Assembly type airport pavement service life estimation method considering flatness of base layer

Abstract

The invention relates to a fabricated airport pavement life estimation method considering base course flatness, which comprises the following steps: establishing a base course top surface elevation data set under different flatness levels based on a discrete surface random generation method, and generating a base course finite element model according to the base course top surface elevation data set; building a pavement slab model, wherein the pavement slab model and the base layer finite element model under different flatness levels form an airfield pavement finite element model under different flatness levels; calculating the airfield pavement finite element models under different flatness levels to obtain a stress cloud picture; obtaining the maximum tensile stress of the plate bottom under different flatness levels according to the stress nephogram; and calculating the fatigue life according to the maximum tensile stress of the plate bottom under different flatness levels and a fatigue equation to obtain the fatigue life under different flatness levels. According to the method, the influence of the flatness of the pavement base layer can be reflected more truly, and the fatigue life of the pavement can be estimated more reasonably.

Description

Technical Field

[0001] The present application relates to the field of prefabricated airport pavement engineering, and in particular, to a method for estimating the life of a prefabricated airport pavement taking into account the flatness of the base layer. Background Art

[0002] Airport pavements are the most crucial component of airport infrastructure. Existing airport pavements are primarily cement concrete, accounting for approximately 90% of the total. Traditional cast-in-place cement concrete pavements require lengthy repair and maintenance after damage, making them difficult to maintain during non-stop airport construction. Recent rapid developments in prefabricated technology have enabled their application in airport pavement construction to meet the demands of non-stop flight operations. Prefabricated cement concrete pavements are manufactured in a prefabricated factory, transported to the construction site using transport equipment, and then installed and connected on a base layer, allowing for rapid deployment.

[0003] Since the maximum nominal particle size of cement-stabilized crushed stone base is generally 31.5mm, it is difficult to completely level the base due to its material properties and construction process. Unlike cast-in-place cement concrete pavements, when the base surface is uneven, there will be problems with poor fit between the prefabricated pavement panels and the base, or even gaps, which will lead to stress redistribution within the pavement panels, seriously affecting the bearing capacity and service life of the pavement structure. The "Technical Specifications for Construction of Earthwork and Pavement Base (Pad) Layers in Civil Airport Airfields" (MH / T 5014-2022) stipulates that the unevenness of the base layer shall not exceed 8mm. Therefore, the unevenness of the base surface is an objective reality. However, in the existing airport pavement structure design and finite element simulation, insufficient consideration is given to the flatness of the base layer, and the impact of the base layer flatness on the stress response of the prefabricated pavement and the slab thickness design is ignored. Currently, the simulation of flatness is only at the stage of mathematical modeling. The finite element simulation method still uses the traditional vehicle dynamic load coefficient method, which simply introduces the load magnification factor without changing the finite element solid model. It is unable to accurately simulate the flatness of the base surface and cannot reflect the changes in the pavement stress distribution, resulting in a large deviation between the simulation results and the actual results. Summary of the Invention

[0004] In order to overcome at least one shortcoming in the prior art, the present application provides a method for estimating the life of an assembled airport pavement taking into account the flatness of the base layer.

[0005] First, a method for estimating the life of a prefabricated airport pavement taking into account the flatness of the base layer is provided, including:

[0006] The base layer top surface elevation datasets with different flatness levels are established based on the discrete surface random generation method, and the base layer finite element model is generated according to the base layer top surface elevation datasets.

[0007] Establish a pavement panel model, which together with the base finite element model at different flatness levels forms a finite element model of the airport pavement at different flatness levels;

[0008] The finite element model of the airport pavement at different levels of smoothness is calculated to obtain stress cloud maps. Based on the stress cloud maps, the maximum tensile stress at the bottom of the slab at different levels of smoothness is obtained.

[0009] The fatigue life is calculated based on the maximum tensile stress of the plate bottom under different flatness levels and the fatigue equation, and the fatigue life under different flatness levels is obtained.

