Method and device for determining vertical compressive bearing capacity of concrete pipe pile

Abstract

This application relates to the field of pipe pile technology, and particularly to a method and equipment for determining the vertical compressive bearing capacity of concrete pipe piles. The method includes: acquiring the actual vertical compressive bearing capacity parameters of a first pipe pile and the foundation physical parameters of a second pipe pile; determining the bearing capacity influence coefficient of the second pipe pile, effectively mapping the depth range variation characteristics of the measured data of the first pipe pile to the second pipe pile; extracting core feature points from the foundation physical parameters of the second pipe pile, combining the depth progression relationship and the bearing capacity influence coefficient; constructing the mechanical performance information of the second pipe pile based on the core feature points and the bearing capacity influence coefficient of the second pipe pile; and obtaining the final vertical compressive bearing capacity value by hierarchically integrating and optimizing the mechanical performance information. This significantly reduces the cost and time investment of on-site testing while maintaining prediction accuracy, and improves decision-making efficiency in engineering design and construction processes.

Description

Technical Field

[0001] This application belongs to the field of pipe pile technology, and in particular relates to a method and equipment for determining the vertical compressive bearing capacity of concrete pipe piles. Background Technology

[0002] In existing civil engineering construction and pile foundation design, concrete pipe piles are the core component of the foundation bearing structure, and their vertical compressive bearing capacity is a key parameter to ensure the safety and stability of the overall structure. Typically, to obtain the vertical compressive bearing capacity of the pipe piles, static load tests or dynamic tests are conducted on the pipe piles before or during construction, and the bearing capacity data is obtained through actual measurements.

[0003] However, existing methods have obvious limitations: on the one hand, static load or dynamic tests are costly and time-consuming, especially in large-scale construction scenarios, where it is not economical to conduct individual tests on each pipe pile; on the other hand, due to differences in site conditions or soil environment, the test results of a single pipe pile are difficult to be directly extended to other pipe piles under similar geological conditions, resulting in significant uncertainty in bearing capacity prediction.

[0004] To improve prediction efficiency, existing technologies attempt to establish empirical models based on the fundamental physical parameters of pipe piles (such as pile length, pile diameter, concrete strength, and the coefficient of friction of the soil around the pile) to estimate bearing capacity. However, these models typically only consider local or average parameters, lacking a detailed characterization of the stress characteristics along the depth of the pile, making it difficult to accurately reflect the overall bearing capacity of the pipe pile under different geological layers. Furthermore, existing methods often fail to fully utilize measured actual bearing capacity data from other pipe piles, resulting in a low degree of integration between measured data and theoretical models. This leads to insufficient accuracy in bearing capacity prediction, posing certain safety hazards in construction design and risk assessment. Summary of the Invention

[0005] This application provides a method and equipment for determining the vertical compressive bearing capacity of concrete pipe piles, which can solve the problem that the existing technology cannot make full use of the actual bearing capacity data of other pipe piles that have been measured, resulting in insufficient bearing capacity prediction accuracy and certain safety hazards in construction design and risk assessment.

[0006] In a first aspect, embodiments of this application provide a method for determining the vertical compressive bearing capacity of concrete pipe piles, including:

[0007] Obtain the actual vertical compressive bearing capacity parameters of the first pipe pile and the foundation physical parameters of the second pipe pile; the second pipe pile and the first pipe pile are related in the same geological environment; Based on the actual vertical compressive bearing capacity parameters of the first pipe pile, determine the bearing capacity influence coefficient of the second pipe pile; Extract core feature points that reflect the stress characteristics of the second pipe pile from the foundation physical parameters; Based on the core feature points and the bearing capacity influence coefficient of the second pipe pile, the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile is determined; The vertical compressive bearing capacity of the second pipe pile is determined based on the mechanical performance information.

