Reliability analysis methods, apparatus, and computer equipment for cement pavement

Abstract

This application relates to a method, apparatus, and computer equipment for reliability analysis of cement pavement. The method includes: acquiring mechanical property data points of cement concrete and pavement structure data of the cement pavement; constructing a variability numerical model of the cement concrete based on the mechanical property data points; constructing a pavement structure model of the cement pavement based on the pavement structure data; performing performance assignment processing on the pavement structure model based on the variability numerical model to generate mechanical response distribution data for each pavement unit; identifying the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab based on the mechanical response distribution data of each pavement unit; and identifying the reliability analysis result of the cement pavement based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab through a reliability analysis strategy. This method can improve the accuracy of PCC pavement analysis.

Description

Technical Field

[0001] This application relates to the field of road engineering technology, and in particular to a reliability analysis method, apparatus and computer equipment for cement pavement. Background Technology

[0002] Portland cement concrete (PCC) pavement has been widely used in the construction of critical infrastructure such as airport runways, highways, and docks due to its significant advantages, including high stiffness, strong durability, and relatively low life-cycle cost. However, since most of its surface is directly exposed to the natural environment, these rigid pavements are inevitably subjected to the combined effects of moving vehicle loads and environmental weathering loads during use. Under such complex loading conditions, PCC pavement is prone to tensile stress on the underside of the slab due to vehicle loads, leading to fatigue damage. Furthermore, the inherent uncertainties of the pavement structure itself can affect its damage process, thereby impacting the analysis of pavement structural performance and lifespan. Therefore, improving the accuracy of pavement structural performance and lifespan analysis is a current research focus.

[0003] Traditional methods for analyzing the performance and lifespan of pavement structures primarily rely on a system platform developed using Python, a pre-processing module based on ABAQUS software, a random field simulation module based on MATLAB software, and a finite element analysis module based on ABAQUS software. These modules automatically call upon the system for one-click batch processing and analysis. However, the numerical models of cement pavements using this approach do not consider the spatial variability of materials. Currently, they either treat the entire panel as a whole and use the same mechanical parameters, or they assume that the mechanical properties of different points in the concrete slab satisfy the same probability distribution, resulting in low accuracy in the analysis of PCC pavements. Summary of the Invention

[0004] Therefore, it is necessary to provide a reliability analysis method, apparatus, computer equipment, computer-readable storage medium, and computer program product for cement pavement in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a reliability analysis method for cement pavement, including:

[0006] Acquire the mechanical property data points of cement concrete and the pavement structure data of cement pavement, and construct the variability numerical model of cement concrete based on the mechanical property data points.

[0007] Based on the pavement structure data, a pavement structure model of the cement pavement is constructed, and based on the variability numerical model, the pavement structure model is subjected to performance assignment processing to generate mechanical response distribution data of each pavement unit.

[0008] Based on the mechanical response distribution data of each pavement panel unit, the probability distribution information of the maximum tensile stress at the bottom of the cement pavement is identified, and based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement, the reliability analysis result of the cement pavement is identified through a reliability analysis strategy.

[0009] Optionally, constructing a numerical model of the variability of the cement concrete based on each of the mechanical property data points includes:

[0010] The mechanical performance data points are grouped to obtain data pairs, and the distance between the two mechanical performance data points in each data pair is identified.

[0011] Based on the distance value of each data pair, calculate the dissimilarity value between the two mechanical performance data points in each data pair, and based on the dissimilarity value between the two mechanical performance data points in each data pair and the mechanical performance value data between the two mechanical performance data points in each data pair, calculate the semi-variable function value between the two mechanical performance data points in each data pair.

[0012] Based on the distance between two mechanical performance data points in each data pair and the semi-variable function value between two mechanical performance data points in each data pair, the numerical relationship between the semi-variable function value and the distance value is identified.

[0013] Based on the mechanical property data values ​​of each of the aforementioned mechanical property data points, the variability parameters of the cement concrete material are identified through a statistical identification strategy. Based on the variability parameters of the cement concrete material and the numerical relationship between the semivariable function value and the distance value, a variability numerical model of cement concrete is constructed.

[0014] Optionally, constructing the pavement structure model of the cement pavement based on the pavement structure data includes:

[0015] Obtain the current ambient temperature data of the cement pavement and identify the temperature gradient data corresponding to the current ambient temperature data;

[0016] The road surface structure data is broken down into substructure data of each pavement unit and connection structure data between each pavement unit;

[0017] Based on the substructure data of each of the pavement units, a finite element model of each of the pavement units is constructed, and based on the connection structure data between each of the pavement units, a connection structure model corresponding to each connection structure data is fitted.

[0018] Based on the finite element models of each pavement panel unit, the connection structure models corresponding to each connection structure data, and the temperature gradient data, a pavement structure model of the cement pavement is constructed.

[0019] Optionally, the step of performing performance assignment processing on the pavement structure model based on the variability numerical model to generate mechanical response distribution data for each pavement unit includes:

[0020] Identify the spatial position of the center point of each pavement unit in the road structure model, and calculate the spatial distance between each pavement unit based on the spatial position of the center point of each pavement unit;

[0021] Based on the spatial distance between each of the pavement panels and the finite element model of each of the pavement panels, the covariance matrix of each of the pavement panels is generated;

[0022] Based on the variability numerical model and the covariance matrix, a three-dimensional mechanical property matrix is ​​constructed for each of the plywood units;

[0023] The three-dimensional mechanical property matrix of each of the plywood units is used to identify the discrete distribution data of the mechanical properties of each of the plywood units, and the mechanical response distribution data of each of the plywood units is calculated based on the discrete distribution data of the mechanical properties.

