Method and device for predicting site settlement deformation under special load

Abstract

The application relates to the technical field of road and bridge engineering, and discloses a prediction method and device for site settlement deformation under special load, wherein the prediction method for site settlement deformation under special load comprises the following steps: determining the road surface base layer type of a site to be evaluated for bearing a special load to be evaluated, and the structural layer combined thickness representing the sum of the thicknesses of the road surface surface layer and the base layer of the site; collecting deflection detection data of the road surface of the site under the action of a preset detection load, wherein the preset detection load is smaller than the special load to be evaluated; and then according to the road surface base layer type, the structural layer combined thickness and the deflection detection data, a preset corresponding relationship is used to determine the settlement deformation prediction result of the site under the action of the special load, wherein the preset corresponding relationship is used to represent the mapping relationship between the structural layer combined thickness, the deflection detection data and the settlement deformation under different road surface base layer types. The application can predict the settlement deformation of the site under the action of the special load.

Description

Technical Field

[0001] This application relates to the field of road and bridge engineering technology, specifically to a method and device for predicting site settlement and deformation under special loads. Background Technology

[0002] Settlement deformation of a site under special loads is a crucial indicator for assessing its ability to withstand such loads. To determine this settlement deformation, relevant techniques typically require directly applying a load equivalent to the intended special load to the site under evaluation and conducting on-site tests to measure the resulting settlement deformation. However, conditions such as pavement base type and structural layer thickness often vary between sites, making it difficult to implement the aforementioned method of directly applying special loads to every site under evaluation.

[0003] Therefore, there is an urgent need for a method to predict site settlement deformation under special loads, in order to solve the problem that it is difficult to predict site settlement deformation under special loads in related technologies. Summary of the Invention

[0004] This application provides a method and apparatus for predicting site settlement deformation under special loads, in order to solve the problem in related technologies that it is difficult to conveniently predict site settlement deformation under special loads.

[0005] Firstly, this application provides a method for predicting site settlement deformation under special loads, the method comprising: Obtain information on the type of pavement base course and the thickness of the structural layer combination at the site to be evaluated. The thickness of the structural layer combination is used to characterize the sum of the surface layer thickness and the base course thickness of the pavement at the site to be evaluated. The site to be evaluated is used to bear the special load to be evaluated. Obtain deflection test data of the pavement at the site to be evaluated. The deflection test data is used to characterize the settlement deformation of the pavement at the site to be evaluated under a preset test load, which is less than the special load to be evaluated. Based on information on pavement base course type, structural layer combination thickness, and deflection test data, the predicted settlement deformation of the site under special load is determined according to a preset correspondence. The preset correspondence is used to characterize the mapping relationship between pavement structural layer combination thickness, deflection test data, and settlement deformation under different pavement base course types.

[0006] In one optional implementation, obtaining deflection test data of the pavement at the site to be evaluated includes: Acquire multiple batches of deflection test data for test points on the pavement of the site to be evaluated, with each batch containing multiple sets of deflection data; Calculate the arithmetic mean and sample variance of deflection data for each batch; Deflection test data are determined based on the arithmetic mean of the sample variance.

[0007] In one optional implementation, the base course type information indicates that the base course type includes flexible base course and semi-rigid base course; the flexible base course is a granular base course, and the semi-rigid base course is an inorganic binder stabilized base course; a preset correspondence is established for flexible base course and semi-rigid base course respectively.

[0008] In one optional implementation, the preset correspondence includes: The prediction results and the structural layer combination thickness information satisfy a non-linear negative correlation. When the structural layer combination thickness information is lower than the first feature inflection point, the change of the prediction results with the structural layer combination thickness information is greater than the change when the structural layer combination thickness information is higher than the first feature inflection point. The prediction results and the deflection detection data have a nonlinear positive correlation. When the deflection detection data is higher than the second feature inflection point, the change of the prediction results with the deflection detection data is greater than the change when the deflection detection data is lower than the second feature inflection point. The influence of structural layer thickness information on the prediction results varies with the changes in deflection detection data.

[0009] In one optional implementation, the expression for the preset correspondence is: ; Where z represents the predicted settlement deformation of the site under special load; x represents the thickness value represented by the combined thickness information of the structural layers; y represents the deflection detection data; z0, B, C, D, E, F, G, and H are coefficients determined based on the type of pavement base course; the first characteristic inflection point corresponds to C, and the second characteristic inflection point corresponds to F.

[0010] In one optional implementation, C is the feature inflection point critical value corresponding to the structural layer combination thickness information. When the structural layer combination thickness information is lower than C, the magnitude of the change in the prediction result with the structural layer combination thickness information is greater than the corresponding magnitude when the structural layer combination thickness information is higher than C. F is the critical value of the characteristic inflection point corresponding to the deflection test data. When the deflection test data is less than F, it indicates that the roadbed stiffness meets the requirements. When the deflection test data is greater than F, it indicates that the roadbed stiffness is insufficient.

[0011] In one alternative implementation, the coefficients are determined through the following steps: Establish a three-dimensional numerical simulation model that includes the surface layer, base layer and subgrade; Multiple sets of parameter working conditions are set, including surface layer thickness parameters, base layer thickness parameters, surface layer elastic modulus parameters, base layer elastic modulus parameters, and subgrade elastic modulus parameters. Preset detection loads and special loads are applied to each working condition in the three-dimensional numerical simulation model to obtain the deflection response data and special load settlement data corresponding to each working condition. For flexible and semi-rigid base courses, regression fitting was performed using structural layer combination thickness parameters and deflection response data as input variables and special load settlement data as target variables to obtain the coefficients corresponding to each base course type.

[0012] In one alternative implementation, the method further includes: The prediction results are corrected based on the comprehensive safety factor; the comprehensive safety factor is synthesized by multiplying the reliability factor and the fitting error factor; the reliability factor is determined based on the target reliability of the target road grade and the level of variation of structural parameters; the fitting error factor is equal to the sum of the proportions of the maximum deviations of the predicted values ​​relative to the experimental values ​​and the preset correspondence between 1 and the predicted values.

[0013] In one alternative implementation, the method further includes: The revised prediction results are compared with the preset site allowable settlement limit to obtain the judgment result of whether the site to be evaluated meets the special load bearing requirements.

[0014] Secondly, this application provides a device for predicting site settlement deformation under special loads, comprising: The thickness information acquisition module is used to acquire information on the type of the pavement base course and the thickness of the structural layer combination at the site to be evaluated. The thickness information of the structural layer combination is used to characterize the sum of the surface layer thickness and the base course thickness of the pavement at the site to be evaluated. The site to be evaluated is used to bear the special load to be evaluated. The deflection data acquisition module is used to acquire deflection test data of the pavement of the site to be evaluated. The deflection test data is used to characterize the settlement deformation of the pavement of the site to be evaluated under the action of a preset test load, which is less than the special load to be evaluated. The settlement deformation prediction module is used to determine the settlement deformation prediction results of the site to be evaluated under special load based on the pavement base type information, structural layer combination thickness information and deflection test data, according to the preset correspondence. The preset correspondence is used to characterize the mapping relationship between the pavement structural layer combination thickness, deflection test data and settlement deformation under different pavement base types.

[0015] The method for predicting site settlement deformation under special loads provided in this application obtains information on the pavement base type, structural layer combination thickness, and pavement deflection data under a preset detection load. This information is then processed according to a preset correspondence to determine the predicted settlement deformation of the site under special loads. Since this preset correspondence represents the mapping relationship between structural layer combination thickness, deflection data, and settlement deformation under different pavement base types, and the preset detection load is less than the special load to be evaluated, there is no need to directly apply a special load to the site. The settlement deformation of the site under special loads can be predicted solely based on the deflection data obtained under a smaller detection load, as well as the pavement base type and structural layer combination thickness, thereby reducing the difficulty of predicting site settlement deformation under special loads. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of the first method for predicting site settlement deformation under special load according to an embodiment of this application; Figure 2 This is a schematic diagram of the second process of a method for predicting site settlement deformation under special loads according to an embodiment of this application; Figure 3 This is a schematic diagram of a computational model according to an embodiment of this application; Figure 4 This is a schematic diagram of the model unit division according to an embodiment of this application; Figure 5 This is a schematic diagram of the impact load time history curve according to an embodiment of this application; Figure 6 This is a comparative schematic diagram showing the maximum vertical displacement of the deflection center when the number of grids is different according to the embodiments of this application; Figure 7 This is a schematic diagram of the impact factor analysis according to an embodiment of this application; Figure 8 This is a cloud map of the vertical deformation of the road surface structure at time t=1.05s according to an embodiment of this application; Figure 9 This is a vertical stress cloud diagram of the road surface structure at time t=1.05s according to an embodiment of this application; Figure 10This is a vertical stress cloud diagram of the soil at t=1.05s according to an embodiment of this application; Figure 11 This is a plastic strain contour map of the soil at t=1.05s according to an embodiment of this application; Figure 12 This is a schematic diagram of the vertical settlement time history curve of the monitoring point at the center of the road surface under special load according to an embodiment of this application; Figure 13 This is a scatter plot comparing the predicted and experimental settlement values ​​of the flexible base layer according to the embodiments of this application; Figure 14 This is a scatter plot comparing the predicted settlement values ​​and experimental values ​​of a semi-rigid base course according to an embodiment of this application; Figure 15 This is a structural block diagram of a site settlement deformation prediction device under special load according to an embodiment of this application; Figure 16 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] It is understood that before using the technical solutions disclosed in the various embodiments of this application, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this application in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained.