[0008] The technical solutions described in this application embodiment have at least the following technical effects: The method for determining the vertical compressive bearing capacity of concrete pipe piles provided in this application obtains the actual vertical compressive bearing capacity parameters of the first pipe pile and the foundation physical parameters of the second pipe pile, and establishes the bearing capacity correlation between the two, providing a reliable measured data basis for predicting the bearing capacity of the second pipe pile. By determining the bearing capacity influence coefficient of the second pipe pile, the depth range variation characteristics of the measured data of the first pipe pile are effectively mapped to the second pipe pile, realizing accurate adjustment of the pipe pile bearing capacity under different geological environments and avoiding prediction deviations caused by relying solely on empirical formulas or average parameters. Core feature points are extracted from the foundation physical parameters of the second pipe pile, and combined with the depth progression relationship and the bearing capacity influence coefficient, the mechanical performance information of the second pipe pile is constructed, realizing a fine characterization of the stress characteristics of the pile body along the depth. By integrating and optimizing the mechanical performance information in layers, the initial bearing capacity value is corrected to the final vertical compressive bearing capacity value that conforms to the geological conditions and pile structure characteristics. This ensures that the prediction results take into account both the theoretical model and the actual pile characteristics, enabling the bearing capacity calculation process to make full use of the measured pipe pile data. At the same time, it enables high-precision prediction of unmeasured pipe piles, avoiding the safety hazards that may be caused by traditional static or empirical methods in construction design. While maintaining the accuracy of prediction, it significantly reduces the cost and time investment of field tests and improves the decision-making efficiency in the engineering design and construction process.

[0009] Secondly, embodiments of this application provide a system for determining the vertical compressive bearing capacity of concrete pipe piles, applied to electronic devices. The system for determining the vertical compressive bearing capacity of concrete pipe piles includes: The acquisition unit is used to acquire the actual vertical compressive bearing capacity parameters of the first pipe pile and the foundation physical parameters of the second pipe pile; the second pipe pile and the first pipe pile are related in the same geological environment; The coefficient unit is used to determine the bearing capacity influence coefficient of the second pipe pile based on the actual vertical compressive bearing capacity parameters of the first pipe pile. The extraction unit is used to extract core feature points reflecting the stress characteristics of the second pipe pile from the foundation physical parameters of the second pipe pile. An information unit is used to determine mechanical performance information for calculating the vertical compressive bearing capacity of the second pipe pile based on the core feature points and the bearing capacity influence coefficient of the second pipe pile. The result unit is used to determine the vertical compressive bearing capacity of the second pipe pile based on the mechanical performance information.

[0010] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any of the foregoing aspects.

[0011] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method described in any of the preceding aspects.

[0012] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to perform the method described in any of the preceding aspects.

[0013] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the above aspects, and will not be repeated here. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a method for determining the vertical compressive bearing capacity of concrete pipe piles according to an embodiment of this application; Figure 2 This is a schematic diagram of the core feature points of the method for determining the vertical compressive bearing capacity of concrete pipe piles provided in an embodiment of this application; Figure 3 This is a vector calibration schematic diagram of a method for determining the vertical compressive bearing capacity of concrete pipe piles provided in an embodiment of this application; Figure 4 This is a structural schematic diagram of a system for determining the vertical compressive bearing capacity of concrete pipe piles according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0016] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0017] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0018] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0019] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determination" or "if the described condition or event is detected" may be interpreted, depending on the context, as "once determination," "in response to determination," "once the described condition or event is detected," or "in response to the detection of the described condition or event."

[0020] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] In existing civil engineering construction and pile foundation design, concrete pipe piles are the core component of the foundation bearing structure, and their vertical compressive bearing capacity is a key parameter to ensure the safety and stability of the overall structure. Typically, to obtain the vertical compressive bearing capacity of the pipe piles, static load tests or dynamic tests are conducted on the pipe piles before or during construction, and the bearing capacity data is obtained through actual measurements.

[0023] However, existing methods have obvious limitations: on the one hand, static load or dynamic tests are costly and time-consuming, especially in large-scale construction scenarios, where it is not economical to conduct individual tests on each pipe pile; on the other hand, due to differences in site conditions or soil environment, the test results of a single pipe pile are difficult to be directly extended to other pipe piles under similar geological conditions, resulting in significant uncertainty in bearing capacity prediction.

[0024] To improve prediction efficiency, existing technologies attempt to establish empirical models based on the fundamental physical parameters of pipe piles (such as pile length, pile diameter, concrete strength, and the coefficient of friction of the soil around the pile) to estimate bearing capacity. However, these models typically only consider local or average parameters, lacking a detailed characterization of the stress characteristics along the depth of the pile, making it difficult to accurately reflect the overall bearing capacity of the pipe pile under different geological layers. Furthermore, existing methods often fail to fully utilize measured actual bearing capacity data from other pipe piles, resulting in a low degree of integration between measured data and theoretical models. This leads to insufficient accuracy in bearing capacity prediction, posing certain safety hazards in construction design and risk assessment.