[0010] In one embodiment, a base layer top surface elevation dataset at different flatness levels is established based on a discrete surface random generation method, and a base layer finite element model is generated based on the base layer top surface elevation dataset, including:

[0011] The optimal values ​​of the number of control points and the number of interpolation times are determined by the within-group variance analysis based on the actual values ​​of the flatness standard deviation;

[0012] Determine multiple theoretical values ​​of flatness standard deviation; the theoretical values ​​of flatness standard deviation are used to reflect the flatness level;

[0013] For any theoretical value of the roughness standard deviation, based on the optimal value of the number of control points and the theoretical value of the roughness standard deviation, the elevation data of each control point under the theoretical value of the roughness standard deviation is obtained through the elevation probability density function;

[0014] Interpolate the elevation data of any two control points based on the optimal value of the interpolation times to obtain the elevation data of each node after interpolation;

[0015] After interpolation, the elevation data of each node is added to the actual thickness of the base layer to obtain the node elevation data taking thickness into account;

[0016] Convert node elevation data considering thickness into node coordinate data;

[0017] The node coordinate data is imported into Abaqus to generate the base finite element model under the theoretical value of flatness standard deviation.

[0018] In one embodiment, determining the optimal values ​​of the number of control points and the number of interpolation times based on the intra-group variance analysis of the actual value of the flatness standard deviation includes:

[0019] Set various working conditions with different numbers of control points and different interpolation times;

[0020] Under each working condition, the theoretical value of the flatness standard deviation is uniformly set, and the theoretical value of the flatness standard deviation is substituted into the elevation probability density function. The interval range of the elevation data of the control point is determined according to the elevation probability density function; according to the number of control points, multiple values ​​are randomly taken within the interval range to obtain the elevation data of each control point;

[0021] Perform the interpolation corresponding to the working condition on the elevation data of any two control points to obtain the elevation data of each node after interpolation; calculate the actual value of the flatness standard deviation based on the elevation data of each node after interpolation, completing the data collection process once; under each working condition, the data collection process is repeated multiple times, and the intra-group variance is calculated based on the actual values ​​of the flatness standard deviation obtained from multiple data collections;

[0022] The variances under each working condition are compared, and the number of control points and the number of interpolations in the working condition corresponding to the minimum variance are selected as the optimal values ​​of the number of control points and the number of interpolations.

[0023] In one embodiment, the interval range of the theoretical value of the flatness standard deviation is determined using the following formula:

[0024] σ * =0.5916IRI+0.013

[0025] Among them, σ * is the interval range to which the theoretical value of the flatness standard deviation belongs, and IRI is the International Roughness Index, which is an interval.

[0026] In one embodiment, the elevation probability density function is:

[0027]

[0028] Among them, PDF(z) is the elevation probability density function, z is the elevation data of the control point, and σ is the theoretical value of the flatness standard deviation.

[0029] In one embodiment, the actual value of the flatness standard deviation is calculated based on the interpolated elevation data of each node using the following formula:

[0030]

[0031] Among them, S a is the actual value of the flatness standard deviation, L x is the plate length along the x direction, L y is the length of the plate along the y direction, and z(x,y) is the elevation data of each node after interpolation.

[0032] In one embodiment, a pavement panel model is established. The pavement panel model and the base layer finite element model at different flatness levels form an airport pavement finite element model at different flatness levels, including:

[0033] For the finite element model of the base layer at any flatness level, a new surface layer model of a set size is created based on the component module in Abaqus; and eight surface layer models of the same size are copied; the nine surface layer models constitute the road panel model;

[0034] Move the pavement panel model to the corresponding position above the base layer finite element model, and set a joint between the two adjacent surface layer models; create a new dowel rod model and tie rod model, and arrange multiple tie rods or dowel rods at equal intervals along the length and width of the panel;

[0035] The flexural strength, elastic modulus and Poisson's ratio of the material are measured through indoor flexural strength tests and flexural elastic modulus tests, and the material properties of the road panel model are given;

[0036] The normal contact relationship between the pavement panel model and the base layer finite element model is defined as hard contact, the tangential contact is defined as Punish, the friction coefficient is defined as 0.6, and the interference factor is adjusted to eliminate interference. The contact relationship between the tie rod model, the dowel rod model, and the pavement panel model is defined as built-in. No solid model is established for the soil foundation, and only an elastic foundation is set at the bottom of the base layer finite element model.