[0022] Special load: refers to the instantaneous impact load on the ground generated by a specific device during operation. It is characterized by large load magnitude, short duration, and rapid load change, and is different from general vehicle traffic load.

[0023] Deflection: The vertical bending settlement deformation of the pavement structure when subjected to external loads. It is usually characterized by the maximum vertical displacement at the center of the load application point and comprehensively reflects the overall mechanical state of each structural layer of the pavement and the subgrade.

[0024] Deflection value: A quantitative expression of pavement deflection, usually measured in millimeters (0.01 mm). The larger the deflection value, the weaker the overall pavement structure; conversely, the smaller the deflection value, the harder the pavement and the stronger its load-bearing capacity.

[0025] Deflectometer: A specialized pavement testing device used to measure the deflection of a pavement under standard loads. It is a non-destructive testing device that can obtain deflection data without damaging the pavement structure.

[0026] Three-dimensional numerical simulation model: A calculation model established in a computer using numerical calculation methods such as finite element method to simulate the three-dimensional geometric shape, material mechanical properties and external load conditions of the real pavement structure. Through numerical solution, the dynamic response of the pavement structure under various load conditions such as stress, strain and displacement can be obtained.

[0027] The Finite Element Method (FEM) is a numerical calculation method that discretizes a continuous medium into a finite number of elements (mesh) and then solves the mechanical equations for each element as a whole. It is widely used in simulation analysis in fields such as structural mechanics and geotechnical engineering.

[0028] Elastic modulus (also known as Young's modulus): A mechanical parameter describing a material's ability to resist deformation within its elastic range. It is equal to the ratio of normal stress to the corresponding normal strain, and its unit is MPa (megapascal, or 10^12 MPa). 6 (Pa). The higher the elastic modulus, the higher the material stiffness, and the smaller the deformation under the same load.

[0029] Poisson's ratio is a dimensionless mechanical parameter that describes the ratio of transverse strain to axial strain of a material under uniaxial stress. It reflects the transverse deformation characteristics of the material and is usually between 0 and 0.5.

[0030] Cohesion: The intrinsic binding force between soil or granular material particles due to surface attraction or cementation. It is one of the fundamental strength parameters of the Mohr-Coulomb shear strength criterion, and its unit is kPa (kilopascal, i.e., 10³ Pa).

[0031] Internal Friction Angle: A mechanical parameter describing the shear strength component generated between soil or granular material particles due to contact surface friction and interlocking effect. It is another core parameter of the Mohr-Coulomb strength criterion, and its unit is degrees (°).

[0032] Dilation Angle: A parameter describing the degree of volume expansion of a material during plastic shear flow, measured in degrees (°), reflecting the correlation between volume change and shear deformation during plastic flow.

[0033] Subgrade Resilient Modulus: A mechanical parameter characterizing the elastic deformation recovery properties of subgrade soil under repeated dynamic loads, measured in MPa. It describes the dynamic elastic stiffness of the subgrade under dynamic loads. The higher the subgrade resilient modulus, the harder the subgrade and the smaller the overall deformation of the pavement.

[0034] Orthogonal Test (Orthogonal Experiment): A multi-factor, multi-level experimental design method based on orthogonal arrays. By selecting a balanced and dispersed typical experimental combination, it systematically examines the influence of each factor on the experimental index with a smaller number of experiments, and quantifies the significance ranking of the influence of each factor through statistical methods such as analysis of variance, which greatly reduces the computational workload of the full combination experiment.

[0035] Comprehensive Safety Factor: The overall safety correction factor obtained by combining the correction factors corresponding to multiple sources of uncertainty. By multiplying the theoretical calculation results by this factor, the corrected calculation value can encompass all kinds of uncertainty risks, ensuring that the assessment conclusion has a sufficient conservative safety margin.

[0036] Reliability: The probability that an engineering structure will perform its intended function under specified conditions, usually expressed as a percentage (e.g., 80%); the reliability coefficient is a statistical correction coefficient determined according to relevant specifications based on the target reliability and the level of variation of structural parameters.

[0037] Limit State Design Criterion: A fundamental design principle in structural engineering that requires the deformation effect caused by loads to not exceed the allowable limit value of the structure in order to ensure the safety of the structure under specified working conditions.

[0038] Mesh Independence: A quality verification index for numerical simulation models, indicating that when the finite element mesh is further refined, the calculation results tend to converge and no longer change significantly, indicating that the calculation accuracy is not affected by the mesh density.

[0039] MPa (megapascal): A unit of measurement for pressure and elastic modulus, 1 MPa = 10 6 Pascal (Pa) is commonly used to express the elastic modulus of structural layers and the resilient modulus of subgrade. kPa (kilopascal): a unit of measurement for pressure and stress, 1 kPa = 10³ Pascals, commonly used to express parameters such as cohesion and tensile strength.

[0040] As an optional application scenario of this application embodiment, the method for predicting site settlement deformation under special load can be executed by a terminal device, by a server, or by a combination of a terminal device and a server.

[0041] The terminal equipment can be devices that collect or input site-related data. For example, it can be a data acquisition terminal connected to or integrated with deflection testing equipment, or a smartphone, tablet, laptop, desktop computer, or vehicle-mounted terminal for operators to input data such as pavement base type, structural layer thickness, and deflection testing data. The server can be a device with data processing capabilities, such as a standalone physical server, a server cluster or distributed system, or a cloud server providing cloud services. Communication between the terminal equipment and the server can be via a network, which can be a wired or wireless network.

[0042] In one optional implementation, the terminal device can acquire the pavement base type, structural layer combination thickness, and deflection test data of the site to be evaluated, and send the data to the server via the network. The server determines the settlement deformation prediction result of the site under special load according to the preset correspondence and then returns it to the terminal device for presentation. In another optional implementation, the acquisition of the above data and the determination of the settlement deformation prediction result can both be completed by the same terminal device. This embodiment does not limit this.

[0043] It should be noted that the above optional application scenarios are merely examples of one application scenario and do not limit the scope of protection of this application.

[0044] In related technologies, in order to know the settlement deformation of a site under special load, it is usually necessary to directly apply a load equivalent to the special load to the site to be evaluated for on-site testing. In view of the technical problems of high testing costs and difficult implementation caused by this, the embodiments of this application provide a method for predicting the settlement deformation of a site under special load, which can predict the settlement deformation of the site under special load without directly applying the special load to the site.

[0045] According to an embodiment of this application, a method for predicting site settlement deformation under special loads is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0046] This embodiment provides a method for predicting site settlement deformation under special loads. It can be used in the aforementioned terminal device or the aforementioned server, or it can be executed collaboratively by the terminal device and the server. This embodiment does not limit this method. Figure 1 This is a flowchart of a method for predicting site settlement deformation under special loads according to an embodiment of this application, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Obtain information on the type of the pavement base course and the thickness of the structural layer combination at the site to be evaluated. The thickness of the structural layer combination is used to characterize the sum of the surface layer thickness and the base course thickness of the pavement at the site to be evaluated. The site to be evaluated is used to bear the special load to be evaluated.

[0047] In this step, the site to be evaluated refers to the site that will need to withstand special loads in the future, such as roads, paving stones, or other sites with pavement structures.

[0048] Special loads refer to large-scale loads that the site to be evaluated will have to withstand, such as instantaneous impact loads; correspondingly, the special loads to be evaluated are the special loads that the site to be evaluated will subsequently have to withstand.