[0025] To address the aforementioned issues, this application provides a method for determining the vertical compressive bearing capacity of concrete pipe piles. This method acquires the actual vertical compressive bearing capacity parameters of a first pipe pile and the foundation physical parameters of a second pipe pile, establishing a bearing capacity correlation between the two. This provides a reliable basis of measured data for predicting the bearing capacity of the second pipe pile. By determining the bearing capacity influence coefficient of the second pipe pile, the depth range variation characteristics of the measured data of the first pipe pile are effectively mapped to the second pipe pile, achieving precise adjustment of the pipe pile bearing capacity under different geological environments and avoiding prediction deviations caused by relying solely on empirical formulas or average parameters. Core feature points are extracted from the foundation physical parameters of the second pipe pile, and combined with the depth progression relationship and the bearing capacity influence coefficient, constructing the mechanical performance information of the second pipe pile, thus achieving a detailed characterization of the stress characteristics of the pile body along its depth. By integrating and optimizing the mechanical performance information in layers, the initial bearing capacity value is corrected to the final vertical compressive bearing capacity value that conforms to the geological conditions and pile structure characteristics. This ensures that the prediction results take into account both the theoretical model and the actual pile characteristics, enabling the bearing capacity calculation process to make full use of the measured pipe pile data. At the same time, it enables high-precision prediction of unmeasured pipe piles, avoiding the safety hazards that may be caused by traditional static or empirical methods in construction design. While maintaining the accuracy of prediction, it significantly reduces the cost and time investment of field tests and improves the decision-making efficiency in the engineering design and construction process.

[0026] The method for determining the vertical compressive bearing capacity of concrete pipe piles provided in this application embodiment can be applied to electronic devices. In this case, the electronic device is the executing subject of the method for determining the vertical compressive bearing capacity of concrete pipe piles provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of electronic device.

[0027] It is understandable that electronic devices can be various smart devices. For example, electronic devices can be mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), desktop computers, smart screens, smart TVs, and other terminal devices.

[0028] To better understand the method for determining the vertical compressive bearing capacity of concrete pipe piles provided in this application embodiment, the specific implementation process of the method for determining the vertical compressive bearing capacity of concrete pipe piles provided in this application embodiment is described below by way of example.

[0029] Figure 1 A schematic flowchart of the method for determining the vertical compressive bearing capacity of concrete pipe piles provided in an embodiment of this application is shown. Figure 2 This illustration shows a flowchart of the method for determining the vertical compressive bearing capacity of concrete pipe piles provided in an embodiment of this application. The method for determining the vertical compressive bearing capacity of concrete pipe piles includes: S100, obtain the actual vertical compressive bearing capacity parameters of the first pipe pile and the foundation physical parameters of the second pipe pile; the second pipe pile and the first pipe pile have the same geological environment.

[0030] It is understandable that the actual vertical compressive bearing capacity parameters of the first pipe pile refer to quantitative indicators that directly characterize the vertical compressive capacity of the first pipe pile, obtained through on-site static load tests, dynamic testing, or long-term monitoring. These indicators include ultimate bearing capacity values, allowable bearing capacity values, and corresponding settlement curve parameters. The actual vertical compressive bearing capacity parameters reflect the actual stress limit performance of the pipe pile in the target geological environment. The foundation physical parameters of the second pipe pile refer to initial parameters reflecting the geometric and material properties of the pipe pile, obtained from design drawings or geological exploration reports before a complete bearing capacity test is conducted. These parameters include pile diameter, pile length, concrete strength grade, reinforcement configuration, pile integrity, and embedment depth. Since the first and second pipe piles are located in the same geological environment, their bearing performance is statistically correlated. The bearing capacity test results of the first pipe pile can be read from the database, and the physical parameters of the second pipe pile can be extracted from structural design documents or sensor data. Both types of data can be stored in a structured format to provide input for the subsequent calculation of the bearing capacity influence coefficient.

[0031] S200, based on the actual vertical compressive bearing capacity parameters of the first pipe pile, determine the bearing capacity influence coefficient of the second pipe pile.

