monitoring network. Through mathematical statistics, it obtains the cumulative probability density function of temperature, the cumulative probability density function of axle type, and the cumulative probability density function of axle load interval. Based on Monte Carlo simulation, it uses inverse function sampling of each discrete probability density function to obtain sampling data of asphalt pavement structures under different temperature and traffic load conditions. Structural response calculations are performed on each sampling combination and input into the fatigue life model of the asphalt pavement fatigue layer. The fatigue life of the asphalt pavement structure is determined and converted into a standard fatigue life. By statistically analyzing the probability that the standard fatigue life obtained from multiple Monte Carlo simulations is greater than the standard cumulative number of actions within the design period, the fatigue life reliability is determined.

[0033] This invention proposes a method for calculating the fatigue life reliability of asphalt pavement. Based on measured temperature field data and traffic load spectrum, the Monte Carlo method is used to simulate and determine the fatigue life reliability. The method fully considers the influence of factors such as ambient temperature, traffic load, and vehicle axle load on the fatigue life reliability of asphalt pavement structure. A simulation method that unifies temperature-load combined sampling strategy, standard fatigue life distribution acquisition, and fatigue life reliability is designed. This method achieves accurate acquisition of the distribution law of the true statistical situation of fatigue life of asphalt pavement structure, effectively ensuring the reliability and engineering applicability of asphalt pavement structure design. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the pavement structure of a newly built highway.

[0035] Figure 2 This is the cumulative probability distribution curve for temperature.

[0036] Figure 3 This is an axial cumulative probability density curve.

[0037] Figure 4 The figures show the cumulative probability density curves for axle load intervals; in the figure, (a) is the cumulative probability density curve for a single axle with a single tire, (b) is the cumulative probability density curve for a single axle with a double tire, (c) is the cumulative probability density curve for a double axle, and (d) is the cumulative probability density curve for a triple axle. Detailed Implementation

[0038] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0039] This embodiment takes a newly built expressway in a certain region as an example. The pavement structure design scheme of the newly built expressway 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.

[0040] The existing full-thickness pavement structure surrounding the newly built highway is equipped with a long-term performance monitoring network for asphalt pavement. This network allows for real-time measurement of the internal temperature of the existing asphalt pavement structure, enabling the acquisition of historical data on the measured temperature field of the existing asphalt pavement structure.

[0041] In this embodiment, based on the historical measured temperature field data of existing full-thickness pavement structures, and based on the measured temperature field, traffic load, and Monte Carlo simulation, a method for calculating the fatigue life reliability of asphalt pavement proposed in this invention is adopted, specifically including the following steps:

[0042] Step 1: Obtain the design scheme of the new highway pavement structure to be laid. In this embodiment, the design life of the new highway pavement structure is 15 years, and the cumulative number of standard axle loads N within the design life is... e It is 8.69×10 8 .

[0043] Historical temperature field data of existing asphalt pavement structures were obtained using the long-term performance monitoring network for asphalt pavement. Based on preset time points, historical temperature values ​​were extracted at depths of 0.02m, 0.04m, 0.07m, 0.1m, 0.15m, 0.2m, 0.25m, and 0.34m from the pavement surface within two years, resulting in a total of 17,520 sets of historical temperature field data, which were then used to construct a historical temperature field data set.

[0044] 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.

[0045] Table 1. Internal Temperature Statistics of Existing Full-Thickness Pavement Structures

[0046]

[0047]

[0048] Step 2: Number each group of historical temperature data in the historical temperature data set. The historical temperature data in the historical temperature data set is numbered from 1 to 17520. Set temperature intervals with an interval of 5℃ to determine the temperature interval to which each historical temperature value in the historical temperature data set belongs. Then, reassign each historical temperature value in the historical temperature data set by rounding down within the corresponding temperature interval. Table 2 shows the historical temperature data set after reassignment.

[0049] Table 2. Internal Temperature Statistics of Existing Full-Thickness Pavement Structures After Reassignment.