[0037] Based on the load module, a three-axis two-wheel aircraft load is set at the most unfavorable load position on the edge of the middle surface layer model; constraints are set around the overall model composed of the base finite element model and the road panel model: U1=U2=0, the force transmission rod constraint is UR1=0, and the tie rod constraint is UR2=0, where U1 is the displacement along the x-axis, U2 is the displacement along the y-axis, UR1 is the rotational displacement in the x-axis direction, and UR2 is the rotational displacement in the y-axis direction;

[0038] Based on the mesh module, the mesh type of the surface layer model is set to quadratic hexahedron reduced integration; the mesh type of the tie rod model and the force transmission rod model is set to linear beam element; the mesh type of the base layer finite element model is set to linear hexahedron reduced integration.

[0039] In one embodiment, the fatigue life is calculated based on the maximum tensile stress of the plate bottom at different flatness levels and the fatigue equation, and the fatigue life at different flatness levels is obtained, including:

[0040] The stress ratio is calculated based on the maximum tensile stress at the bottom of the plate using the following formula:

[0041]

[0042] Where S is the stress ratio, σ max is the maximum tensile stress at the bottom of the plate, f f is the flexural strength;

[0043] The fatigue life is calculated based on the stress ratio and fatigue equation. The fatigue equation is:

[0044] S=1.16312-0.08241lgN

[0045] Where N is the fatigue life.

[0046] In a second aspect, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, it realizes the above-mentioned method for estimating the life of an assembled airport pavement taking into account the flatness of the base layer.

[0047] In a third aspect, a computer program product is provided, comprising a computer program / instruction. When the computer program / instruction is executed by a processor, the method for estimating the life of a prefabricated airport pavement taking into account the flatness of the base layer is implemented.

[0048] Compared with the prior art, this application has the following beneficial effects:

[0049] 1. The flatness of the base layer significantly affects the magnitude and distribution of pavement stress. Existing technologies simulate the effect of base layer flatness on pavement stress distribution under aircraft loads by assigning a dynamic load coefficient to the aircraft load. However, the base layer surface remains a completely flat model, and the base layer model is not modified, making it unable to accurately reflect the changes in pavement stress distribution caused by varying base layer flatness. This application, however, establishes a finite element model that takes into account the actual flatness of the base layer, achieving a realistic representation of base layer flatness and more intuitively reflecting the impact of changes in base layer surface unevenness on pavement stress distribution. This method better reflects the actual pavement conditions than the method that adds a dynamic load coefficient.

[0050] 2. Since the simulation methods in the prior art cannot reflect the changes in the local flatness of the pavement, it is impossible to study the impact of the flatness of one or several key areas on the pavement stress response separately. The present application can not only simulate the global flatness, but also set different flatness levels in certain local areas of focus. It can accurately study the sensitivity of the pavement stress response to different flatness conditions and different areas of the base layer, and thus accurately obtain the impact of the base layer flatness on the pavement.

[0051] In summary, this application can more realistically reflect the impact of the pavement base smoothness and can more reasonably estimate the fatigue life of the pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present application may be better understood by referring to the following description in conjunction with the accompanying drawings, which together with the following detailed description are incorporated into and form a part of this specification. In the drawings:

[0053] Figure 1 A flowchart of a method for estimating the life of a prefabricated airport pavement taking into account the flatness of the base layer is shown;

[0054] Figure 2 The original road surface base model without considering the flatness is shown;

[0055] Figure 3 A schematic diagram of the discretization of the top surface of the base model is shown;

[0056] Figure 4 A new pavement base model considering flatness is shown;

[0057] Figure 5 A new pavement structure model considering flatness is shown. DETAILED DESCRIPTION

[0058] Exemplary embodiments of the present application are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of actual embodiments are described in this specification. However, it should be understood that in the process of developing any such actual embodiment, many implementation-specific decisions may be made to achieve the developer's specific goals, and these decisions may vary from one implementation to another.

[0059] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, the accompanying drawings only show the device structure closely related to the solution according to the present application, while other details that are not closely related to the present application are omitted.

[0060] It should be understood that the present application is not limited to the described embodiments due to the following description with reference to the accompanying drawings. In this document, where feasible, the embodiments may be combined with each other, features between different embodiments may be replaced or borrowed, and one or more features may be omitted in one embodiment.