[0032] The bearing capacity influence coefficient is a proportional coefficient reflecting the difference between the measured bearing capacity of the first pipe pile and the theoretical bearing capacity of the second pipe pile. It is used to correct the results predicted based on physical parameters. The measured bearing capacity of the first pipe pile can be compared with its theoretical value to obtain a deviation ratio. This deviation ratio, after normalization, is defined as the bearing capacity influence coefficient. This bearing capacity influence coefficient essentially represents the corrective effect of local geological conditions and construction techniques on the bearing capacity. When the influence coefficient is less than 1, it indicates that the second pipe pile may have a bearing capacity reduction; when the influence coefficient is greater than 1, it indicates that the second pipe pile may have a bearing capacity amplification effect. By establishing the bearing capacity influence coefficient, the test results of the first pipe pile can be mapped to the second pipe pile, thus avoiding the high-cost practice of conducting individual tests on all pipe piles.

[0033] In one possible implementation, S200, based on the actual vertical compressive bearing capacity parameters of the first pipe pile, determines the bearing capacity influence coefficient of the second pipe pile, including: S210, determine the first bearing capacity characteristic based on the actual vertical compressive bearing capacity parameters of the first pipe pile, and determine the second bearing capacity characteristic based on the foundation physical parameters of the second pipe pile.

[0034] It can be understood that bearing capacity features refer to feature vectors extracted from input parameters that characterize bearing performance. The first bearing capacity feature is a set of features generated from the measured bearing capacity data of the first pipe pile, such as ultimate bearing capacity, settlement rate, and the slope of the load-settlement curve. These raw experimental data can be processed through feature extraction and converted into numerical vectors that can be input for subsequent calculations. The second bearing capacity feature is a calculation vector generated from the foundation physical parameters of the second pipe pile, such as the ratio of pile length to pile diameter, pile end bearing area, and pile side friction coefficient. Structured values ​​reflecting the bearing trend can be obtained by performing formula calculations on the physical parameters. Both types of features are modeled in the same feature space, ensuring that data from different sources can be compared and calculated on a unified dimension.

[0035] S220, the first bearing capacity characteristic and the second bearing capacity characteristic are weighted and averaged to obtain the average bearing capacity characteristic.

[0036] It's understandable that weighted average processing refers to combining two feature sets into a new comprehensive feature according to preset weight coefficients. This can be achieved by dimensional alignment of the first and second bearing capacity features, followed by applying the weight formula. The average bearing capacity characteristics were calculated, among which... Indicates the first bearing capacity characteristic. Indicates the second bearing capacity characteristic. and These are the corresponding weighting coefficients. Weighting coefficients are typically obtained through fitting historical data, or they can be set based on expert experience. This method ensures the accuracy of the measured results for the first pipe pile while also considering the applicability of the physical parameters for the second pipe pile, thus generating a more stable bearing capacity prediction characteristic.

[0037] S230, generate the bearing capacity characteristic parameters of the second pipe pile based on the average bearing capacity characteristics.

[0038] It can be understood that bearing capacity characteristic parameters refer to a structured set of numerical values ​​that can be used to characterize the vertical bearing capacity of the second pipe pile. The average bearing capacity characteristic has already been weighted and integrated with the measured data of the first pipe pile and the physical characteristics of the second pipe pile in the previous step. This comprehensive characteristic itself is still in an intermediate vectorized representation stage and cannot be directly used as a bearing capacity parameter. Therefore, the average bearing capacity characteristic vector can be mapped to a preset bearing capacity parameter space. The mapping process typically includes steps such as numerical normalization, feature decoupling, and principal component recombination. For example, the pile end resistance and pile side friction components included in the average bearing capacity characteristic can be decomposed, and the corresponding bearing capacity parameter values ​​can be calculated separately, then combined into a complete set of bearing capacity parameters. The bearing capacity characteristic parameters obtained in this way not only reflect the bearing performance of the second pipe pile numerically, but also ensure that the parameter structure is consistent with the input format of the actual calculation formula, laying the foundation for subsequent stress analysis based on feature points.

[0039] S300 extracts core feature points from the foundation physical parameters of the second pipe pile to reflect the stress characteristics of the second pipe pile.