[0050]

[0051]

[0052] 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 are assigned the same ID. The frequency of occurrence of historical temperature data within the same set is calculated in the reassigned historical temperature data set. The IDs of each set of historical temperature data are then updated, and a cumulative temperature probability distribution curve is generated. Figure 2 As shown.

[0053] Step 3: Obtain traffic axle load data for adjacent asphalt pavement structures using the long-term performance monitoring network for asphalt pavements, calculate the cumulative probability of each axle type, and plot as shown below. Figure 3 The cumulative probability density curves for different axle types are shown. In this embodiment, four axle types are included: single-axle single-tire, single-axle dual-tire, dual-axle, and triple-axle. The cumulative probability density calculation formulas for each axle type are as follows:

[0054]

[0055] In the formula, i is the shaft type number, ALDF i Let NA be the cumulative probability density of the i-th axis type; i NA represents the total number of axes for the i-th axis type. t This represents the total number of axles for all vehicles.

[0056] Calculate the axle load spectrum for each axle type, and determine the cumulative probability density curve for each axle load interval, such as... Figure 4 As shown. The axle load spectrum is used to obtain the cumulative probability density of the axle type in each axle load interval, and the calculation formula is:

[0057]

[0058] In the formula, ALDF ij NA represents the percentage of the i-th axle type in the j-th axle load range across all vehicles. ij This represents the total number of axles of type i in all vehicles within the j-th axle load range.

[0059] Step 4: Perform Monte Carlo simulation. Using the random number generator in Matlab, generate a random number 0.398 within the range [0,1]. Perform inverse function sampling on the cumulative temperature probability distribution curve. Based on the historical temperature data corresponding to the random number 0.398, extract a set of historical temperature data corresponding to 0.398. This set of historical temperature data is determined to be 50.32℃, 47.28℃, 44.47℃, 42.20℃, 39.40℃, 37.98℃, 36.82℃, and 36.15℃. Use a cubic spline smoothing curve to fit along the depth direction to obtain the temperature regression equation:

[0060] y(x) = -576.56 × (x - 0.02) 3 +497.88×(x-0.02) 2 -152.08×(x-0.02)+52.92(8)

[0061] In the formula, y(·) represents the internal temperature of the structural layer in the asphalt pavement structure; x represents the depth of the middle position of the structural layer.

[0062] Substituting the intermediate depth of each structural layer of the asphalt pavement structure into the temperature regression equation, the internal temperatures of each structural layer in the asphalt pavement structure were determined to be 52.92℃, 45.46℃, 38.97℃, 36.71℃, and 35.79℃, respectively. Combined with the vehicle speed of 60km / h on the asphalt pavement structure in this embodiment, the load frequency was determined to be 10Hz. Substituting the internal temperature and load frequency of each structural layer of the asphalt pavement structure into the master curve of the dynamic modulus of the asphalt mixture used in each structural layer of the asphalt pavement, the dynamic modulus sampling data under this temperature data condition were obtained as 611.78MPa, 2764.66MPa, 4643.48MPa, 5154.77MPa, and 4114.72MPa, thus completing the dynamic modulus sampling of the asphalt pavement structure.

[0063] Step 5: Re-generate a random number 0.435 in [0,1] using the random number generator in Matlab software. Then, perform inverse function sampling on the cumulative probability density curve of each axis type to extract the axis type corresponding to the random number.

[0064] Taking the first Monte Carlo simulation as an example, random numbers were used to perform inverse function sampling on the cumulative probability density curve of the axle type. The axle type corresponding to the random number 0.435 was found to be a single axle with dual tires.

[0065] Step 6: Regenerate a random number 0.319 within the range [0,1] using the random number generator in Matlab. Perform inverse function sampling on the cumulative probability density curve of the axle load interval in Step 5 to extract the axle load interval corresponding to the random number, and take the midpoint of the axle load interval as the traffic axle load P. ijx Calculate the strain at the bottom of the asphalt fatigue layer, determine the fatigue life of the asphalt pavement structure, and convert it to the standard fatigue life.