[0061] The method for estimating the life of an assembled airport pavement that takes into account the flatness of the base layer in this application first considers the actual flatness of the base layer and performs a finite element simulation of the assembled airport pavement. The flatness reflects the objectively existing changes in the unevenness and elevation of the road surface. Therefore, a finite element model that takes into account the actual flatness of the base layer can be established by randomly generating discrete surfaces. When modeling using the method of discretizing the model and randomly assigning elevation data, since random distribution is involved when assigning elevations during the model generation process, each generated model is an independent individual and is not completely consistent, and the actual value of the flatness standard deviation of the generated model and the theoretical value of the flatness standard deviation set during modeling are not exactly the same. When generating models multiple times under the same parameters, the smaller the variance of the actual values ​​of the standard deviations of the obtained sets of models, the higher the stability of the modeling method, and the more accurate the final plate bottom stress.

[0062] The present invention provides a method for estimating the life of an assembled airport pavement taking into account the flatness of the base layer. Figure 1A flowchart of a method for estimating the life of a prefabricated airport pavement considering the flatness of the base layer is shown. Figure 1 , the method mainly includes the following steps:

[0063] Step S1: establishing a base layer top surface elevation dataset at different flatness levels based on a discrete surface random generation method, and generating a base layer finite element model according to the base layer top surface elevation dataset.

[0064] Step S2: establishing a pavement panel model. The pavement panel model and the base layer finite element model at different flatness levels form the airport pavement finite element model at different flatness levels.

[0065] Step S3, calculating the finite element model of the airport pavement at different flatness levels to obtain a stress cloud map; and obtaining the maximum tensile stress of the plate bottom at different flatness levels based on the stress cloud map.

[0066] Step S4, calculate the fatigue life according to the maximum tensile stress of the bottom of the plate at different flatness levels and the fatigue equation, and obtain the fatigue life at different flatness levels

[0067] This embodiment establishes a finite element model that takes into account the actual flatness of the base layer, achieving a realistic representation of base layer flatness and more intuitively reflecting the impact of surface unevenness on the pavement stress distribution. Compared to the method of adding a dynamic load coefficient, it better reflects the actual pavement conditions. This application not only simulates global flatness but also allows for the individual setting of different flatness levels in certain key local areas. This allows for precise study of the sensitivity of the pavement stress response to different base layer flatness conditions and different regions, thereby accurately determining the impact of base layer flatness on the pavement.

[0068] Figure 2 The original road surface base model without considering the flatness is shown. Figure 3 shows a schematic diagram of the discretization of the top surface of the base model, Figure 4 A new road surface base model considering flatness is shown. Figure 5 A new pavement structure model considering flatness is shown.

[0069] In one embodiment, step S1, establishing a base layer top surface elevation dataset at different flatness levels based on a discrete surface random generation method, and generating a base layer finite element model based on the base layer top surface elevation dataset, includes:

[0070] Step S11 , determining the optimal values ​​of the number of control points and the number of interpolation times based on the intra-group variance analysis of the actual value of the flatness standard deviation.

[0071] Specifically, we first set up various operating conditions combining different numbers of control points and different interpolation orders. Here, the base layer of the airport pavement finite element model is 5m x 2.5m, and 28 operating conditions are established, combining nine different numbers of control points with six different interpolation orders. For example, in one operating condition, the number of control points is 17 and the interpolation order is 4.

[0072] Then, under each working condition, the theoretical value of the flatness standard deviation is uniformly set, and the theoretical value of the flatness standard deviation is substituted into the elevation probability density function. The interval range of the elevation data of the control point is determined according to the elevation probability density function; according to the number of control points, multiple values ​​are randomly taken within the interval range to obtain the elevation data of each control point;

[0073] Perform corresponding interpolation on the elevation data of any two control points to obtain the elevation data of each node after interpolation; calculate the actual value of the flatness standard deviation based on the elevation data of each node after interpolation to complete the data collection process; under each working condition, the data collection process is repeated multiple times, for example, 5 times, and the intra-group variance is calculated based on the actual values ​​of the flatness standard deviation obtained from multiple data collections; here, under the same working condition, the number of control points and the number of interpolation times are the same, and the theoretical value of the flatness standard deviation is the same for each data collection.

[0074] Specifically, the elevation probability density function is:

[0075]

[0076] Among them, PDF(z) is the elevation probability density function, z is the elevation data of the control point, and σ is the theoretical value of the flatness standard deviation.