[0049] The base course type information is used to characterize the type of base course used in the pavement of the site to be evaluated. For example, the base course type can include different categories such as flexible base course and semi-rigid base course. The mechanical properties of different types of base courses usually differ.

[0050] The structural layer combination thickness information is used to characterize the sum of the surface layer thickness and the base layer thickness of the road surface at the site. For example, when the surface layer thickness is 20cm and the base layer thickness is 20cm, the structural layer combination thickness can be 40cm.

[0051] The "acquisition" can be implemented in various ways. For example, it can be by receiving the above information entered by the operator through an input device, or by reading from a memory or database that has pre-stored site information, or by receiving the above information sent by other devices via a network. This embodiment does not limit the acquisition method.

[0052] Taking a road site to be evaluated as an example, we can first determine that the road surface base is a semi-rigid base, and determine the sum of its surface layer thickness and base layer thickness as the structural layer combination thickness, thereby obtaining the road surface base type information and structural layer combination thickness information of the site to be evaluated, providing basic input for subsequent prediction.

[0053] Step S102: Obtain the deflection test data of the pavement of the site to be evaluated. The deflection test data is used to characterize the settlement deformation of the pavement of the site to be evaluated under the action of a preset test load. The preset test load is less than the special load to be evaluated.

[0054] In this step, the deflection test data is used to characterize the settlement deformation of the pavement at the site to be evaluated under the preset test load. Deflection can be understood as the vertical deformation of the pavement under the load.

[0055] The preset test load refers to the load applied to the road surface to obtain deflection test data. It is different from the special load to be evaluated, and the preset test load is smaller than the special load to be evaluated. In other words, this step does not require directly applying a load equivalent to the special load to the road surface. Instead, a preset test load of a smaller magnitude is used to load the road surface, and the corresponding deflection test data is obtained accordingly.

[0056] Similarly, "acquiring" here can refer to the deflection data measured by the implementing entity itself after applying a preset test load to the road surface, or it can refer to receiving or reading deflection data measured and provided by other devices. For example, a preset test load smaller than the special load to be evaluated can be applied to the target location of the site to be evaluated, and the deflection data at that location under this preset test load can be measured. Since the preset test load is smaller than the special load to be evaluated, its application is relatively easy, thus facilitating the acquisition of deflection data for subsequent prediction.

[0057] Step S103: Based on the information of the road base type, the information of the combined thickness of the structural layers, and the deflection test data, determine the predicted settlement deformation of the site to be evaluated under special load according to the preset correspondence; wherein, the preset correspondence is used to characterize the mapping relationship between the combined thickness of the road structural layers, the deflection test data and the settlement deformation under different road base types.

[0058] In this step, the preset correspondence is used to characterize the mapping relationship between the structural layer combination thickness, deflection test data, and settlement deformation under different pavement base types. That is, different pavement base types can have their own corresponding mapping relationships; under the same pavement base type, this mapping relationship reflects the correspondence between the structural layer combination thickness, deflection test data, and settlement deformation. For example, this preset correspondence can be implemented in various forms such as a functional relationship, a correspondence table, or a pre-established model; this embodiment does not limit its specific form. The settlement deformation prediction result is used to characterize the predicted settlement deformation of the site under special load.

[0059] In practical implementation, the executing entity can first determine the mapping relationship corresponding to the base course type from the preset correspondence based on the pavement base course type information obtained in step S101; then, it can substitute the structural layer combination thickness information obtained in step S101 and the deflection test data obtained in step S102 into the mapping relationship for processing, thereby determining the settlement deformation prediction result of the site to be evaluated under special load. For example, for the site to be evaluated with a semi-rigid base course type, the mapping relationship corresponding to the semi-rigid base course can be selected, and its structural layer combination thickness and deflection test data can be substituted to obtain the settlement deformation prediction result of the site under special load. Thus, without directly applying a special load to the site to be evaluated, the settlement deformation of the site under special load can be predicted using the deflection test data obtained under a preset test load less than the special load, combined with the pavement base course type information and the structural layer combination thickness information.

[0060] In summary, the method for predicting site settlement deformation under special loads provided in this application obtains information on the pavement base type, structural layer combination thickness, and pavement deflection detection data under a preset detection load for the site to be evaluated. By processing this information according to a preset correspondence, the predicted settlement deformation result of the site under special loads can be determined. Since the preset detection load is less than the special load to be evaluated, and the preset correspondence characterizes the mapping relationship between structural layer combination thickness, deflection detection data, and settlement deformation under different pavement base types, it is not necessary to directly apply a special load to the site to predict the settlement deformation under special loads, thereby reducing the cost and implementation difficulty of predicting site settlement deformation under special loads.

[0061] This embodiment provides a method for predicting site settlement deformation under special loads. It can be used in the aforementioned terminal device or the aforementioned server, or it can be executed collaboratively by the terminal device and the server. This embodiment does not limit this method. Figure 2 This is a flowchart of a method for predicting site settlement deformation under special loads according to an embodiment of this application, as shown below. Figure 2As shown, the process includes the following steps: Step S201: Obtain information on the type of the pavement base course and the thickness of the structural layer combination at the site to be evaluated. The thickness of the structural layer combination is used to characterize the sum of the surface layer thickness and the base course thickness of the pavement at the site to be evaluated. The site to be evaluated is used to bear the special load to be evaluated.

[0062] In some embodiments, to facilitate subsequent differentiation of pavement structures and the establishment of corresponding prediction relationships, the base course types indicated by the pavement base course type information can be further subdivided. Accordingly, the base course types indicated by the pavement base course type information can include flexible base courses and semi-rigid base courses; flexible base courses are granular base courses, and semi-rigid base courses are inorganic binder stabilized base courses; preset correspondences are established separately for flexible base courses and semi-rigid base courses. Granular base courses can be understood as base courses mainly composed of granular materials, such as graded crushed stone base courses; inorganic binder stabilized base courses can be understood as base courses formed by stabilizing materials with inorganic binders, such as cement stabilized base courses. Since flexible base courses and semi-rigid base courses differ in their stress-deformation characteristics, establishing preset correspondences for the two types of base courses helps to adapt the prediction relationships to the mechanical properties of different base courses, thereby improving the applicability and accuracy of settlement deformation prediction results to a certain extent.

[0063] Step S202: Obtain deflection test data of the pavement of the site to be evaluated. The deflection test data is used to characterize the settlement deformation of the pavement of the site to be evaluated under the action of a preset test load, which is less than the special load to be evaluated.

[0064] To obtain more stable and reliable deflection test data, in some embodiments, multiple batch tests can be used to acquire deflection test data. Accordingly, acquiring deflection test data of the pavement of the site to be evaluated may include: acquiring multiple batches of deflection test data for test points on the pavement of the site to be evaluated, with each batch containing multiple sets of deflection data; calculating the arithmetic mean and sample variance of the deflection data of each batch; and determining the deflection test data based on the arithmetic mean according to the sample variance.

[0065] Among them, the test point refers to the location selected on the road surface for deflection testing; multiple batches refer to testing the same test point multiple times, and each batch can continuously measure multiple sets of deflection data; the arithmetic mean is the ratio of the sum of the deflection data of each group in a batch to the number of groups; the sample variance is a statistic used to reflect the dispersion of the deflection data of each group in a batch.

[0066] In practical implementation, batches with smaller sample variances, i.e., lower data dispersion, can be selected, and the arithmetic mean of these batches can be used as the deflection test data. Alternatively, further selection can be made from batches whose sample variances meet preset conditions; this embodiment does not limit this approach. By conducting tests on multiple batches and processing the data using the arithmetic mean and sample variance, data distortion caused by random testing errors can be avoided, thereby improving the representativeness and reliability of the deflection test data to a certain extent.

[0067] Step S203: Based on the information of the road base type, the information of the combined thickness of the structural layers, and the deflection test data, determine the predicted settlement deformation of the site to be evaluated under special load according to the preset correspondence; wherein, the preset correspondence is used to characterize the mapping relationship between the combined thickness of the road structural layers, the deflection test data and the settlement deformation under different road base types.

[0068] In this step, the preset correspondence is used to characterize the mapping relationship between the structural layer combination thickness, deflection test data and settlement deformation of the pavement under different pavement base types; the settlement deformation prediction results are used to characterize the predicted settlement deformation of the site to be evaluated under special load.