[0024] Optionally, identifying the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab based on the mechanical response distribution data of each of the pavement slab units includes:

[0025] For each plywood unit, based on the mechanical response distribution data of the plywood unit, the tensile stress value of each frame of each bottom node of the plywood unit is extracted;

[0026] Based on the tensile stress values ​​of each bottom node in each frame, identify the maximum stress value of each bottom node;

[0027] Based on the maximum stress value of each bottom node, identify the maximum unit stress value of the plywood unit, and return to perform the step of extracting the tensile stress value of each frame of each bottom node of the plywood unit based on the mechanical response distribution data of the plywood unit, until the preset iteration condition is met, and obtain the maximum unit stress value of each unit of the plywood unit.

[0028] Based on the maximum stress value of each unit of the pavement panel, the sub-probability distribution information of the maximum tensile stress of the pavement panel is obtained by fitting, and the sub-probability distribution information of the maximum tensile stress of all pavement panel units is used as the probability distribution information of the maximum tensile stress at the bottom of the cement pavement.

[0029] Optionally, the reliability analysis results of the cement pavement are identified using a reliability analysis strategy based on the probability distribution information of the maximum tensile stress at the bottom of the pavement slab, including:

[0030] Based on the sub-probability distribution information of the maximum tensile stress of each of the aforementioned panel units, the reliability evaluation results of each of the aforementioned panel units are identified through a panel reliability evaluation strategy.

[0031] Based on the reliability evaluation results of each pavement panel unit and the connection relationship between each pavement panel unit, the reliability analysis results of the cement pavement are identified through a cement pavement reliability analysis strategy.

[0032] Secondly, this application also provides a reliability analysis device for cement pavement, comprising:

[0033] The acquisition module is used to acquire various mechanical property data points of cement concrete and pavement structure data of cement pavement, and to construct a numerical model of the variability of cement concrete based on each of the mechanical property data points.

[0034] The generation module is used to construct a pavement structure model of the cement pavement based on the pavement structure data, and to perform performance assignment processing on the pavement structure model based on the variability numerical model to generate mechanical response distribution data of each pavement unit.

[0035] The identification module is used to identify the probability distribution information of the maximum tensile stress at the bottom of the cement pavement based on the mechanical response distribution data of each of the pavement slab units, and to identify the reliability analysis results of the cement pavement based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement through a reliability analysis strategy.

[0036] Optionally, the acquisition module is specifically used for:

[0037] The mechanical performance data points are grouped to obtain data pairs, and the distance between the two mechanical performance data points in each data pair is identified.

[0038] Based on the distance value of each data pair, calculate the dissimilarity value between the two mechanical performance data points in each data pair, and based on the dissimilarity value between the two mechanical performance data points in each data pair and the mechanical performance value data between the two mechanical performance data points in each data pair, calculate the semi-variable function value between the two mechanical performance data points in each data pair.

[0039] Based on the distance between two mechanical performance data points in each data pair and the semi-variable function value between two mechanical performance data points in each data pair, the numerical relationship between the semi-variable function value and the distance value is identified.

[0040] Based on the mechanical property data values ​​of each of the aforementioned mechanical property data points, the variability parameters of the cement concrete material are identified through a statistical identification strategy. Based on the variability parameters of the cement concrete material and the numerical relationship between the semivariable function value and the distance value, a variability numerical model of cement concrete is constructed.

[0041] Optionally, the generation module is specifically used for:

[0042] Obtain the current ambient temperature data of the cement pavement and identify the temperature gradient data corresponding to the current ambient temperature data;

[0043] The road surface structure data is broken down into substructure data of each pavement unit and connection structure data between each pavement unit;

[0044] Based on the substructure data of each of the pavement units, a finite element model of each of the pavement units is constructed, and based on the connection structure data between each of the pavement units, a connection structure model corresponding to each connection structure data is fitted.

[0045] Based on the finite element models of each pavement panel unit, the connection structure models corresponding to each connection structure data, and the temperature gradient data, a pavement structure model of the cement pavement is constructed.

[0046] Optionally, the generation module is specifically used for:

[0047] Identify the spatial position of the center point of each pavement unit in the road structure model, and calculate the spatial distance between each pavement unit based on the spatial position of the center point of each pavement unit;

[0048] Based on the spatial distance between each of the pavement panels and the finite element model of each of the pavement panels, the covariance matrix of each of the pavement panels is generated;

[0049] Based on the variability numerical model and the covariance matrix, a three-dimensional mechanical property matrix is ​​constructed for each of the plywood units;

[0050] The three-dimensional mechanical property matrix of each of the plywood units is used to identify the discrete distribution data of the mechanical properties of each of the plywood units, and the mechanical response distribution data of each of the plywood units is calculated based on the discrete distribution data of the mechanical properties.

[0051] Optionally, the identification module is specifically used for:

[0052] For each plywood unit, based on the mechanical response distribution data of the plywood unit, the tensile stress value of each frame of each bottom node of the plywood unit is extracted;

[0053] Based on the tensile stress values ​​of each bottom node in each frame, identify the maximum stress value of each bottom node;

[0054] Based on the maximum stress value of each bottom node, identify the maximum unit stress value of the plywood unit, and return to perform the step of extracting the tensile stress value of each frame of each bottom node of the plywood unit based on the mechanical response distribution data of the plywood unit, until the preset iteration condition is met, and obtain the maximum unit stress value of each unit of the plywood unit.

[0055] Based on the maximum stress value of each unit of the pavement panel, the sub-probability distribution information of the maximum tensile stress of the pavement panel is obtained by fitting, and the sub-probability distribution information of the maximum tensile stress of all pavement panel units is used as the probability distribution information of the maximum tensile stress at the bottom of the cement pavement.

[0056] Optionally, the identification module is specifically used for:

[0057] Based on the sub-probability distribution information of the maximum tensile stress of each of the aforementioned panel units, the reliability evaluation results of each of the aforementioned panel units are identified through a panel reliability evaluation strategy.

[0058] Based on the reliability evaluation results of each pavement panel unit and the connection relationship between each pavement panel unit, the reliability analysis results of the cement pavement are identified through a cement pavement reliability analysis strategy.