[0040] Core feature points refer to spatial locations or numerical nodes that significantly influence the stress distribution during the vertical bearing process of the pipe pile, such as the pile top, pile middle, and pile tip, or specific depth locations at geological strata changes. Core feature points reflect the changing patterns of local stress states. The basic physical parameters of the second pipe pile are typically stored in two-dimensional or three-dimensional structured data, such as pile length, pile diameter, and geological stratification depth. Feature point extraction algorithms can be called to traverse these basic physical parameters layer by layer and determine which depth locations are selected as core feature points based on predefined feature selection rules. Feature selection rules generally include conditions such as bearing capacity sensitivity thresholds, geological interface markers, and abrupt changes in pile structure. By extracting core feature points, the originally continuous physical parameter curves can be transformed into a finite number of discrete point data points, thereby reducing computational load while ensuring that the selected points are sufficiently representative.

[0041] In one possible implementation, S300 extracts core feature points reflecting the stress characteristics of the second pipe pile from the foundation physical parameters, including: S310, Obtain geological survey information for the second pipe pile.

[0042] Geological exploration information refers to data describing the geological characteristics of the strata where the second pipe pile is located, obtained through drilling sampling, in-situ testing, or geological modeling. This information typically includes the distribution of soil layers at different depths, soil physical properties (such as void ratio, water content, and density), mechanical properties (such as compression modulus, cohesion, and internal friction angle), and groundwater level. This data can be read from geological exploration databases or sensor interfaces and stored in a structured manner according to depth coordinates, forming a layered information table that can be used for subsequent calculations. The geological parameters of each layer are combined with the physical parameters of the second pipe pile to determine the contribution of that depth to the bearing capacity of the pipe pile. By obtaining complete geological exploration information, a data model corresponding to depth and soil layer properties can be established, thus providing an accurate environmental background for subsequent core feature point extraction and bearing capacity calculation.

[0043] S320, the depth interval between core feature points is determined based on the geological survey information of the second pipe pile.

[0044] Depth interval, as understood, refers to the distance between adjacent core feature points in the longitudinal direction, used to describe the density of feature points distributed across the pile body. Determining the depth interval is a numerical partitioning operation based on the layered structure and parameter variations in the geological survey information. It involves scanning the thickness and mechanical property differences of each soil layer in the geological survey information. When soil properties change abruptly, a core feature point is set at that location, and the depth interval between adjacent feature points is calculated based on the soil layer thickness. For example, at the interface between the upper clay layer and the lower sand layer, due to significant differences in frictional characteristics and bearing capacity contribution, a feature point is automatically inserted, and the distance from the pile top to that point is calculated. The depth interval value not only reflects the hierarchical nature of the physical structure but also serves as the sampling step size in subsequent bearing capacity feature extraction, ensuring that the extracted feature points cover the overall structure while avoiding data redundancy, thus making the bearing capacity calculation both efficient and accurate.

[0045] S330 identifies core feature points reflecting bearing capacity characteristics from the foundation physical parameters of the second pipe pile based on depth intervals.

[0046] The identification of core feature points can be understood as combining geological stratification information with the geometric and physical parameters of the second pipe pile to locate key nodes that numerically reflect vertical bearing characteristics. The depth interval, determined in the previous step, acts as a longitudinal coordinate reference system. This interval value can be used to extract corresponding physical parameters, such as pile diameter, cross-sectional area, pile perimeter, and pile end area, segment by segment within the pile length. By comparing the differences in parameters between adjacent segments, it is determined whether there is a significant change in the stress characteristics of that segment. If the change exceeds a preset threshold, the depth location is marked as a core feature point. For example, when the pile diameter remains constant but the frictional characteristics of the geological layer change abruptly, a feature point will be set at the point of abrupt change. The identification process can be achieved through matrix scanning and threshold determination, ultimately generating a set of feature points. Each point in the set contains its location coordinates and corresponding physical attributes, providing a discretized input basis for subsequent calculation of the bearing capacity vector.

[0047] S400, based on the core feature points and the bearing capacity influence coefficient of the second pipe pile, determines the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile.