[0069] In practical implementation, the mapping relationship corresponding to the road base type can be determined from the preset correspondence based on the road base type information. Then, the structural layer combination thickness information and deflection detection data can be substituted into the mapping relationship for processing to obtain the settlement deformation prediction result. For example, the preset correspondence can be implemented in various forms such as functional relationship, correspondence table or pre-established model. This embodiment does not limit its specific form.

[0070] To further clarify the changing patterns reflected by the preset correspondence, in some embodiments, the preset correspondence can exhibit the following characteristics: the prediction result and the structural layer combination thickness information satisfy a nonlinear negative correlation; when the structural layer combination thickness information is lower than the first feature inflection point, the change in the prediction result with the change in the structural layer combination thickness information is greater than the change when the structural layer combination thickness information is higher than the first feature inflection point; the prediction result and the deflection detection data satisfy a nonlinear positive correlation; when the deflection detection data is higher than the second feature inflection point, the change in the prediction result with the change in ...

[0071] Among them, the nonlinear negative correlation means that the overall prediction result decreases as the thickness of the structural layer combination information increases, and the relationship between the two is not a simple linear proportional relationship; the nonlinear positive correlation means that the overall prediction result increases as the deflection detection data increases, and the relationship between the two is also not a simple linear proportional relationship; the first characteristic inflection point refers to the value corresponding to the thickness of the structural layer combination information that changes the magnitude of its influence on the prediction result; the second characteristic inflection point refers to the value corresponding to the deflection detection data that changes the magnitude of its influence on the prediction result.

[0072] This characteristic reflects the respective effects of structural layer thickness and deflection test data on the prediction results, as well as the mutual influence between the two, which helps to make the prediction results more consistent with the actual deformation of the pavement structure under special loads.

[0073] Based on the above relationships, to facilitate quantitative calculations, in some embodiments, the preset correspondence can be implemented using a settlement prediction function, the expression of which is: ; Where z is the predicted settlement deformation of the site under special load; x is the thickness value represented by the structural layer combination thickness information; y is the deflection detection data; z0, B, C, D, E, F, G, and H are coefficients determined according to the type of road base; the first feature inflection point corresponds to C, and the second feature inflection point corresponds to F.

[0074] In the above function, the term containing 'x' can be understood as the effect of the structural layer combination thickness on the prediction result, the term containing 'y' can be understood as the effect of the deflection detection data on the prediction result, and the term containing both 'x' and 'y' can be understood as the correction term for the prediction result due to their interaction. By substituting the structural layer combination thickness value and the deflection detection data into this function, the settlement deformation prediction result can be quantitatively obtained, thus facilitating calculation and implementation.

[0075] To facilitate understanding the physical meaning of the correlation coefficients for characteristic inflection points in the above functions, in some embodiments, the coefficients C and F can be explained as follows: C is the critical value of the characteristic inflection point corresponding to the structural layer combination thickness information. When the structural layer combination thickness information is lower than C, the magnitude of the change in the prediction result with the change in structural layer combination thickness information is greater than the corresponding magnitude when the structural layer combination thickness information is higher than C; F is the critical value of the characteristic inflection point corresponding to the deflection detection data. When the deflection detection data is less than F, it indicates that the roadbed stiffness meets the requirements; when the deflection detection data is greater than F, it indicates that the roadbed stiffness is insufficient. Here, the critical value refers to the boundary value used to distinguish different magnitudes of change or different states; roadbed stiffness can be understood as the roadbed's ability to resist deformation. By giving C and F clear physical meanings, it is beneficial to understand and grasp the prediction process and results.

[0076] In some embodiments, the coefficients of the above functions can be determined through the following steps: Establish a three-dimensional numerical simulation model that includes the surface layer, base layer and subgrade; Multiple sets of parameter working conditions are set, including surface layer thickness parameters, base layer thickness parameters, surface layer elastic modulus parameters, base layer elastic modulus parameters, and subgrade elastic modulus parameters. Preset detection loads and special loads are applied to each working condition in the three-dimensional numerical simulation model to obtain the deflection response data and special load settlement data corresponding to each working condition. For flexible and semi-rigid base courses, regression fitting was performed using structural layer combination thickness parameters and deflection response data as input variables and special load settlement data as target variables to obtain the coefficients corresponding to each base course type.

[0077] Here, subgrade refers to the natural or treated foundation soil located below the base course and supporting the pavement structure; three-dimensional numerical simulation model refers to a three-dimensional model used to simulate the stress and deformation of the pavement structure in a computational environment; parametric working condition refers to the calculation situation composed of different combinations of parameters such as surface layer thickness, base course thickness, surface layer elastic modulus, base course elastic modulus, and subgrade elastic modulus; elastic modulus refers to a parameter used to characterize the material's ability to resist elastic deformation; deflection response data refers to the deflection-related data obtained by the model under the preset detection load; special load settlement data refers to the settlement-related data obtained by the model under special load; input variable and target variable refer to the quantities used as independent and dependent variables in regression fitting, respectively.

[0078] By acquiring data from multiple sets of parameter conditions in a three-dimensional numerical simulation model and performing regression fitting to determine the coefficients, it is helpful to obtain the coefficients corresponding to each type of base layer without relying on a large number of field tests, thereby reducing the implementation cost and difficulty of coefficient determination.

[0079] After obtaining the settlement deformation prediction results, considering the uncertainties that may exist in the on-site detection and prediction process, in some embodiments, the method may further include: Step S204: Correct the prediction results based on the comprehensive safety factor.

[0080] The comprehensive safety factor is synthesized by multiplying the reliability coefficient and the fitting error coefficient; the reliability coefficient is determined based on the target reliability of the target road grade and the level of variation of structural parameters; the fitting error coefficient is equal to the sum of the proportions of the maximum deviations of the predicted value relative to the experimental value and the preset correspondence between 1 and the test value.

[0081] In this step, the comprehensive safety factor is the coefficient used to correct the prediction results; the reliability factor is the coefficient determined based on the target reliability of the target road grade and the level of variation of structural parameters; and the fitting error factor is the coefficient determined based on the deviation of the predicted value from the experimental value according to the preset correspondence.

[0082] Among them, the target road grade refers to the road grade corresponding to the site to be evaluated; the target reliability refers to the reliability index corresponding to the target road grade; the structural parameter variation level grade refers to the grade used to characterize the dispersion of structural parameters; and the maximum deviation ratio refers to the larger of the deviation ratios between the predicted value and the experimental value.

[0083] For example, when the reliability coefficient is 1.05 and the fitting error coefficient is 1.17, the comprehensive safety factor can be 1.23, which is the product of 1.05 and 1.17. This comprehensive safety factor is then used to amplify and correct the prediction result. Since the reliability coefficient and the fitting error coefficient correspond to uncertainties from different sources, multiplying them to form a comprehensive safety factor and then correcting the prediction result helps to simultaneously account for random test errors and model biases, making the corrected prediction result relatively safer.

[0084] After obtaining the revised prediction results, in order to further determine whether the site to be evaluated can withstand the special load, in some embodiments, the method may further include the following determination steps: Step S205: Compare the corrected prediction results with the preset site allowable settlement limit to obtain the judgment result of whether the site to be evaluated meets the special load bearing requirements.

[0085] In this step, the preset site allowable settlement limit refers to the upper limit of settlement that the site is allowed to undergo under special loads; the judgment result refers to the conclusion on whether the site to be evaluated meets the special load bearing requirements.

[0086] For example, when the corrected prediction result is less than the preset allowable settlement limit of the site, it can be determined that the site to be evaluated meets the special load bearing requirements; otherwise, it can be determined that the site to be evaluated does not meet the special load bearing requirements. By comparing the corrected prediction result with the preset allowable settlement limit of the site, the determination result of whether the site to be evaluated meets the special load bearing requirements can be obtained directly, thereby facilitating the selection of sites in engineering applications.

[0087] In summary, the method for predicting site settlement deformation under special loads provided in this application obtains information on the type of pavement base course, the thickness of structural layer combination, and deflection test data of the pavement under a preset test load. Based on a preset correspondence, the method determines the predicted settlement deformation of the site under special loads. Furthermore, it can improve the reliability of deflection test data through multiple batches of testing, achieve quantitative prediction through correspondence established for different base course types and settlement prediction functions, determine function coefficients through three-dimensional numerical simulation models and regression fitting, and obtain the bearing capacity determination result through comprehensive safety factor correction and comparison with allowable settlement limits. Thus, without directly applying special loads to the site, the method can predict the settlement deformation of the site under special loads and determine its bearing capacity suitability, which helps to reduce the corresponding implementation difficulty.