[0059] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0060] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0061] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0062] The aforementioned reliability analysis method, apparatus, and computer equipment for cement pavement acquire various mechanical property data points of cement concrete and pavement structure data. Based on these mechanical property data points, a variability numerical model of the cement concrete is constructed. Based on the pavement structure data, a pavement structure model of the cement pavement is constructed. Based on the variability numerical model, performance values ​​are assigned to the pavement structure model to generate mechanical response distribution data for each pavement unit. Based on the mechanical response distribution data of each pavement unit, the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab is identified. Based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab, a reliability analysis strategy is used to identify the reliability analysis results of the cement pavement. This scheme analyzes the mechanical response distribution data of each pavement unit of the cement pavement by incorporating the spatial variability information of the cement pavement slab material properties into the pavement structure model of the cement pavement. This effectively improves the comprehensiveness and accuracy of the analysis of cement pavement, avoiding the limitations and high error rate of considering cement pavement as a whole panel. Then, the terminal further analyzes and identifies the probability distribution information of the maximum tensile stress at the bottom of the cement pavement panel by paving each panel, thereby identifying the reliability analysis results of the cement pavement. Structurally, it is divided into multiple paving panels, and in detail, by combining the spatial variability information of cement concrete, a sub-node model of the cement pavement is constructed. This makes the identification of the reliability analysis results of the cement pavement more in line with the actual situation, thereby improving the accuracy of the analysis of PCC pavement. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating a reliability analysis method for cement pavement in one embodiment;

[0065] Figure 2 This is a schematic diagram of a road structure model in one embodiment;

[0066] Figure 3 This is a schematic diagram illustrating the distribution of mechanical response results for each plywood panel in one embodiment.

[0067] Figure 4 This is a flowchart illustrating an example of reliability analysis for a cement pavement in one embodiment;

[0068] Figure 5This is a structural block diagram of a reliability analysis device for cement pavement in one embodiment;

[0069] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0071] The reliability analysis method for cement pavement provided in this application embodiment can be applied to the application environment of cement pavement reliability analysis. This method can be applied to a terminal, a server, or a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. The terminal analyzes the mechanical response distribution data of each pavement unit by incorporating the spatial variability information of the cement pavement paving material properties into the pavement structure model. This effectively improves the comprehensiveness and accuracy of the cement pavement analysis, avoiding the limitations and high errors of considering the cement pavement as a whole paving panel. Then, the terminal analyzes and identifies the probability distribution information of the maximum tensile stress at the bottom of each pavement panel, thereby identifying the reliability analysis results of the cement pavement. Structurally, it is divided into multiple pavement panels; in detail, by combining the spatial variability information of cement concrete, a sub-node model of the cement pavement is constructed, making the identification of the cement pavement reliability analysis results more consistent with the actual situation, thus improving the accuracy of PCC pavement analysis.

[0072] In one exemplary embodiment, such as Figure 1 As shown, a reliability analysis method for cement pavement is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein:

[0073] Step S101: Obtain the mechanical property data points of cement concrete and the pavement structure data of cement pavement, and construct a numerical model of the variability of cement concrete based on the mechanical property data points.

[0074] In this embodiment, the terminal performs multi-point detection on the mechanical properties of cement concrete to obtain the mechanical property data of cement concrete at various locations on the cement pavement, and uses the mechanical property data of each location as a mechanical property data point. This multi-point detection method can include, but is not limited to, on-site core drilling and rebound hammer testing. Then, the terminal performs structural scanning processing on the cement pavement to obtain pavement structure data, which includes substructure data of each pavement panel and connection structure data of the connection structures of each pavement panel. Then, based on each mechanical property data point, the terminal constructs a variability numerical model of cement concrete. This variability data model is a numerical model constructed based on the numerical relationship between the semi-variable function of cement concrete and distance, as well as the mean and coefficient of variation of concrete material properties. The specific construction process will be described in detail later.

[0075] Step S102: Based on the pavement structure data, construct a pavement structure model of cement pavement, and based on the variability numerical model, perform performance assignment processing on the pavement structure model to generate mechanical response distribution data of each pavement unit.

[0076] In this embodiment, the terminal constructs a pavement structure model of the cement pavement based on pavement structure data, and performs performance assignment processing on the pavement structure model based on a variability numerical model to generate mechanical response distribution data for each pavement unit. This pavement structure model includes finite element models of each pavement panel, connection structure models of the connections between pavement panels, and a structural model of the temperature gradient of the environment in which the cement pavement panels are located. The specific construction process will be explained in detail later. The mechanical response distribution data for each pavement unit is obtained by converting the structural model of each pavement unit into a covariance matrix, constructing a three-dimensional matrix including mechanical performance vectors, and then calculating the mechanical response distribution results of each pavement panel. The specific construction process will be explained in detail later.

[0077] Step S103: Based on the mechanical response distribution data of each pavement unit, identify the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab, and based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab, identify the reliability analysis results of the cement pavement through a reliability analysis strategy.

[0078] In this embodiment, the terminal identifies the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab based on the mechanical response distribution data of each pavement slab unit. Based on this probability distribution information, the terminal uses a reliability analysis strategy to identify the reliability analysis results of the cement pavement. This reliability analysis strategy is an analysis strategy that analyzes the reliability information of the cement pavement based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab. The specific analysis process will be explained in detail later.

[0079] Based on the above scheme, by incorporating the spatial variability information of the properties of cement pavement slab materials into the pavement structure model, the mechanical response distribution data of each slab unit of the cement pavement can be analyzed. This effectively improves the comprehensiveness and accuracy of the analysis of cement pavement, avoiding the limitations and high errors of considering the cement pavement as a whole slab. Then, the probability distribution information of the maximum tensile stress at the bottom of each slab is analyzed and identified separately to identify the reliability analysis results of the cement pavement. Structurally, it is divided into multiple slabs, and in detail, by combining the spatial variability information of cement concrete, a sub-node model of the cement pavement is constructed. This makes the identification of the reliability analysis results of the cement pavement more in line with the actual situation, thereby improving the accuracy of the analysis of PCC pavement.