[0048] It can be understood that mechanical performance information refers to a set of parameters describing the internal stress distribution and displacement response of the second pipe pile under vertical load. Mechanical performance information is not a direct physical measurement result, but rather obtained through calculation using core feature points and bearing capacity influence coefficients. Core feature points include three or more key locations selected along the depth direction of the second pipe pile, used to reflect the vertical stress characteristics of the pile body at different depth intervals. The first feature point is usually located near the pile top, representing the mechanical state of the interaction between the upper part of the pile body and the surface soil layer; the second feature point is located in the middle of the pile body, reflecting the stress distribution and deformation of the middle section of the pile body under soil bearing; the third feature point is located near the pile bottom or pile tip, reflecting the bearing capacity of the soil layer at the pile tip and the resistance effect at the pile bottom. The core feature points are arranged in a linear progression along the longitudinal direction of the pile body; please refer to [link to relevant documentation]. Figure 2The depth of the first feature point is less than that of the second feature point, and the depth of the second feature point is less than that of the third feature point. This ensures that mechanical parameters can be continuously mapped and accumulated along the pile depth direction when calculating bearing capacity. This progressive relationship facilitates dividing the pile body into different intervals along the depth during modeling, analyzing the mechanical properties of each interval separately, and then generating an overall bearing capacity prediction through weighted integration. The physical and geological parameters of each core feature point can be input into the bearing capacity correction model, where the bearing capacity influence coefficient acts as an adjustment factor to correct the difference between the theoretical bearing capacity value and the measured results. Through this correction, the stress characteristics at different depths can be uniformly mapped into a standardized mechanical performance space, obtaining numerical values ​​that can be used for subsequent layered integration. Mechanical performance information typically includes the axial stress value, shear force distribution, and deformation gradient of each feature point. Finally, this information is stored as a structured matrix as a direct input for calculating the vertical compressive bearing capacity.

[0049] In one possible implementation, the core feature points include a first feature point, a second feature point, and a third feature point; the first feature point and the second feature point have a depth-progression relationship, and the second feature point and the third feature point also have a depth-progression relationship; S400, based on the core feature points and the bearing capacity influence coefficient of the second pipe pile, the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile is determined, including: S410, obtain a first change vector to reflect the change of mechanical parameters from the first feature point to the second feature point, and obtain a second change vector to reflect the change of mechanical parameters from the second feature point to the third feature point.

[0050] The variation vector can be understood as a mathematical vector used to describe the changing trends of mechanical parameters between feature points at different depths. The first variation vector is the parameter difference vector between the first and second feature points, such as differences in stress, displacement, and friction coefficients; the second variation vector is the same type of difference vector between the second and third feature points. Mechanical performance information corresponding to each feature point can be read from a database, the values ​​of adjacent points can be subtracted, and the result stored as a vector structure. The direction of the vector reflects the increasing or decreasing trend of parameter changes, and the magnitude of the vector reflects the magnitude of the change. For example, if the axial stress at the second feature point is greater than that at the first feature point, then the stress component of the first variation vector is positive. In this way, the originally discrete point data can be transformed into variation characteristics over a continuous interval, providing basic data for subsequent weighted averaging and bearing capacity correction.

[0051] S420, perform a weighted average of the first change vector and the second change vector to obtain the first average change vector.

[0052] As can be understood, weighted averaging refers to combining the mechanical change trends of two different depth intervals into a single comprehensive trend by setting weighting coefficients. The first average change vector is essentially a weighted result, representing the overall mechanical change trend from the first feature point to the third feature point. This can be achieved using the formula... Perform vector weighting, where Indicates the first change vector. Indicates the second change vector. and This refers to the weighting factor. The weighting factor can be dynamically adjusted based on the length of the depth interval, the importance of the geological layer, or the rate of parameter change. For example, if the first interval is longer, a higher weight is assigned to the first change vector. The weighted average vector can avoid distortion of the overall bearing capacity assessment due to fluctuations in a single segment, thus obtaining a smoother and more reliable trend parameter.

[0053] S430, based on the first average variation vector and the bearing capacity influence coefficient of the second pipe pile, determine the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile.

[0054] It is understandable that the first average variation vector can be corrected into a set of mechanical parameters that directly characterize the overall vertical compressive bearing capacity of the second pipe pile. The first average variation vector reflects the variation trend within the depth range, while the bearing capacity influence coefficient provides the weight for correcting the difference between measured and theoretical values. The components of the first average variation vector can be multiplied by the bearing capacity influence coefficient or subjected to nonlinear mapping to obtain the corrected vector set. The corrected vector is then further projected onto the dimension space required for bearing capacity calculation, such as the vertical displacement dimension or the vertical stress dimension, thereby generating the final mechanical performance information. This process ensures that the bearing capacity prediction of the second pipe pile not only considers the variation characteristics of the local range but also incorporates the correction effect of the measured data of the first pipe pile on the overall environment, guaranteeing that the output parameters have both theoretical basis and engineering applicability.