[0077] The range of the theoretical value of the flatness standard deviation σ is determined by the following formula:

[0078] σ * =0.5916IRI+0.013

[0079] Among them, σ * is the interval range to which the theoretical value of the flatness standard deviation belongs, and IRI is the International Roughness Index, which is an interval.

[0080] The actual value of the flatness standard deviation is calculated using the following formula:

[0081]

[0082] Among them, S a is the actual value of the flatness standard deviation, L x is the plate length along the x direction, L y is the length of the plate along the y direction, and z(x,y) is the elevation data of each node after interpolation.

[0083] Then, the variances under each working condition are compared, and the number of control points and the number of interpolations in the working condition corresponding to the minimum variance are selected as the optimal values ​​of the number of control points and the number of interpolations.

[0084] In this embodiment, the optimal value of the number of control points is determined to be 17, and the optimal value of the number of interpolation times is determined to be 2.

[0085] Step S12, determining a plurality of theoretical values ​​of flatness standard deviation; the theoretical value of flatness standard deviation is used to reflect the level of flatness. Here, the plurality of theoretical values ​​of flatness standard deviation can be, for example, 9, belonging to the interval range σ * .

[0086] Step S13 , for any theoretical value of the roughness standard deviation, based on the optimal value of the number of control points and the theoretical value of the roughness standard deviation, obtain the elevation data of each control point under the theoretical value of the roughness standard deviation through the elevation probability density function.

[0087] Here, the control points are set according to the optimal number of control points, and the elevation data of each control point is determined. For a specific determination method, see step S11.

[0088] Step S14: interpolate the elevation data of any two control points based on the optimal value of the interpolation times to obtain the elevation data of each node after interpolation. Here, the elevation data of each node after interpolation constitutes the base layer top surface elevation data set.

[0089] Step S15: After interpolation, the elevation data of each node is added to the actual thickness of the base layer, for example, 0.3m, to obtain the node elevation data taking the thickness into consideration, and save it as a txt file.

[0090] Step S16: converting the node elevation data taking the thickness into consideration into node coordinate data.

[0091] Step S17: import the node coordinate data into Abaqus to generate a base layer finite element model under the theoretical value of the flatness standard deviation.

[0092] Here, a Python script is used to overwrite the node coordinate data in the txt file into the corresponding node unit information of the original inp file, save it as a new inp file and import it into Abaqus to generate an isolated mesh.

[0093] Then, using the geometry editing function of Abaqus, the six surfaces of the isolated mesh were patched to obtain a completely closed shell model; and the shell model was converted into a base solid model by selecting Create Solid - Create from Shell.

[0094] Next, open the base solid model and, using the Abaqus component module, create a new base model with dimensions of 15.016m × 7.516m × 0.3m. Select the Create Cut function, draw a 5m × 2.5m sketch in the center of the new base model, and cut through it entirely. Move the base model containing the flatness into the central slot of the generated base solid model, keeping the bottom flush. Use the Merge Instance function, select Merge Geometry, Delete Boundary, and perform Boolean operations to create a new base model containing the c-degree. Save it and obtain the base finite element model based on the theoretical value of the flatness standard deviation.

[0095] For other theoretical values ​​of the flatness standard deviation, steps S13 to S17 are repeated to obtain finite element models of the base layer at different flatness levels.

[0096] In one embodiment, step S2 is to establish a pavement panel model, wherein the pavement panel model and the base layer finite element model at different flatness levels form an airport pavement finite element model at different flatness levels, including:

[0097] Step S21: For the finite element model of the base layer at any flatness level, a new surface layer model of 5m×2.5m×0.2m is created based on the component module in Abaqus; eight surface layer models of the same size are copied; and the nine surface layer models constitute the road panel model;

[0098] Step S22: Move the pavement panel model to the corresponding position above the base layer finite element model, and set a 0.008m joint between two adjacent surface layer models; create a new dowel rod model with a diameter of 0.035m and a tie rod model with a diameter of 0.018m, and arrange 6 tie rods or dowel rods at equal intervals along the length and width of the panel;

[0099] Step S23, measuring the flexural strength, elastic modulus and Poisson's ratio of the material through indoor flexural strength test and flexural elastic modulus test, and assigning material properties to the road panel model;

[0100] Step S24: Define the normal contact relationship between the road panel model and the base finite element model as hard contact, the tangential contact relationship as Punish, the friction coefficient as 0.6, and adjust to eliminate interference. The contact relationship between the tie rod model, the dowel rod model, and the road panel model is built-in. No solid model is established for the soil foundation, and only an elastic foundation is set at the bottom of the base finite element model.