[0080] Optionally, based on the mechanical property data points, a numerical model of the variability of cement concrete is constructed, including: grouping the mechanical property data points to obtain data pairs, and identifying the distance between the two mechanical property data points in each data pair; calculating the dissimilarity value between the two mechanical property data points in each data pair based on the distance value, and calculating the semi-variable function value between the two mechanical property data points in each data pair based on the dissimilarity value and the mechanical property value data; identifying the numerical relationship between the semi-variable function value and the distance value based on the distance value and the semi-variable function value; and identifying the variability parameters of cement concrete material through a statistical identification strategy based on the mechanical property data values ​​of each mechanical property data point, and constructing a numerical model of the variability of cement concrete based on the variability parameters of cement concrete material and the numerical relationship between the semi-variable function value and the distance value.

[0081] In this embodiment, the terminal groups the mechanical performance data points to obtain data pairs and identifies the distance between the two mechanical performance data points in each data pair. Specifically, the terminal first acquires n discrete data points of cement concrete mechanical performance at different locations. These data points are combined in pairs to form N = n(n-1) / 2 data pairs. The distance between the two points is represented by L. All point pairs with the same distance are recorded, with a total number of N(L).

[0082] Then, based on the distance value of each data pair, the terminal calculates the dissimilarity value between the two mechanical performance data points in each data pair. Based on the dissimilarity value and the mechanical performance values ​​between the two mechanical performance data points in each data pair, the terminal also calculates the semi-dissimilarity function value between the two mechanical performance data points in each data pair. Specifically, the dissimilarity between two points can be calculated using the following formula:

[0083]

[0084] in express The semivariogram of the measured mechanical properties at a subsequent distance L can be calculated using the following formula:

[0085]

[0086] Then, based on the distance between two mechanical performance data points in each data pair and the semi-variable function value between two mechanical performance data points in each data pair, the terminal identifies the numerical relationship between the semi-variable function value and the distance value through function fitting.

[0087] Then, based on the mechanical performance data values ​​of each mechanical performance data point, the terminal identifies the variability parameters of cement concrete materials through a statistical identification strategy. Based on these variability parameters and the numerical relationship between the semi-variable function value and the distance value, a numerical model of cement concrete variability is constructed. Specifically, the terminal can assume the functional relationship between different correlation functions (such as negative exponential autocorrelation function or Gaussian autocorrelation function). Taking the negative exponential autocorrelation function as an example, consider the nugget effect:

[0088]

[0089] in To account for the intercept term resulting from the nugget effect, and with C and Together they form the undetermined coefficients, and the correlation distance of the concrete random field can be determined by function fitting. .

[0090] Subsequent statistical calculations on the discrete data directly yield the mean and coefficient of variation of the specific cement pavement concrete material properties. In practical applications, the corresponding mean and coefficient of variation can be selected based on the chosen concrete grade. The numerical model is constructed using a traditional numerical model building program, based on the mean and coefficient of variation of the cement pavement concrete material properties (i.e., the variability parameters of the cement concrete material), and the numerical relationship between the semi-variable function value and the distance value, to build a variability numerical model for the cement concrete.

[0091] Based on the above scheme, after accurately identifying the parameters of the numerical model of cement concrete, a numerical model of the spatial variability of cement pavement material properties is established, which improves the efficiency and accuracy of constructing the numerical model of the spatial variability of cement pavement material properties.

[0092] Optionally, based on the pavement structure data, a pavement structure model of the cement pavement is constructed, including: acquiring the current ambient temperature data of the cement pavement and identifying the temperature gradient data corresponding to the current ambient temperature data; decomposing the pavement structure data into substructure data of each pavement panel unit and connection structure data between each pavement panel unit; constructing a finite element model of each pavement panel unit based on the substructure data of each pavement panel unit, and fitting the connection structure model corresponding to each connection structure data based on the connection structure data between each pavement panel unit; and constructing a pavement structure model of the cement pavement based on the finite element model of each pavement panel unit, the connection structure model corresponding to each connection structure data, and the temperature gradient data.

[0093] In this embodiment, the terminal acquires the current ambient temperature data of the cement pavement and identifies the temperature gradient data corresponding to the current ambient temperature data. This temperature gradient data represents the gradient distribution information of the ambient temperature of the cement pavement across each paving panel.

[0094] Then, the terminal breaks down the road structure data into substructure data of each pavement unit and connection structure data between each pavement unit.

[0095] Next, the terminal constructs a finite element model of each pavement panel based on the substructure data of each pavement panel unit, and fits the connection structure model corresponding to each connection structure data based on the connection structure data between the pavement panel units. Specifically, the terminal establishes a finite element model of the cement pavement in the finite element software and performs mesh generation on the model. For cement concrete pavement, due to the influence of joints, at least three concrete pavement panels should be established. The force transmission rod connection between pavement panels can be simulated using spring elements, and the specific boundary conditions and contact conditions should be selected according to the actual situation. In order to discretize the random field and assign mechanical properties to the surface layer elements in the subsequent process, the elements of the pavement panel (the middle pavement panel) used for calculation should be as fine as possible under the condition that the calculation allows, and the element type that makes the stress calculation more accurate should be selected. The base and subbase elements in the direction perpendicular to it should also be appropriately densified, while the remaining elements can be appropriately sparsed to improve the calculation efficiency.

[0096] Finally, the terminal constructs a pavement structure model based on the finite element models of each pavement unit, the connection structure models corresponding to each connection structure data, and the temperature gradient data. The terminal first stitches together the finite element models of each pavement unit and the connection structure models corresponding to each connection structure data to obtain an initial pavement structure model. Then, the terminal adds the temperature gradient data to this initial pavement structure model using the numerical addition method of the finite element model, thus obtaining the pavement structure model. For example, Figure 2 The image shows a pavement structure model for a cement road surface. The terminal uses a single-axle dual-wheel load to apply a moving load to the cement road surface. The loading wheel track of one of the wheels should be located at the edge of the pavement. The loading frequency of the moving load is selected according to the axle load travel speed. It should be ensured that there are at least 25 frames during the process of the moving load acting on the pavement. This is determined by the fact that the distance traveled by the axle load in two adjacent frames cannot be too large.