[0088] To better illustrate the method for predicting site settlement deformation under special loads according to embodiments of this application, a preferred embodiment will be provided below. This embodiment is intended to detail the implementation process of this application, but is not intended to limit the scope of protection of this application.

[0089] This implementation method uses Class III and Class IV cement concrete pavements, which are widely used, as preferred typical examples to provide a detailed explanation of the prediction method for site settlement deformation under special loads. The reasons for choosing these types of pavements as examples are as follows: First, Class III and Class IV cement concrete pavements are extremely widespread and are the most common candidate site types for special loads in actual engineering projects, and the research results can directly serve a large number of engineering practices; Second, compared with high-grade pavements such as highways, the structural layer parameters of Class III and Class IV pavements differ more significantly, and the spatial heterogeneity of mechanical conditions between different sites is stronger, making the assessment more representative. Furthermore, compared with high-grade highways, Class III and Class IV highway pavements typically do not undergo strict control of the subgrade resilient modulus, resulting in higher uncertainty in the special load settlement response and a more urgent need for assessment. It should be noted that this implementation method is only a preferred example and does not limit the scope of application of this method. This method is also applicable to other types of pavements and sites, and relevant technical personnel can refer to this implementation method to make corresponding adjustments to the assessment process and parameters according to specific site conditions.

[0090] In the field of special load bearing capacity assessment, candidate sites are mostly based on various levels of highways and leveled ground. Significant differences exist in the road structure layer materials, layer thickness, and foundation soil properties of each site, resulting in substantial spatial heterogeneity in site mechanical conditions. For expressways and Class I and II highways, due to their thicker pavement structure layers and higher requirements for soil resilient modulus control, their instantaneous settlement under special loads generally meets the requirements, as verified by field investigations and tests. However, for numerous Class III and IV cement concrete roads and corresponding grounds, the lack of systematic assessment methods means there is no effective means to determine their bearing capacity. Existing technologies mainly rely on full-scale field testing, directly applying large-scale loads to measure site response. This is not only labor-intensive, difficult to implement, and costly, but also difficult to accurately reproduce the effects of real special load impact dynamic loads, failing to efficiently and systematically reveal the dynamic bearing characteristics of various sites. To address these issues, this implementation proposes a quantitative assessment method for site special load bearing capacity using on-site deflection testing as the assessment tool.

[0091] The entire evaluation method comprises six main steps: selecting key influencing parameters, establishing a three-dimensional numerical simulation model, classifying key influencing parameters and conducting orthogonal experimental analysis, performing successive tests, establishing a deflection value and settlement fitting function, and establishing a bearing capacity evaluation criterion. Each step is described in detail below.

[0092] Roads or pavements generally consist of three parts: the surface layer, the base course (including the subbase or subgrade), and the subgrade. The surface layer, as the topmost structure of the pavement, directly bears external loads; the base course, located below the surface layer, provides support by transferring and dispersing loads downwards; the subgrade, as the natural foundation of the pavement structure, has a decisive influence on the overall deformation response of the pavement. Key factors affecting the bearing capacity of a road, especially transient vertical deformation, mainly include parameters such as the thickness of each structural layer, the elastic modulus of each structural layer, and the resilient modulus of the subgrade, which are expressed as functions as follows: ; In the formula, , Corresponding elastic modulus of each structural layer; Corresponding to the thickness of each structural layer; , , Corresponding to the cohesion of each structural layer; , , The corresponding friction angles are for each structural layer; subscripts 1, 2, and 0 correspond to the surface layer, base layer, and subgrade, respectively.

[0093] In order to obtain a large amount of dynamic mechanical response data of pavement under different combinations of structural parameters in a systematic and efficient manner, and to avoid the high cost of large-scale field tests, this implementation method uses a three-dimensional numerical simulation model (i.e., a virtual pavement structure calculation model established in a computer using the finite element method) as the core research method.

[0094] Figure 3 This is a schematic diagram of the computational model. Figure 4 This is a schematic diagram of the model unit division. For example... Figure 3 and Figure 4 As shown, the computational model consists of three parts: the pavement surface layer, the pavement base layer, and the subgrade. The geometric dimensions of each structural layer are set as follows: the width of the surface layer and base layer is 6m, the width of the subgrade is 20m, the length of the surface layer is 10m, the length of the base layer and subgrade is 20m, and the depth of the subgrade is 10m. The impact load application area is set as a circular region with a radius of 0.9m. Boundary conditions are set as follows: X-direction constraints are applied to the two end faces perpendicular to the X direction, Z-direction constraints are applied to the end faces perpendicular to the Z direction, and Y-direction constraints are applied to the bottom surface of the model to simulate the actual constraint state around and at the bottom of the pavement structure. Interlayer contact relationships are established between the surface layer and the base layer, and between the base layer and the subgrade. These interlayer contact relationships are set as vertical stiffness contact and horizontal Coulomb friction contact to reflect the true force transmission characteristics between the interfaces of each layer.

[0095] The Mohr-Coulomb constitutive model was adopted for the material constitutive relations. This model describes the yield criterion of the material using two basic strength parameters: cohesion and internal friction angle. It can well describe the elastoplastic mechanical behavior of pavement structural layer materials and foundation soil under impact dynamic loads. The main mechanical properties of each structural layer are shown in Table 1.

[0096] Table 1; ; Table 1 presents key mechanical parameters for the surface layer (cement concrete), base layer (water-stabilized crushed stone, lime-stabilized soil, and graded crushed stone), and subgrade (silt / weak, clay / medium, and clay / strong), including structural thickness (cm), density (kg / m³), elastic modulus (MPa), Poisson's ratio, cohesion (kPa), internal friction angle (°), dilatation angle, and tensile strength (kPa). For example, the elastic modulus of the cement concrete surface layer is 27000 MPa, Poisson's ratio is 0.17, cohesion is 3600 kPa, and internal friction angle is 50.8°. The elastic modulus of the subgrade is 25 MPa in the silt / weak state and 60 MPa in the clay / strong state, showing significant differences in modulus between different subgrade strengths.

[0097] Figure 5This is a schematic diagram of the time history curve of impact load. (For example...) Figure 5 As shown, the special load (i.e., special impact load) acts on a circular cross-section with a diameter of 1.8m. The time-history pressure change exhibits distinct three-stage characteristics: from t=0s to t=0.21s, the load increases approximately linearly (rapid loading stage); from t=0.21s to t=0.80s, the load remains relatively stable, with a peak pressure of approximately 0.79MPa, corresponding to a peak impact load of approximately 200 tons (short-term maintenance stage); from t=0.80s to t=1.20s, the load unloads approximately linearly (rapid unloading stage). The load is simplified to be uniformly distributed within the action area, and the strain rate during the impact load loading and unloading process is controlled at 10. -2 s -1 The strain rate is on the order of magnitude and falls within the low strain rate range, so the influence of strain rate on material parameters does not need to be considered. Simultaneously, the loading condition of a 3-ton deflectometer was simulated in the calculation model, and its equivalent load time history curve was also simplified to three stages: rapid loading, short-term maintenance, and rapid unloading. The calculation process is divided into two steps: first, an implicit initial stress field analysis is performed to establish the initial stress state of the pavement structure; then, an explicit impact dynamic analysis is performed to obtain the transient dynamic response of the pavement structure under impact load.

[0098] To verify the effectiveness and mesh independence of the computational model (i.e., the computational results do not change significantly with mesh refinement), the element size was further reduced proportionally, and three sets of models with a total number of elements of 12099, 15732 and 18376 were established in sequence. The maximum vertical displacement at the pavement deflection center of each model under the peak impact pressure was calculated and compared with the deflection test data. Figure 6 This is a comparison chart of the maximum vertical displacement of the deflection center for different grid numbers, such as... Figure 6 As shown, the error range between the calculated and experimental values ​​corresponding to the three meshing schemes is 3.58% to 7.16%, which is within the normal error range acceptable for engineering. This indicates that the established calculation model has good effectiveness and mesh convergence, and can be used for subsequent large-scale parametric simulation calculations.

[0099] After identifying the key influencing parameters, this implementation method employs an orthogonal test for systematic analysis to pinpoint the core factors most significantly affecting the settlement response under special loads and to determine the ranking of the factors' influence strength. An orthogonal test is an efficient experimental design method that comprehensively examines the influence patterns of multiple factors and levels with minimal computation by selecting a small number of representative, evenly distributed test combinations.