[0101] Step S25: Based on the load module, a three-axle, two-wheel B777-300ER aircraft load is set at the most unfavorable load position on the edge of the middle surface layer model; constraints are set around the overall model composed of the base layer finite element model and the road panel model: U1=U2=0, the dowel rod constraint is UR1=0, and the tie rod constraint is UR2=0, where U1 is the displacement along the x-axis, U2 is the displacement along the y-axis, UR1 is the rotational displacement along the x-axis, and UR2 is the rotational displacement along the y-axis.

[0102] In step S26, based on the mesh module, the mesh type of the surface layer model is set to quadratic hexahedron reduced integration C3D20R, and the mesh of the loading area is refined to ensure calculation accuracy; the mesh type of the tie rod model and the force transmission rod model is set to linear beam element B31; the mesh type of the base layer finite element model is set to linear hexahedron reduced integration C3D8R.

[0103] In one embodiment, step S4, calculating fatigue life according to the maximum tensile stress of the plate bottom at different flatness levels to obtain fatigue life at different flatness levels, includes:

[0104] The stress ratio is calculated based on the maximum tensile stress at the bottom of the plate using the following formula:

[0105]

[0106] Where S is the stress ratio, σ max is the maximum tensile stress at the bottom of the plate, f f is the flexural strength;

[0107] The fatigue life is calculated based on the stress ratio and fatigue equation. The fatigue equation is:

[0108] S=1.16312-0.08241lgN

[0109] Where N is the fatigue life.

[0110] The above formula is determined as follows:

[0111] Small beam specimens with a size of 550×150×150mm were formed indoors, and flexural tensile strength fatigue tests were carried out using a UTM-250 electro-hydraulic servo fatigue testing machine. Equal-amplitude sinusoidal waves were used for loading, and the loading frequency was 10Hz. The fatigue life data of the specimens at three stress levels of 0.7, 0.8 and 0.9 were obtained, thereby establishing the above fatigue equation.

[0112] Comparative Example:

[0113] Step 1: Establish an ordinary cement concrete road slab model, including:

[0114] In step 1.1, create a new Abaqus finite element file and switch to the component module. Select a new cement-stabilized gravel base model with dimensions of 15.016m × 7.516m × 0.03m. Create a new surface layer model with dimensions of 5m × 2.5m × 0.2m and copy eight copies of the same surface layer model. These nine surface layer models constitute the pavement panel model. Move the newly created pavement panel model to the corresponding position above the base layer model, and set a 0.008m joint between two adjacent surface layer models. Create a new dowel rod model with a diameter of 0.035m and a tie rod model with a diameter of 0.018m. Arrange six tie rods or dowel rods at equal intervals along the length and width of the panel.

[0115] Step 1.2: Determine the flexural strength, elastic modulus, and Poisson's ratio of the material through indoor flexural strength tests and flexural elastic modulus tests, and assign material properties to the road panel model.

[0116] In step 1.3, define the normal contact relationship between the surface layer model and the base layer model as hard contact, the tangential contact as Punish, and the friction coefficient as 0.6; the contact relationship between the tie rods, force transfer rods and the surface layer as built-in; no solid model is established for the soil base, and only an elastic foundation is set at the bottom of the base layer model instead.

[0117] In step 1.4, switch to the load module and set the three-axle, two-wheel B777-300ER aircraft load at the most unfavorable load position on the edge of the middle surface model; set the constraints U1=U2=0, the force transmission rod UR1=0, and the tension rod UR2=0 around the model.

[0118] In step 1.5, switch to the mesh module. Set the mesh type of both the surface layer model and the base layer model to quadratic hexahedron reduced integration C3D20R. Refine the mesh in the loading area to ensure calculation accuracy. Set the mesh type of the tie rod and dowel rod to linear beam element B31.

[0119] Step 2: Model Analysis

[0120] After submitting the assignment and analyzing the output stress cloud map, we found that the maximum tensile stress at the bottom of the plate is 2.958 MPa, and the maximum tensile stress occurs on the long side.