[0066] Taking the first Monte Carlo simulation as an example, since the axle type determined by the inverse function sampling of random numbers in step 5 is single-axle dual tire, inverse function sampling is performed on the cumulative probability density curve of the single-axle dual tire axle load range. The axle load range corresponding to the random number 0.319 is 58.5KN to 63KN. The median value of the axle load range, 60.75KN, is taken as the traffic axle load P. ijx Based on the elastic layer theory, the strain at the bottom of the asphalt fatigue layer in this Monte Carlo simulation of the asphalt pavement structure was calculated to be 26.71 με. Substituting this strain into the fatigue life model of the asphalt pavement fatigue layer, the fatigue life of the asphalt pavement structure was calculated to be 1.04 × 10⁻⁶. 10 The fatigue life was then converted to the standard fatigue life, resulting in a standard fatigue life of 1.4165 × 10⁻⁶ for this Monte Carlo simulation. 9 .

[0067] Furthermore, the conversion formula between fatigue life and standard fatigue life in this embodiment is as follows:

[0068]

[0069] In the formula, N sfx N represents the standard fatigue life obtained from the x-th Monte Carlo simulation; fxC1 represents the fatigue life obtained from the xth Monte Carlo simulation; C1 is the coefficient of the first axle group. In this embodiment, when the distance between the front and rear axles of the vehicle is greater than 3m, the coefficient of the first axle group C1 is calculated based on a single axle. When the distance between the front and rear axles of the vehicle is not greater than 3m, the coefficient of the first axle group C1 for a double axle is 2.1, and the coefficient of the first axle group C1 for a triple axle is 3.2; C2 represents the coefficient of the second axle group, where the coefficient of the second axle group for a single wheel group is 4.5, and the coefficient of the second axle group for a double wheel group is 1.0; P ijx P represents the fatigue layer bottom strain obtained from the xth Monte Carlo simulation; s In this embodiment, axle load P is designed for axle load design. s It is 100 kN.

[0070] Step 7: Repeat steps 4 to 6 until the preset number of simulations reaches 8000. Obtain the standard fatigue life determined by each Monte Carlo simulation. Then, based on the standard fatigue life exceeding the standard cumulative number of actions N within the design life from all Monte Carlo simulations... e The probability is used to determine the fatigue life reliability R, where R is:

[0071] R = P(N) sfx >N e (4)

[0072] In the formula, P is a statistical function used to count the number of times the standard fatigue life exceeds the standard cumulative action within the design period in the Monte Carlo simulation; N sfx The standard fatigue life determined for the xth Monte Carlo simulation.

[0073] In this embodiment, after 5000 Monte Carlo simulations, a total of 4826 sets of data exceeded the standard cumulative number of actions N within the design period. e Thus, the fatigue life reliability of the asphalt pavement structure was determined to be 96.52%.

[0074] Therefore, the method of this invention, based on measured temperature field data and traffic load spectrum, uses the Monte Carlo method for simulation, fully considering the influence of factors such as ambient temperature, traffic load, and vehicle axle load on the fatigue life reliability of asphalt pavement structure. It unifies the temperature-load combined sampling strategy, the acquisition of standard fatigue life distribution, and the prediction of fatigue life reliability, achieving accurate prediction of fatigue life reliability of asphalt pavement, effectively improving the reliability of asphalt pavement structure design, and providing a basis for the formulation of high-reliability asphalt pavement structure design schemes.