[0100] Based on the principle of orthogonal experiment, a three-factor, three-level experimental design was established with surface layer thickness, base course type, and subgrade modulus as the factors to be investigated. The level settings of each factor are shown in Table 2 (orthogonal experimental factor level table): the surface layer thickness is set at three levels: 21cm (strong), 18cm (medium), and 15cm (weak); the base course type is set at three categories: water-stabilized crushed stone (strong), lime-stabilized (medium), and graded crushed stone (weak); and the subgrade modulus is set at three levels: 45MPa (strong), 35MPa (medium), and 25MPa (weak).

[0101] Based on the orthogonal array, a total of 9 orthogonal test calculations were carried out. The corresponding factor combinations and the settlement calculation results at the impact center under special loads for each group are shown in Table 3 (orthogonal test table). All settlement values ​​in Table 3 are negative (downward is the negative direction), and the larger the absolute value, the greater the settlement. The results show that when the soil modulus is the lowest (25MPa), the settlement at the impact center is much greater than that under the condition with a higher soil modulus. The maximum settlement can reach -0.116m, which is significantly higher than -0.029m to -0.033m under the condition with a soil modulus of 45MPa.

[0102] Table 2; ; Table 3; ; Figure 7 This is an impact factor analysis diagram, showing the average settlement at the impact center and its changing trends at different levels of each factor. For example... Figure 7 As shown, analysis of variance of the orthogonal test results yields the following conclusions: the strength of the subgrade is most sensitive to the settlement at the impact center, followed by the thickness of the cement concrete surface layer. The type of base course has a relatively small impact on the settlement at the impact center. The sensitivity of the surface layer thickness to the base course type is similar, with the former being slightly higher than the latter. This ranking reveals that the mechanical properties of the subgrade are the most critical factor determining the site settlement under special loads. This understanding provides a basis for establishing a predictive model using deflection (which is highly sensitive to subgrade condition) as the core input variable.

[0103] Based on the identification of key influencing factors, in order to construct a complete training dataset covering the typical parameter range of Class III and IV cement concrete pavements, this implementation method systematically performs traversal calculations on each key influencing factor within its corresponding parameter interval. The specific parameter settings are as follows: The subgrade modulus is set for 5 working conditions: 20MPa, 30MPa, 40MPa, 60MPa, and 80MPa; the surface layer elastic modulus is set for 4 working conditions: 3000MPa, 3500MPa, 4000MPa, and 4500MPa; the base layer elastic modulus is set for 4 working conditions: 300MPa, 700MPa, 1400MPa, and 2100MPa; the surface layer thickness is set for 3 working conditions: 16cm, 20cm, and 23cm; the base layer thickness is set for 3 working conditions: 16cm, 20cm, and 25cm; and the impact load is set for 2 working conditions: 3t deflectometer load and special load.

[0104] The above parameter combinations generated a total of 5×4×4×3×3×2=1440 sets of simulation tests. Among them, the 3t deflectometer condition corresponds to 720 sets of impact center deflection value calculation results, and the special load condition corresponds to 720 sets of impact center settlement value calculation results. This forms 720 pairs of corresponding data for "3t deflection value - special load settlement value", laying a sufficient data foundation for the subsequent establishment of quantitative prediction functions. The parameter range set above covers the structural layer thickness and subgrade conditions of most Class III and IV cement concrete pavements, and the research results have good applicability within this range.

[0105] Taking one typical working condition as an example, a cement concrete surface layer with a surface layer thickness of 20cm and an elastic modulus of 3000MPa, a cement-stabilized base layer with a base layer thickness of 20cm and an elastic modulus of 2100MPa, and a soil subgrade with a resilient modulus of 25MPa were selected to conduct a detailed analysis of the vertical deformation characteristics and stress-strain distribution characteristics of the pavement under impact load. Figure 8 This is a contour map of the vertical deformation of the pavement structure at time t=1.05s (the load stabilization stage), as shown below. Figure 8 As shown, under impact load, the road surface exhibits significant downward bending settlement in the load center region, with a clear deflection basin shape, and the vertical displacement gradually decreases from the load center outwards. Figure 9 This is the vertical stress contour diagram of the pavement structure at time t=1.05s, as shown below. Figure 9 As shown, stress concentration is mainly distributed in the load area and directly below it. The vertical compressive stress gradually decreases from the load center outwards and downwards, and the peak compressive stress can reach the order of -2384 kPa. Figure 10 This is the vertical stress contour diagram of the soil at t=1.05s, as shown below. Figure 10 As shown, after the load is transferred to the subgrade through the pavement structure layer, significant compressive stress concentration occurs in the shallow subgrade area. The stress distribution range expands with increasing depth, but the amplitude gradually decreases. Figure 11 This is the plastic strain contour map of the soil at t=1.05s, as shown below. Figure 11As shown, the concentrated area of ​​plastic strain is mainly distributed in the shallow soil directly below the load application point. The appearance of plastic strain indicates that the soil in this area has undergone irreversible plastic deformation under the special load impact, which is an important manifestation of the difference between special load settlement and elastic deformation.

[0106] Figure 12 It is the vertical settlement time history curve of the monitoring point at the center of the special load on the road surface. For example... Figure 12 As shown, the vertical settlement at the central monitoring point increases rapidly during the loading phase, reaching its maximum instantaneous settlement value near the end of the load stabilization period at t≈1.05s. After unloading, the pavement experiences a certain degree of elastic rebound, and the settlement tends to stabilize between approximately t=2s and t=4s. The remaining irreversible settlement is the permanent settlement under the special load. By sequentially traversing all 1440 working conditions, the two key mechanical indicators of the pavement deflection basin—maximum instantaneous settlement and maximum irreversible settlement—can be systematically obtained under various parameter combinations.

[0107] Based on the above 720 pairs of data corresponding to "3t deflection value - special load settlement value", a quantitative prediction relationship between deflection value and special load settlement is established.

[0108] Since the settlement value of pavement under special loads is closely related to the elastic modulus of the subgrade, and the deflection value of a 3t deflectometer is affected by the combined influence of the structural layer thickness, the elastic modulus of each layer, and the elastic modulus of the subgrade, and fluctuates greatly, it is difficult to fit the settlement value under special loads using only the deflection value. Therefore, it is necessary to introduce the combined thickness of the structural layers (the sum of the surface layer thickness and the base layer thickness) as a correction variable, and use the deflection value and the combined thickness of the structural layers as dual input variables for modeling. In addition, since the pavement base layer is divided into two main categories, flexible (granular) and semi-rigid (inorganic binder stabilized), the two types of base layers have fundamental differences in load transfer mechanisms and stress-deformation laws. Therefore, corresponding fitting coefficient systems are established for each type to ensure the adaptability of the prediction function to the mechanical properties of different base pavements.

[0109] After comparing and testing various function forms, the double Gompertz function was selected as the fitting function form. This function can effectively describe the nonlinear S-shaped growth characteristic of special load settlement as a function of structural layer thickness and deflection value. The mathematical expression of the fitting function is: ; Where z is the settlement value under special load, in cm; x is the combined thickness of structural layers (the sum of the surface layer thickness and the base layer thickness); y is the deflection value of the deflectometer under a 3t load, in 0.01mm; z0, B, C, D, E, F, G, and H are correlation coefficients.

[0110] Based on the benchmark value for settlement; This is the structural layer composite thickness amplitude coefficient, which controls the total amplitude of the structural layer thickness's contribution to settlement. The critical eigenvalue corresponding to the characteristic inflection point of the combined thickness of the structural layers; the combined thickness is less than At that time, the effect of thickness on settlement changes increases, exceeding... At that time, the settlement changes were gradual; The thickness sensitivity coefficient controls the steepness of the thickness-dependent curve. The deflection amplitude coefficient controls the total amplitude of the settlement contribution when a 3t deflection acts alone. F represents the critical characteristic value corresponding to the inflection point of deflection characteristics. Deflection less than F indicates that the roadbed stiffness is sufficient and the settlement changes slightly with deflection. Deflection greater than F indicates that the roadbed is weak and the settlement increases rapidly. This is the deflection sensitivity coefficient, which controls the steepness of the deflection curve. y is the thickness-deflection interaction coupling coefficient, which characterizes the magnitude of the correction term for settlement caused by the joint coupling effect of structural layer thickness x and deflection y, reflecting the two-factor cross effect.

[0111] Based on the data fitting analysis, the values ​​of each coefficient are as follows.

[0112] When the base layer is a semi-rigid base layer: =2.1835, B= 0.2915,C=30,D=5.3265,E=23.3734,F=43.7890,G=14.970,H= 6.6261; When the base layer is a flexible base layer, the fitting coefficients are G = 23.3944, H = 23.3944. 27.1062H = -27.1062 H= 27.1062.