[0097] Based on the above scheme, after constructing a structural model for each pavement panel, the temperature gradient amplitude is then calculated, which improves the comprehensiveness of the structural analysis of cement pavement.

[0098] Optionally, based on the variability numerical model, the pavement structure model is subjected to performance assignment processing to generate mechanical response distribution data of each pavement unit, including: identifying the spatial location of the center point of each pavement unit in the pavement structure model, and calculating the spatial distance between each pavement unit based on the spatial location of the center point of each pavement unit; generating the covariance matrix of each pavement unit based on the spatial distance between each pavement unit and the finite element model of each pavement unit; constructing the three-dimensional mechanical performance matrix of each pavement unit based on the variability numerical model and the covariance matrix; identifying the discrete distribution data of the mechanical performance of each pavement unit based on the three-dimensional mechanical performance matrix of each pavement unit, and calculating the mechanical response distribution data of each pavement unit based on the discrete distribution data of the mechanical performance.

[0099] In this embodiment, the terminal identifies the spatial location of the center point of each pavement unit in the road structure model, and calculates the spatial distance between each pavement unit based on the spatial location of the center point of each pavement unit. Specifically, the terminal first obtains the coordinates of the center point of each pavement unit in the finite element model through the inp file of the finite element model, and then uses the center point coordinates to calculate the spatial distance between any two points.

[0100] Then, based on the spatial distance between each pavement element and the finite element model of each pavement element, the terminal generates the covariance matrix of each pavement element. Specifically, the terminal obtains the correlation coefficient between any two points according to the variability numerical model. Multiply by variance Obtain the covariance function Thus, the covariance matrix of the finite element elements of the paving panel is constructed. , where n is the total number of elements. For the covariance matrix It can be decomposed using the Cholesky method:

[0101]

[0102] Where L is a lower triangular matrix.

[0103] Then, based on the variability numerical model and the covariance matrix, the terminal constructs the three-dimensional mechanical property matrix of each panel element. Specifically, let Y be a column vector composed of n independent random numbers that follow a standard normal distribution, then the nth-order mechanical property vector Z generated by the random field can be expressed as:

[0104]

[0105] Then, by adding the mean to Z and transforming it into a three-dimensional matrix according to the lengths of the three dimensions x, y, and z, we can obtain a 3D mechanical performance matrix that meets the requirements of random field mean, correlation function, and variance.

[0106] Next, the terminal identifies the discrete distribution data of the mechanical properties of each plywood element based on its three-dimensional mechanical property matrix, and calculates the mechanical response distribution data of each plywood element based on this discrete distribution data. Specifically, the terminal saves the results of random field discretization into m txt files. After finite element modeling is completed, the preprocessing module of the finite element software ABAQUS assigns the mechanical property values ​​from any txt file to each plywood element according to the element coordinates. Then, ABAQUS automatically generates an inp file for the model, named "0.inp". Python code then uses text matching to modify the mechanical property data from the m txt files to the parts of "0.inp" that correspond to the mechanical property values ​​of the plywood elements, thus generating "1.inp", "2.inp", ..., "m.inp". Subsequently, based on the ABAQUS preprocessing module, the m inp files are submitted sequentially, and the corresponding m odb files are calculated. This completes the assignment of mechanical properties to the finite element elements and the calculation of the mechanical response results, resulting in m inp files and m odb files. Figure 3 The image shown is a sample diagram illustrating the distribution of mechanical response results for each paving panel of a cement pavement.

[0107] Based on the above scheme, each paving panel can be decomposed into the distribution information of the discrete mechanical response results corresponding to each node, thereby improving the accuracy of the characterization of the mechanical details of the paving panel.

[0108] Optionally, based on the mechanical response distribution data of each pavement unit, the probability distribution information of the maximum tensile stress at the bottom of the cement pavement is identified, including: for each pavement unit, based on the mechanical response distribution data of the pavement unit, extracting the tensile stress value of each frame of each bottom node of the pavement unit; based on the tensile stress value of each frame of each bottom node, identifying the maximum stress value of each bottom node; based on the maximum stress value of each bottom node, identifying the maximum unit stress value of the pavement unit, and returning to execute the step of extracting the tensile stress value of each frame of each bottom node of the pavement unit based on the mechanical response distribution data of the pavement unit, until the preset iteration condition is met to obtain the maximum unit stress value of each pavement unit; based on the maximum unit stress value of each pavement unit, fitting the sub-probability distribution information of the maximum tensile stress of the pavement unit, and using the sub-probability distribution information of the maximum tensile stress of all pavement units as the probability distribution information of the maximum tensile stress at the bottom of the cement pavement.

[0109] In this embodiment, the terminal extracts the tensile stress values ​​of each bottom node of each plywood unit for each frame based on the mechanical response distribution data of the plywood unit. Specifically, for each odb file, the terminal uses the ABAQUS post-processing module to extract the tensile stress of all nodes at the bottom of the plywood unit in each frame. , where i is the frame number and j is the bottom node number.

[0110] Then, based on the tensile stress values ​​of each bottom node across all frames, the terminal identifies the maximum stress value for each bottom node. Specifically, the terminal uses Python code to extract the maximum stress value from all nodes in each frame. .

[0111] Next, based on the maximum stress value of each bottom node, the terminal identifies the maximum element stress of the plywood unit and returns the step of extracting the tensile stress value of each frame of each bottom node of the plywood unit based on the mechanical response distribution data of the plywood unit, until the preset iteration conditions are met, thus obtaining the maximum element stress of each plywood unit. Specifically, the terminal further extracts the maximum stress value in all frames. Then, the terminal performs the above steps for each ADB file to obtain the maximum stress values ​​of all m ADB files. Where m is a preset iteration condition.