[0121] Step 3: Estimating fatigue life

[0122] The maximum tensile stress at the bottom of the slab is substituted into the fatigue equation of ordinary concrete pavement, and the fatigue life of the pavement panel under this working condition is calculated to be 11.908 million times.

[0123] The finite element model of the airport pavement established in this application method, which takes into account the flatness of the base layer, shows a maximum tensile stress of 3.925 MPa and a fatigue life of 59,600 cycles. Compared with the maximum tensile stress of 2.958 MPa and a fatigue life of 11.908 million cycles in the control group, the tensile stress at the bottom of the pavement increased by 32.69% and the fatigue life decreased by nearly 200 times. This shows that the flatness of the base layer has a significant impact on the stress response and fatigue life of the pavement. The method of this application can accurately establish a base layer model at various flatness levels, thereby more reasonably analyzing and estimating the stress response and fatigue life of the prefabricated pavement under the actual state.

[0124] An embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for estimating the life of an assembled airport pavement taking into account the flatness of the base layer is implemented.

[0125] An embodiment of the present application provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the above-mentioned method for estimating the life of an assembled airport pavement considering the flatness of the base layer.

[0126] In summary, this application has the following technical effects:

[0127] 1. The simulation method in the prior art can only add the dynamic load coefficient to the load, and does not modify the base model, and cannot accurately reflect the changes in the pavement stress distribution caused by the different flatness of the base. However, the present application can establish a finite element model that takes into account the actual flatness of the base, achieve a true expression of the base flatness, and more intuitively reflect the impact of the unevenness changes on the pavement stress distribution. Compared with the method of adding the dynamic load coefficient, it is more in line with the actual condition of the pavement.

[0128] 2. Since the simulation methods in the prior art cannot reflect the changes in the local flatness of the pavement, it is impossible to study the impact of the flatness of one or several key areas on the pavement stress response separately. The present application can not only simulate the global flatness, but also set different flatness levels in certain local areas of focus. It can accurately study the sensitivity of the pavement stress response to different flatness conditions and different areas of the base layer, and thus accurately obtain the impact of the base layer flatness on the pavement.

[0129] The above descriptions are merely examples of various embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be readily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for estimating the life of an assembled airport pavement taking into account the flatness of the base layer, characterized in that: include: Establishing a base layer top surface elevation dataset at different flatness levels based on a discrete surface random generation method, and generating a base layer finite element model according to the base layer top surface elevation dataset; Establishing a pavement panel model, wherein the pavement panel model and the base layer finite element model at different flatness levels form an airport pavement finite element model at different flatness levels; Calculating the finite element model of the airport pavement at different flatness levels to obtain a stress cloud map; obtaining the maximum tensile stress of the plate bottom at different flatness levels based on the stress cloud map; The fatigue life is calculated according to the maximum tensile stress of the plate bottom under the different flatness levels and the fatigue equation, thereby obtaining the fatigue life under the different flatness levels.

2. The method according to claim 1, wherein in, A base layer top surface elevation dataset at different flatness levels is established based on a discrete surface random generation method, and a base layer finite element model is generated according to the base layer top surface elevation dataset, including: The optimal values ​​of the number of control points and the number of interpolation times are determined by the within-group variance analysis based on the actual values ​​of the flatness standard deviation; Determining a plurality of theoretical values ​​of flatness standard deviations; wherein the theoretical values ​​of flatness standard deviations are used to reflect the level of flatness; For any theoretical value of the flatness standard deviation, based on the optimal value of the number of control points and the theoretical value of the flatness standard deviation, the elevation data of each control point under the theoretical value of the flatness standard deviation is obtained through the elevation probability density function; Interpolating the elevation data of any two control points based on the optimal value of the interpolation number to obtain the elevation data of each node after interpolation; The elevation data of each node after the interpolation is added to the actual thickness of the base layer to obtain the node elevation data taking the thickness into consideration; Converting the thickness-considered node elevation data into node coordinate data; The node coordinate data is imported into Abaqus to generate a base layer finite element model under the theoretical value of the flatness standard deviation.