[0113] Error analysis and verification were performed on the fitting results. Table 4 (Predicted values ​​and error table of road surface) lists the structural layer thickness, 3t deflection value, special load settlement test value, fitted predicted value and prediction error under several typical working conditions.

[0114] Table 4; ; Figure 13 and Figure 14 This is a scatter plot comparing predicted and experimental settlement values. Figure 13 A scatter plot showing the comparison of flexible base layers. Figure 14 This is a scatter plot showing the comparison of semi-rigid base layers. (Example:) Figure 13 and Figure 14As shown, the predicted values ​​and experimental values ​​agree well under various working conditions. The fitting error for flexible granular concrete pavement can be controlled within 13%, and the settlement values ​​corresponding to some data points with larger errors are still within a safe range. The fitting error for semi-rigid water-stabilized concrete pavement can be controlled within 17%. These results indicate that the established fitting prediction function has good prediction accuracy and reliability for both types of base pavements.

[0115] The evaluation criteria are based on the Limit State Design Criterion. The basic control condition is that the deformation effect caused by the load does not exceed the allowable deformation limit of the site, that is, the actual settlement S should not exceed the allowable settlement limit [S].

[0116] Due to the inherent uncertainties of field testing, theoretical model bias, and the randomness of soil parameters, directly comparing settlement values ​​calculated theoretically carries a risk of being biased towards a dangerous level. Therefore, a comprehensive safety factor is introduced. The theoretical settlement prediction results are conservatively corrected by appropriately amplifying the calculation of deformation effects to encompass various uncertainties. A comprehensive safety factor is then applied. From the reliability coefficient With fitting error coefficient The components are synthesized by multiplication, that is: ; Reliability coefficient Based on the target reliability level and structural parameter variation level of the target road grade, pavement deflection and settlement are random variables following a normal distribution. The reliability coefficient is essentially the one-sided upper quantile of the normal distribution, used to estimate the upper limit of deformation under a specified probability level. This implementation method sets an 80% target reliability for Class III roads, and combines it with a medium variation level table for reference. =1.05; This value ensures that, with an 80% probability level, the actual settlement of the site will not exceed the calculated value after correction by the reliability coefficient.

[0117] Fitting error coefficient This equals 1 plus the sum of the proportions of the maximum deviations between the settlement prediction function's predicted values ​​and the experimental values. As an empirical fitting model, the settlement prediction function possesses an inherent, unavoidable systematic fitting residual. This residual differs from the random statistical errors of field measurements and belongs to the model's own systematic bias. Experimental results show that this systematic bias can be controlled to within 17% at most; therefore, the fitting error coefficient is taken as... =1+17%=1.17, to compensate for the potential systematic underestimation of the prediction function's calculation results.

[0118] The random statistical error corresponding to reliability and the model systematic error brought about by the fitting formula belong to two independent and uncorrelated sources of uncertainty. Based on the relative error propagation law of independent random variables, the two types of dimensionless proportional correction coefficients are synthesized by multiplication, which can achieve simultaneous envelope of dual risks. Substituting the above parameters, we get: ; The comprehensive safety factor of 1.23 is the safety correction factor corresponding to the actual deflection value measured by the 3t deflectometer on site.

[0119] Based on the above analysis, the specific criteria for evaluating the bearing capacity of special load sites are as follows: First, investigate the type of the pavement base layer at the proposed site to determine whether it is a flexible or semi-rigid base layer, and measure the total thickness of the entire pavement structure layer; second, conduct three batches of parallel deflection tests on the target measuring points, continuously measuring 3 to 5 sets of deflection data for each batch, calculating the sample mean and sample variance of each batch, and selecting the batch with the smallest variance and the lowest data dispersion, using the arithmetic mean of this batch as the representative deflection value of the measuring point to avoid data distortion caused by accidental testing errors; third, substitute the representative deflection value of the measuring point and the total thickness of the pavement structure layer into the above-mentioned fitting prediction function, and select the corresponding fitting coefficient set according to the base layer type to calculate the special load settlement prediction value; finally, multiply the special load settlement prediction value by a comprehensive safety factor of 1.23 to obtain the corrected settlement prediction value, and compare it with the preset special load settlement limit: if the corrected predicted settlement value does not exceed the limit, the site is judged to meet the special load bearing requirements; otherwise, the site is judged not to meet the requirements, and alternative sites should be selected or appropriate treatment measures should be taken for the site. It should be noted that the special load settlement limit is preset based on the maximum ground settlement adaptability requirements of the device that generates instantaneous impact load. That is, within this limit range, the device can meet the corresponding attitude safety requirements.

[0120] In summary, this implementation method constructs a refined three-dimensional numerical simulation model and uses orthogonal experiments to clarify the sensitivity ranking of subgrade strength, surface layer thickness, and base course type to special load settlement. Based on this, 1440 sets of systematic traversal calculations are conducted to establish a dual Gompertz-type settlement prediction function with structural layer combination thickness and 3t deflection value as dual input variables. Fitting coefficients are calibrated for flexible and semi-rigid base courses, with the fitting error controlled within 13% for flexible base courses and within 17% for semi-rigid base courses. Furthermore, by multiplying the reliability coefficient and the fitting error coefficient to form a comprehensive safety factor of 1.23, a bearing capacity evaluation criterion with dual uncertainty coverage is established. This method replaces costly large-scale field tests with simple and easy-to-implement field deflection testing, enabling quantitative prediction and evaluation of the bearing capacity of Class III and IV cement concrete pavement sites under special load impact. This provides technical support with clear accuracy guarantees and safety margins for the rapid selection of qualified sites in engineering projects.

[0121] This embodiment also provides a device for predicting site settlement deformation under special loads. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0122] This embodiment provides a device for predicting site settlement deformation under special loads, such as... Figure 15 As shown, it includes: The thickness information acquisition module 1501 is used to acquire the pavement base type information and structural layer combination thickness information of the site to be evaluated. The structural layer combination thickness information is used to characterize the sum of the surface layer thickness and base layer thickness of the pavement at the site to be evaluated. The site to be evaluated is used to bear the special load to be evaluated. The deflection data acquisition module 1502 is used to acquire the deflection test data of the pavement of the site to be evaluated. The deflection test data is used to characterize the settlement deformation of the pavement of the site to be evaluated under the action of a preset test load, which is less than the special load to be evaluated. The settlement deformation prediction module 1503 is used to determine the settlement deformation prediction results of the site to be evaluated under special load based on the pavement base type information, structural layer combination thickness information and deflection test data, according to the preset correspondence relationship. The preset correspondence relationship is used to characterize the mapping relationship between the pavement structural layer combination thickness, deflection test data and settlement deformation under different pavement base types.

[0123] In one alternative implementation, the deflection data acquisition module 1502 is used for: Acquire multiple batches of deflection test data for test points on the pavement of the site to be evaluated, with each batch containing multiple sets of deflection data; Calculate the arithmetic mean and sample variance of deflection data for each batch; Deflection test data are determined based on the arithmetic mean of the sample variance.

[0124] In one optional implementation, the base course type information indicates that the base course type includes flexible base course and semi-rigid base course; the flexible base course is a granular base course, and the semi-rigid base course is an inorganic binder stabilized base course; a preset correspondence is established for flexible base course and semi-rigid base course respectively.

[0125] In one optional implementation, the preset correspondence includes: The prediction results and the structural layer combination thickness information satisfy a non-linear negative correlation. When the structural layer combination thickness information is lower than the first feature inflection point, the change of the prediction results with the structural layer combination thickness information is greater than the change when the structural layer combination thickness information is higher than the first feature inflection point. The prediction results and the deflection detection data have a nonlinear positive correlation. When the deflection detection data is higher than the second feature inflection point, the change of the prediction results with the deflection detection data is greater than the change when the deflection detection data is lower than the second feature inflection point. The influence of structural layer thickness information on the prediction results varies with the changes in deflection detection data.

[0126] In one optional implementation, the expression for the preset correspondence is: ; Where z represents the predicted settlement deformation of the site under special load; x represents the thickness value represented by the combined thickness information of the structural layers; y represents the deflection detection data; z0, B, C, D, E, F, G, and H are coefficients determined based on the type of pavement base course; the first characteristic inflection point corresponds to C, and the second characteristic inflection point corresponds to F.