[0075] 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 method for calculating the fatigue life reliability of asphalt pavement, characterized in that, Includes the following steps: 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: After numbering each group of historical temperature data in the historical temperature data set, set temperature intervals with an interval of 5℃ to determine the temperature interval to which each historical temperature value in the historical temperature data set belongs. Then, reassign each historical temperature value in the historical temperature data set by rounding down within the corresponding temperature interval. Two groups of historical temperature data with the same historical temperature value at all specified depths are regarded as the same group of historical temperature data. The historical temperature data in the same group have the same number. Count the frequency 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 the temperature cumulative probability distribution curve. Step 3: Use the long-term performance monitoring network of asphalt pavement to obtain traffic axle load data of adjacent asphalt pavement structures, determine the cumulative probability and axle load spectrum of each axle type, and draw the cumulative probability density curve of axle type and the cumulative probability density curve of axle load interval. Step 4: Perform Monte Carlo simulation to generate random numbers [0,1]. Perform inverse function sampling on the temperature cumulative probability distribution curve, extract the historical temperature data corresponding to the random numbers, fit the temperature regression equation, determine the internal temperature of each structural layer in the asphalt pavement structure, and calculate the dynamic modulus sampling data of each structural layer by combining the vehicle speed on the asphalt pavement structure and the dynamic modulus master curve of the asphalt mixture of each structural layer. Step 5: Regenerate [0,1] random numbers, perform inverse function sampling on the axis type cumulative probability density curve, and extract the axis type corresponding to the random numbers; Step 6: Regenerate random numbers in the range [0,1]. Perform inverse function sampling on the cumulative probability density curve of the axle load interval in step 5, extract the axle load interval corresponding to the random number, and take the midpoint of the interval as the traffic axle load. Calculate the strain at the bottom of the asphalt fatigue layer, determine the fatigue life of the asphalt pavement structure, and convert it into the standard fatigue life. Step 7: Repeat steps 4 through 6 until the preset number of simulations is reached, and obtain the standard fatigue life determined by each Monte Carlo simulation. The standard fatigue life in all Monte Carlo simulations must exceed the standard cumulative number of actions within the design life. The probability of fatigue life reliability is used to determine the fatigue life reliability. ; The formula for converting fatigue life to standard fatigue life is as follows: （3） In the formula, For the first Standard fatigue life obtained from Monte Carlo simulation; For the first Fatigue life obtained from Monte Carlo simulation; The coefficient of the first axis group. This is the coefficient for the second axis group; For the first The fatigue layer bottom strain obtained from the Monte Carlo simulation; For design axle load; fatigue life reliability for: （4） In the formula, This is a statistical function used to count the number of times the standard fatigue life exceeds the standard cumulative action within the design period in Monte Carlo simulations. For the first The standard fatigue life determined by Monte Carlo simulation.

2. The method for calculating the fatigue life reliability of asphalt pavement according to claim 1, characterized in that, The design scheme includes the structural form of the asphalt pavement structure, the thickness of each structural layer, the design life, and the cumulative number of standard axle loads within the design life.

3. The method for calculating the fatigue life reliability of asphalt pavement according to claim 1, characterized in that, The existing asphalt pavement structure is of the same type as the asphalt pavement structure to be laid, and 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 of asphalt pavement.

4. The method for calculating the fatigue life reliability of asphalt pavement according to claim 1, characterized in that, The axle types include single axle single tire, single axle dual tire, dual axle and triple axle.

5. The method for calculating the fatigue life reliability of asphalt pavement according to claim 4, characterized in that, In step 3, the axial cumulative probability density curve is plotted as shown in formula (1): （1） In the formula, For shaft type serial number; For the first The cumulative probability density of the axial type; For the first Total number of shafts for each type; This represents the total number of axles for all vehicles. Calculate the axle load spectrum for each axle type and plot the cumulative probability density curve of the axle load interval, as shown in formula (2): （2） In the formula, The first among all vehicles Type of shaft in the first Percentage of each axle load range; The first among all vehicles Type of shaft in the first The total number of axes in each axle weight range.

6. The method for calculating the fatigue life reliability of asphalt pavement according to claim 1, characterized in that, In step 4, the internal temperature of the structural layer in the asphalt pavement structure is the temperature at the middle position of the structural layer.

7. The method for calculating the fatigue life reliability of asphalt pavement according to claim 1, characterized in that, The preset number of simulations is at least 8000.

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