3. The method according to claim 2, wherein in, The optimal values ​​of the number of control points and interpolation times are determined by the within-group variance analysis based on the actual values ​​of the flatness standard deviation, including: Set various working conditions with different numbers of control points and different interpolation times; Under each working condition, a uniform theoretical value of the flatness standard deviation is set, and the theoretical value of the flatness standard deviation is substituted into the elevation probability density function. The interval range of the elevation data of the control point is determined according to the elevation probability density function; according to the number of control points, multiple values ​​are randomly taken within the interval range to obtain the elevation data of each control point; Performing a secondary interpolation on the elevation data of any two control points corresponding to the working condition to obtain the elevation data of each node after interpolation; calculating the actual value of the flatness standard deviation based on the elevation data of each node after interpolation to complete a data collection process; under each working condition, the data collection process is repeated multiple times, and the intra-group variance is calculated based on the actual values ​​of the flatness standard deviation obtained from the multiple data collections; The variances under each working condition are compared, and the number of control points and the number of interpolations in the working condition corresponding to the minimum variance are selected as the optimal values ​​of the number of control points and the number of interpolations.

4. The method according to claim 3, wherein The interval range to which the theoretical value of the flatness standard deviation belongs is determined using the following formula: s * =0.5916IRI+0.013 Among them, σ * is the interval range to which the theoretical value of the flatness standard deviation belongs, and IRI is the International Roughness Index, which is an interval.

5. The method according to claim 3, wherein in, The elevation probability density function is: Among them, PDF(z) is the elevation probability density function, z is the elevation data of the control point, and σ is the theoretical value of the flatness standard deviation.

6. The method according to claim 3, wherein in, The actual value of the flatness standard deviation is calculated based on the elevation data of each node after interpolation using the following formula: Among them, S a is the actual value of the flatness standard deviation, L x is the plate length along the x direction, L y is the length of the plate along the y direction, and z(x,y) is the elevation data of each node after interpolation.

7. The method according to claim 1, wherein in, A pavement panel model is established. The pavement panel model and the base layer finite element model at different flatness levels form an airport pavement finite element model at different flatness levels, including: For the finite element model of the base layer at any flatness level, a new surface layer model of a set size is created based on the component module in Abaqus; and eight surface layer models of the same size are copied; the nine surface layer models constitute the road panel model; The pavement panel model is moved to a corresponding position above the base layer finite element model, and a joint is set between two adjacent surface layer models; a new dowel rod model and a tie rod model are newly created, and multiple tie rods or dowel rods are arranged at equal intervals along the length and width of the panel; The flexural strength, elastic modulus and Poisson's ratio of the material are measured through indoor flexural strength tests and flexural elastic modulus tests, and the material properties of the road panel model are given; The normal contact relationship between the pavement panel model and the base layer finite element model is defined as hard contact, the tangential contact is defined as Punish, the friction coefficient is defined as 0.6, and the interference factor is adjusted to eliminate interference. The contact relationship between the tie rod model, the dowel rod model, and the pavement panel model is defined as built-in. No solid model is established for the soil foundation, and only an elastic foundation is set at the bottom of the base layer finite element model. Based on the load module, a three-axis two-wheel aircraft load is set at the most unfavorable load position on the edge of the middle surface layer model; constraints are set around the overall model composed of the base finite element model and the road panel model: U1=U2=0, the force transmission rod constraint is UR1=0, and the tie rod constraint is UR2=0, where U1 is the displacement along the x-axis, U2 is the displacement along the y-axis, UR1 is the rotational displacement in the x-axis direction, and UR2 is the rotational displacement in the y-axis direction; Based on the mesh module, the mesh type of the surface layer model is set to quadratic hexahedron reduced integration; the mesh type of the tie rod model and the force transmission rod model is set to linear beam element; the mesh type of the base layer finite element model is set to linear hexahedron reduced integration.

8. The method according to claim 1, wherein The fatigue life is calculated based on the maximum tensile stress of the plate bottom under different flatness levels and the fatigue equation, and the fatigue life under different flatness levels is obtained, including: The stress ratio is calculated based on the maximum tensile stress at the bottom of the plate using the following formula: Where S is the stress ratio, σ max is the maximum tensile stress at the bottom of the plate, f f is the flexural strength; The fatigue life is calculated based on the stress ratio and the fatigue equation, which is: S=1.16312-0.08241lgN Where N is the fatigue life.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method for estimating the life of an assembled airport pavement taking into account the flatness of the base layer as described in any one of claims 1-8.

10. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements the method for estimating the life of an assembled airport pavement taking into account the flatness of the base layer as described in any one of claims 1-8.

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