[0127] In one optional implementation, C is the feature inflection point critical value corresponding to the structural layer combination thickness information. When the structural layer combination thickness information is lower than C, the magnitude of the change in the prediction result with the structural layer combination thickness information is greater than the corresponding magnitude when the structural layer combination thickness information is higher than C. F is the critical value of the characteristic inflection point corresponding to the deflection test data. When the deflection test data is less than F, it indicates that the roadbed stiffness meets the requirements. When the deflection test data is greater than F, it indicates that the roadbed stiffness is insufficient.

[0128] In one alternative implementation, the coefficients are determined through the following steps: Establish a three-dimensional numerical simulation model that includes the surface layer, base layer and subgrade; Multiple sets of parameter working conditions are set, including surface layer thickness parameters, base layer thickness parameters, surface layer elastic modulus parameters, base layer elastic modulus parameters, and subgrade elastic modulus parameters. Preset detection loads and special loads are applied to each working condition in the three-dimensional numerical simulation model to obtain the deflection response data and special load settlement data corresponding to each working condition. For flexible and semi-rigid base courses, regression fitting was performed using structural layer combination thickness parameters and deflection response data as input variables and special load settlement data as target variables to obtain the coefficients corresponding to each base course type.

[0129] In one alternative embodiment, the device further includes: The result correction module 1504 is used to correct the prediction results based on the comprehensive safety factor. The comprehensive safety factor is synthesized by multiplying the reliability coefficient and the fitting error coefficient. The reliability coefficient is determined based on the target reliability of the target road grade and the level of variation of structural parameters. The fitting error coefficient is equal to the sum of the maximum deviation ratios of the predicted values ​​and the experimental values ​​of the preset correspondence.

[0130] In an optional implementation, the result correction module 1504 is further configured to: The revised prediction results are compared with the preset site allowable settlement limit to obtain the judgment result of whether the site to be evaluated meets the special load bearing requirements.

[0131] The site settlement deformation prediction device under special loads provided in this application embodiment can execute the site settlement deformation prediction method under special loads provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0132] Figure 16 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0133] The following is a detailed reference. Figure 16 The diagram illustrates a structural schematic suitable for implementing the electronic device described in the embodiments of this application. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 1601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1602 or a program loaded from memory 1608 into random access memory (RAM) 1603. The RAM 1603 also stores various programs and data required for the operation of the electronic device. The processor 1601, ROM 1602, and RAM 1603 are interconnected via a bus 1604. An input / output (I / O) interface 1605 is also connected to the bus 1604.

[0134] Typically, the following devices can be connected to the I / O interface 1605: input devices 1606 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1607 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; memory devices 1608 including, for example, magnetic tape, hard disk, etc.; and communication devices 1609. Communication device 1609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 16 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0135] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 1609, or installed from memory 1608, or installed from ROM 1602. When the computer program is executed by processor 1601, it performs the functions defined in the method for predicting site settlement deformation under special loads according to embodiments of this application.

[0136] Figure 16 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0137] This application also provides a computer-readable storage medium. The methods described above according to this application can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the method for predicting site settlement deformation under special loads shown in the above embodiments is implemented.

[0138] A portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0139] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for predicting site settlement deformation under special loads, characterized in that, The method includes: Obtain information on the type of the pavement base course and the thickness of the structural layer combination at the site to be evaluated. The thickness of the structural layer combination is used to characterize the sum of the surface layer thickness and the base course thickness of the pavement at the site to be evaluated. The site to be evaluated is used to bear the special load to be evaluated. Obtain deflection test data of the pavement of the site to be evaluated. The deflection test data is used to characterize the settlement deformation of the pavement of the site to be evaluated under a preset test load, which is less than the special load to be evaluated. Based on the pavement base course type information, the structural layer combination thickness information, and the deflection detection data, the settlement deformation prediction result of the site to be evaluated under the special load is determined according to a preset correspondence; wherein, the preset correspondence is used to characterize the mapping relationship between the pavement structural layer combination thickness, deflection detection data, and settlement deformation under different pavement base course types.

2. The method according to claim 1, characterized in that, The acquisition of deflection test data of the pavement at the site to be evaluated includes: Acquire multiple batches of deflection test data for the test points on the road surface of the site to be evaluated, with each batch containing multiple sets of deflection data; Calculate the arithmetic mean and sample variance of deflection data for each batch; The deflection test data are determined based on the arithmetic mean of the sample variance.

3. The method according to claim 1, characterized in that, The base course type information indicates that the base course type includes flexible base course and semi-rigid base course; the flexible base course is a granular base course, and the semi-rigid base course is an inorganic binder stabilized base course; the preset correspondence is established for the flexible base course and the semi-rigid base course respectively.

4. The method according to claim 1, characterized in that, The preset correspondence includes: The prediction result and the structural layer combination thickness information satisfy a non-linear negative correlation. When the structural layer combination thickness information is lower than the first feature inflection point, the change of the prediction result with the structural layer combination thickness information is greater than the change when the structural layer combination thickness information is higher than the first feature inflection point. The prediction result and the deflection detection data have a nonlinear positive correlation. When the deflection detection data is higher than the second feature inflection point, the change of the prediction result with the deflection detection data is greater than the change when the deflection detection data is lower than the second feature inflection point. The degree to which the thickness information of the structural layer combination affects the prediction result varies with the change of the deflection detection data.

5. The method according to claim 4, characterized in that, The expression for the preset correspondence is: ； Where z is the predicted settlement deformation result of the site to be evaluated under the special load; x is the thickness value represented by the combined thickness information of the structural layers; y is the deflection detection data; z0, B, C, D, E, F, G, and H are coefficients determined according to the type of road base; the first feature inflection point corresponds to C, and the second feature inflection point corresponds to F.

6. The method according to claim 5, characterized in that, C is the feature inflection point critical value corresponding to the structural layer combination thickness information. When the structural layer combination thickness information is lower than C, the magnitude of the change in the prediction result with the structural layer combination thickness information is greater than the corresponding magnitude when the structural layer combination thickness information is higher than C. F is the critical value of the characteristic inflection point corresponding to the deflection detection data. When the deflection detection data is less than F, it indicates that the roadbed stiffness meets the requirements. When the deflection detection data is greater than F, it indicates that the roadbed stiffness is insufficient.

7. The method according to claim 5, characterized in that, The coefficient is determined through the following steps: Establish a three-dimensional numerical simulation model that includes the surface layer, base layer and subgrade; Multiple sets of parameter working conditions are set, including surface layer thickness parameters, base layer thickness parameters, surface layer elastic modulus parameters, base layer elastic modulus parameters, and subgrade elastic modulus parameters. The preset detection load and the special load are applied to each working condition in the three-dimensional numerical simulation model to obtain the deflection response data and special load settlement data corresponding to each working condition. For flexible and semi-rigid base courses, regression fitting was performed using structural layer combination thickness parameters and deflection response data as input variables and special load settlement data as target variables to obtain the coefficients corresponding to each base course type.

8. The method according to claim 1, characterized in that, The method further includes: The prediction results are corrected based on a comprehensive safety factor; the comprehensive safety factor is synthesized by multiplying a reliability factor and a fitting error factor; the reliability factor is determined based on the target reliability of the target road grade and the level of variation of structural parameters; the fitting error factor is equal to the sum of the proportions of the maximum deviation of the predicted value relative to the experimental value and the preset correspondence between 1 and the predicted value.

9. The method according to claim 8, characterized in that, The method further includes: The corrected prediction results are compared with the preset site allowable settlement limit to obtain the determination result of whether the site to be evaluated meets the special load bearing requirements.

10. A device for predicting site settlement deformation under special loads, characterized in that, include: The thickness information acquisition module is used to acquire the road base type information and structural layer combination thickness information of the site to be evaluated. The structural layer combination thickness information is used to characterize the sum of the surface layer thickness and base layer thickness of the road surface at the site to be evaluated. The site to be evaluated is used to bear the special load to be evaluated. The deflection data acquisition module is used to acquire the deflection detection data of the pavement of the site to be evaluated. The deflection detection data is used to characterize the settlement deformation of the pavement of the site to be evaluated under a preset detection load, wherein the preset detection load is less than the special load to be evaluated. The settlement deformation prediction module is used to determine the settlement deformation prediction result of the site to be evaluated under the special load based on the pavement base type information, the structural layer combination thickness information and the deflection detection data, according to a preset correspondence; wherein, the preset correspondence is used to characterize the mapping relationship between the pavement structural layer combination thickness, deflection detection data and settlement deformation under different pavement base types.

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