[0112] Finally, based on the maximum stress values ​​of each pavement unit, the terminal fits the sub-probability distribution information of the maximum tensile stress of the pavement unit, and uses the sub-probability distribution information of the maximum tensile stress of all pavement units as the probability distribution information of the maximum tensile stress at the bottom of the cement pavement. The fitting method involves applying the maximum stress values ​​of m pavement units... By performing a normal distribution fit and a goodness-of-fit test, the probability distribution of the maximum tensile stress at the bottom of the plate is obtained.

[0113] Based on the above scheme, the probability distribution of the maximum tensile stress at the bottom of the slab was obtained by considering the spatial variability of concrete materials and the coupling effect of temperature and moving load, which improved the comprehensiveness and accuracy of the probability distribution identification.

[0114] Optionally, based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement slab, a reliability analysis strategy is used to identify the reliability analysis results of the cement pavement, including: based on the sub-probability distribution information of the maximum tensile stress of each pavement slab unit, a reliability evaluation strategy is used to identify the reliability evaluation results of each pavement slab unit; based on the reliability evaluation results of each pavement slab unit and the connection relationship between each pavement slab unit, a reliability analysis strategy is used to identify the reliability analysis results of the cement pavement.

[0115] In this embodiment, the terminal identifies the reliability evaluation results of each pavement unit based on the sub-probability distribution information of the maximum tensile stress of each pavement unit through a pavement reliability evaluation strategy. Based on the reliability evaluation results of each pavement unit and the connection relationships between them, the terminal identifies the reliability analysis results of the cement pavement through a cement pavement reliability analysis strategy. Specifically, the pavement reliability evaluation strategy involves performing feature evaluation processing on the distribution characteristics corresponding to the sub-probability distribution information of the maximum tensile stress of each pavement unit to obtain the reliability evaluation results of each pavement unit. The cement pavement reliability analysis strategy involves identifying the evaluation value to which the reliability evaluation results of each pavement unit belong, and then performing a weighted summation of the evaluation values ​​of each pavement unit according to the evaluation weight values ​​corresponding to the connection relationships between the pavement units to obtain the reliability analysis results of the cement pavement.

[0116] Based on the above scheme, after combining the probability distribution information of the maximum tensile stress at the bottom of the slab with the different paving slab units under the consideration of the spatial variability of concrete materials and the coupling effect of temperature and moving load, the reliability analysis results of cement pavement are analyzed, which improves the accuracy of reliability analysis.

[0117] This application also provides an example of reliability analysis for cement pavement, such as... Figure 4 As shown, the specific processing procedure includes the following steps:

[0118] Step S401: Obtain the mechanical property data points of cement concrete and the pavement structure data of cement pavement.

[0119] Step S402: Group the mechanical performance data points to obtain data pairs, and identify the distance between the two mechanical performance data points in each data pair.

[0120] Step S403: Based on the distance value of each data pair, calculate the dissimilarity value between the two mechanical performance data points in each data pair, and based on the dissimilarity value between the two mechanical performance data points in each data pair and the mechanical performance value data between the two mechanical performance data points in each data pair, calculate the semi-variable function value between the two mechanical performance data points in each data pair.

[0121] Step S404: Based on the distance value between two mechanical performance data points in each data pair and the semi-variable function value between two mechanical performance data points in each data pair, the numerical relationship between the semi-variable function value and the distance value is identified.

[0122] Step S405: Based on the mechanical property data values ​​of each mechanical property data point, the variability parameters of cement concrete material are identified through a statistical identification strategy. Based on the variability parameters of cement concrete material and the numerical relationship between the semi-variable function value and the distance value, a variability numerical model of cement concrete is constructed.

[0123] Step S406: Obtain the current ambient temperature data of the cement pavement and identify the temperature gradient data corresponding to the current ambient temperature data.

[0124] Step S407: The pavement structure data is split into substructure data of each pavement panel unit and connection structure data between each pavement panel unit.

[0125] Step S408: Based on the substructure data of each plywood unit, construct the finite element model of each plywood unit, and based on the connection structure data between each plywood unit, fit the connection structure model corresponding to each connection structure data.

[0126] Step S409: Based on the finite element model of each pavement unit, the connection structure model corresponding to each connection structure data, and the temperature gradient data, construct the pavement structure model of the cement pavement.

[0127] Step S410: Identify the spatial position of the center point of each pavement unit in the road structure model, and calculate the spatial distance between each pavement unit based on the spatial position of the center point of each pavement unit.

[0128] Step S411: Based on the spatial distance between each pavement element and the finite element model of each pavement element, generate the covariance matrix of each pavement element.

[0129] Step S412: Based on the variability numerical model and the covariance matrix, construct the three-dimensional mechanical property matrix of each panel element.

[0130] Step S413: The three-dimensional mechanical property matrix of each plywood unit is used to identify the discrete distribution data of the mechanical properties of each plywood unit, and the mechanical response distribution data of each plywood unit is calculated based on the discrete distribution data of the mechanical properties.

[0131] Step S414: For each panel element, based on the mechanical response distribution data of the panel element, extract the tensile stress value of each frame of each bottom node of the panel element.

[0132] Step S415: Based on the tensile stress values ​​of each frame for each bottom node, identify the maximum stress value of each bottom node.

[0133] Step S416: Based on the maximum stress of each bottom node, identify the maximum stress of each element in the panel unit, and return to perform the step of extracting the tensile stress value of each frame of each bottom node of the panel unit based on the mechanical response distribution data of the panel unit, until the preset iteration conditions are met, and obtain the maximum stress of each element in the panel unit.

[0134] Step S417: Based on the maximum stress value of each unit of the pavement panel, the sub-probability distribution information of the maximum tensile stress of the pavement panel unit is obtained by fitting, and the sub-probability distribution information of the maximum tensile stress of all pavement panel units is used as the probability distribution information of the maximum tensile stress at the bottom of the cement pavement.

[0135] Step S418: Based on the sub-probability distribution information of the maximum tensile stress of each panel unit, the reliability evaluation result of each panel unit is identified through the panel reliability evaluation strategy.

[0136] Step S419: Based on the reliability evaluation results of each pavement unit and the connection relationship between each pavement unit, the reliability analysis results of the cement pavement are identified through the cement pavement reliability analysis strategy.

[0137] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0138] Based on the same inventive concept, this application also provides a cement pavement reliability analysis device for implementing the aforementioned cement pavement reliability analysis method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more cement pavement reliability analysis device embodiments provided below can be found in the limitations of the cement pavement reliability analysis method described above, and will not be repeated here.

[0139] In one exemplary embodiment, such as Figure 5 As shown, a reliability analysis device for cement pavement is provided, comprising: an acquisition module 510, a generation module 520, and an identification module 530, wherein:

[0140] The acquisition module 510 is used to acquire various mechanical property data points of cement concrete and pavement structure data of cement pavement, and to construct a numerical model of the variability of cement concrete based on each of the mechanical property data points.

[0141] The generation module 520 is used to construct a pavement structure model of the cement pavement based on the pavement structure data, and to perform performance assignment processing on the pavement structure model based on the variability numerical model to generate mechanical response distribution data of each pavement unit.

[0142] The identification module 530 is used to identify the probability distribution information of the maximum tensile stress at the bottom of the cement pavement based on the mechanical response distribution data of each of the pavement slab units, and to identify the reliability analysis result of the cement pavement based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement through a reliability analysis strategy.

[0143] Optionally, the acquisition module 510 is specifically used for:

[0144] The mechanical performance data points are grouped to obtain data pairs, and the distance between the two mechanical performance data points in each data pair is identified.

[0145] Based on the distance value of each data pair, calculate the dissimilarity value between the two mechanical performance data points in each data pair, and based on the dissimilarity value between the two mechanical performance data points in each data pair and the mechanical performance value data between the two mechanical performance data points in each data pair, calculate the semi-variable function value between the two mechanical performance data points in each data pair.

[0146] Based on the distance between two mechanical performance data points in each data pair and the semi-variable function value between two mechanical performance data points in each data pair, the numerical relationship between the semi-variable function value and the distance value is identified.

[0147] Based on the mechanical property data values ​​of each of the aforementioned mechanical property data points, the variability parameters of the cement concrete material are identified through a statistical identification strategy. Based on the variability parameters of the cement concrete material and the numerical relationship between the semivariable function value and the distance value, a variability numerical model of cement concrete is constructed.

[0148] Optionally, the generation module 520 is specifically used for:

[0149] Obtain the current ambient temperature data of the cement pavement and identify the temperature gradient data corresponding to the current ambient temperature data;

[0150] The road surface structure data is broken down into substructure data of each pavement unit and connection structure data between each pavement unit;

[0151] Based on the substructure data of each of the pavement units, a finite element model of each of the pavement units is constructed, and based on the connection structure data between each of the pavement units, a connection structure model corresponding to each connection structure data is fitted.

[0152] Based on the finite element models of each pavement panel unit, the connection structure models corresponding to each connection structure data, and the temperature gradient data, a pavement structure model of the cement pavement is constructed.

[0153] Optionally, the generation module 520 is specifically used for:

[0154] Identify the spatial position of the center point of each pavement unit in the road structure model, and calculate the spatial distance between each pavement unit based on the spatial position of the center point of each pavement unit;

[0155] Based on the spatial distance between each of the pavement panels and the finite element model of each of the pavement panels, the covariance matrix of each of the pavement panels is generated;

[0156] Based on the variability numerical model and the covariance matrix, a three-dimensional mechanical property matrix is ​​constructed for each of the plywood units;

[0157] The three-dimensional mechanical property matrix of each of the plywood units is used to identify the discrete distribution data of the mechanical properties of each of the plywood units, and the mechanical response distribution data of each of the plywood units is calculated based on the discrete distribution data of the mechanical properties.

[0158] Optionally, the identification module 530 is specifically used for:

[0159] For each plywood unit, based on the mechanical response distribution data of the plywood unit, the tensile stress value of each frame of each bottom node of the plywood unit is extracted;

[0160] Based on the tensile stress values ​​of each bottom node in each frame, identify the maximum stress value of each bottom node;

[0161] Based on the maximum stress value of each bottom node, identify the maximum unit stress value of the plywood unit, and return to perform the step of extracting the tensile stress value of each frame of each bottom node of the plywood unit based on the mechanical response distribution data of the plywood unit, until the preset iteration condition is met, and obtain the maximum unit stress value of each unit of the plywood unit.

[0162] Based on the maximum stress value of each unit of the pavement panel, the sub-probability distribution information of the maximum tensile stress of the pavement panel is obtained by fitting, and the sub-probability distribution information of the maximum tensile stress of all pavement panel units is used as the probability distribution information of the maximum tensile stress at the bottom of the cement pavement.

[0163] Optionally, the identification module 530 is specifically used for:

[0164] Based on the sub-probability distribution information of the maximum tensile stress of each of the aforementioned panel units, the reliability evaluation results of each of the aforementioned panel units are identified through a panel reliability evaluation strategy.

[0165] Based on the reliability evaluation results of each pavement panel unit and the connection relationship between each pavement panel unit, the reliability analysis results of the cement pavement are identified through a cement pavement reliability analysis strategy.

[0166] Each module in the aforementioned cement pavement reliability analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0167] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a reliability analysis method for cement pavement. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0168] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0169] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a reliability analysis method for cement pavement.

[0170] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a reliability analysis method for cement pavement.

[0171] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of a reliability analysis method for cement pavement.

[0172] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0173] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0174] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0175] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method of reliability analysis of a cement pavement, characterized by, The method comprises: obtaining each mechanical property data point of cement concrete and pavement structure data of a cement pavement, and constructing a variability numerical model of the cement concrete based on each mechanical property data point; constructing a pavement structure model of the cement pavement based on the pavement structure data; identifying the spatial position of the center point of each pavement panel unit in the pavement structure model, and calculating the spatial distance value between each pavement panel unit based on the spatial position of the center point of each pavement panel unit; generating a covariance matrix of each pavement panel unit based on the spatial distance value between each pavement panel unit and the finite element model of each pavement panel unit; constructing a three-dimensional mechanical property matrix of each pavement panel unit based on the variability numerical model and the covariance matrix; the three-dimensional mechanical property matrix of each pavement panel unit identifies the discrete distribution data of the mechanical properties of each pavement panel unit, and calculates the mechanical response distribution data of each pavement panel unit based on the discrete distribution data of the mechanical properties; based on the mechanical response distribution data of each pavement panel unit, identify the probability distribution information of the maximum tensile stress at the bottom of the cement pavement, and based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement, identify the reliability analysis result of the cement pavement through a reliability analysis strategy.

2. The method of claim 1, wherein, The method comprises: grouping each mechanical property data point to obtain each data pair, and identifying the distance value between the two mechanical property data points in each data pair; based on the distance value of each data pair, calculate the dissimilarity value between the two mechanical property data points in each data pair, and based on the dissimilarity value between the two mechanical property data points in each data pair and the mechanical property value data between the two mechanical property data points in each data pair, calculate the semi-variogram function value between the two mechanical property data points in each data pair; based on the distance value between the two mechanical property data points in each data pair and the semi-variogram function value between the two mechanical property data points in each data pair, identify the numerical relationship between the semi-variogram function value and the distance value; based on the mechanical property data value of each mechanical property data point, identify the variability parameter of the cement concrete material through a statistical identification strategy, and based on the variability parameter of the cement concrete material and the numerical relationship between the semi-variogram function value and the distance value, construct a variability numerical model of the cement concrete.

3. The method of claim 2, wherein, The method comprises: obtaining the current environmental temperature data of the cement pavement, and identifying the temperature gradient data corresponding to the current environmental temperature data; splitting the pavement structure data into substructure data of each pavement panel unit and connection structure data between each pavement panel unit; Based on the substructure data of each pavement slab unit, a finite element model of each pavement slab unit is constructed, and based on the connection structure data between each pavement slab unit, a connection structure model corresponding to each connection structure data is fitted; Based on the finite element model of each pavement slab unit, the connection structure model corresponding to each connection structure data, and the temperature gradient data, a pavement structure model of the cement pavement is constructed.

4. The method of claim 1, wherein, The probability distribution information of the maximum tensile stress at the bottom of the cement pavement is identified based on the mechanical response distribution data of each pavement slab unit, including: For each pavement slab unit, based on the mechanical response distribution data of the pavement slab unit, the tensile stress value of each bottom node of the pavement slab unit is extracted; Based on the tensile stress value of each bottom node, the maximum stress value of each bottom node is identified; Based on the maximum stress value of each bottom node, the unit stress maximum value of the pavement slab unit is identified, and the step of extracting the tensile stress value of each bottom node of the pavement slab unit based on the mechanical response distribution data of the pavement slab unit is returned until the preset iteration condition is met, and the maximum stress value of each unit of the pavement slab unit is obtained; Based on the maximum stress value of each unit of the pavement slab unit, the sub-probability distribution information of the maximum tensile stress of the pavement slab unit is fitted, and the sub-probability distribution information of the maximum tensile stress of all pavement slab units is taken as the probability distribution information of the maximum tensile stress at the bottom of the cement pavement.

5. The method of claim 1, wherein, The reliability analysis result of the cement pavement is identified based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement through a reliability analysis strategy, including: Based on the sub-probability distribution information of the maximum tensile stress of each pavement slab unit, the reliability evaluation result of each pavement slab unit is identified through a pavement slab reliability evaluation strategy; Based on the reliability evaluation result of each pavement slab unit and the connection relationship between each pavement slab unit, the reliability analysis result of the cement pavement is identified through a cement pavement reliability analysis strategy.

6. A reliability analysis device for cement pavement, characterized in that, The device includes: An acquisition module is configured to acquire each mechanical property data point of cement concrete and pavement structure data of a cement pavement, and construct a variability numerical model of the cement concrete based on each mechanical property data point; The generating module is configured to construct a pavement structure model of the cement pavement based on the pavement structure data, identify spatial positions of center points of each pavement panel unit in the pavement structure model, and calculate spatial distance values between each pavement panel unit based on the spatial positions of the center points of each pavement panel unit; generate a covariance matrix of each pavement panel unit based on the spatial distance values between each pavement panel unit and finite element models of each pavement panel unit; construct a three-dimensional mechanical property matrix of each pavement panel unit based on the variability numerical model and the covariance matrix; and identify mechanical property discrete distribution data of each pavement panel unit based on the three-dimensional mechanical property matrix of each pavement panel unit, and calculate mechanical response distribution data of each pavement panel unit based on the mechanical property discrete distribution data. The identifying module is configured to identify probability distribution information of maximum tensile stress at the bottom of the cement pavement based on the mechanical response distribution data of each pavement panel unit, and identify a reliability analysis result of the cement pavement based on the probability distribution information of the maximum tensile stress at the bottom of the cement pavement and a reliability analysis strategy.

7. The apparatus of claim 6, wherein, The obtaining module is specifically configured to: group the mechanical property data points to obtain each data pair, and identify a distance value between the two mechanical property data points in each data pair; calculate a dissimilarity value between the two mechanical property data points in each data pair based on the distance value of each data pair, and calculate a semi-variogram value between the two mechanical property data points in each data pair based on the dissimilarity value between the two mechanical property data points in each data pair and the mechanical property value data between the two mechanical property data points in each data pair; identify a numerical relationship between the semi-variogram value and the distance value based on the distance value between the two mechanical property data points in each data pair and the semi-variogram value between the two mechanical property data points in each data pair; identify a variability parameter of the cement concrete material based on the mechanical property value data of each mechanical property data point through a statistical identification strategy, and construct a variability numerical model of the cement concrete based on the variability parameter of the cement concrete material and the numerical relationship between the semi-variogram value and the distance value.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 5.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5. The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5.

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