[0055] Optionally, S430, based on the first average variation vector and the bearing capacity influence coefficient of the second pipe pile, the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile is determined, including: S431, obtain the calculation dimension of the vertical compressive bearing capacity to be determined.

[0056] The computational dimension refers to the space of mechanical parameters used for calculating vertical compressive bearing capacity. This space can include key physical quantities such as vertical stress, vertical displacement, pile strain, soil reaction, and pile-soil contact stiffness. Obtaining the computational dimension means identifying each mechanical variable involved in the bearing capacity calculation and defining their distribution range in the depth direction or pile location. The structural parameters of the second pipe pile, geological survey data, and mechanical performance information generated in previous steps can be read, and then the corresponding set of dimensions can be generated according to predefined rules or model requirements. For example, for the vertical stress dimension, a discrete node might be set every 0.5 meters along the pile length, recording the stress value at each node; for the displacement dimension, pile top settlement and pile elastic deformation would be combined to form a continuous or discrete displacement space. In this way, subsequent calculations can perform vector mapping and mechanical operations within a unified parameter space, ensuring that the bearing capacity calculation includes both local interval characteristics and reflects the overall pile behavior, providing accurate basic data for subsequent mapping and correction.

[0057] S432, map the first average change vector to the depth position corresponding to the second feature point.

[0058] Mapping can be understood as mapping the numerical and directional information of the first average change vector to the depth coordinates of the feature points of the second pipe pile, thus associating the change trend with the specific pile location. The first average change vector reflects the mechanical change trend along the depth direction, but it is initially generated in the depth reference system of the first pipe pile. Therefore, it needs to be aligned to the feature point location of the second pipe pile through coordinate transformation. This can be achieved by reading the depth information of the feature points of the second pipe pile and establishing a depth mapping relationship, for example, by using linear interpolation or spline interpolation algorithms to map each component of the first average change vector to the depth of the second feature point. In this way, the first average change vector forms a continuous trend curve in the depth space of the second pipe pile, providing a precise numerical basis for subsequent proportional adjustments based on pile structural parameters. The mapping process ensures that the vector can be coupled with the actual structure and geological conditions of the second pipe pile, enabling subsequent mechanical performance information to accurately reflect the stress characteristics of the pile within a specific depth range.

[0059] S433, at the second feature point, the first average change vector is adjusted by a specified ratio in the calculation dimension to obtain the second average change vector; the specified ratio is determined based on the pile body structural parameters of the second pipe pile.

[0060] It is understandable that the mapped first average variation vector is quantitatively adjusted to better reflect the actual mechanical characteristics of the second pipe pile. The so-called specified ratio refers to a scaling factor calculated based on pile structural parameters such as pile diameter, pile length, pile cross-sectional shape, and material elastic modulus. This scaling factor is used to correct the amplitude or direction of the vector, ensuring it accurately reflects the bearing characteristics of the second pipe pile in the calculation dimension. Each feature point can be traversed, the first average variation vector value at the corresponding depth can be read, and it can be multiplied by a coefficient determined by the pile structural parameters to obtain the corrected second average variation vector. The adjustment is not limited to numerical scaling; it can also include vector direction rotation or component adjustment to match the actual stress conditions in the vertical displacement, stress, or deformation dimensions. Through this operation, the second average variation vector can accurately reflect the influence of the pile structure on the mechanical response, providing reliable input data for bearing capacity calculation, ensuring that the prediction results consider both depth trends and differences in pile characteristics.

[0061] S434, the second average variation vector is corrected according to the bearing capacity influence coefficient of the second pipe pile to obtain the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile.

[0062] It is understandable that the bearing capacity influence coefficient, as a correction weight, is used to adjust the second average variation vector to better reflect the overall vertical bearing capacity characteristics of the second pipe pile. The second average variation vector already reflects the stress trend within the depth range and the influence of the pile structure on the mechanical response, but it still needs to be corrected by considering actual geological conditions and pile-soil interaction. Each vector component can be multiplied or nonlinearly mapped with its corresponding bearing capacity influence coefficient to obtain the corrected vector set. This set not only contains numerical amplitude information but also retains the original directional characteristics, providing complete mechanical performance data for subsequent bearing capacity calculations. Through this correction, the measured data of the first pipe pile, the characteristics of the pile structure, and the differences in geological layers can be taken into account, making the generated mechanical performance information both theoretically reasonable and engineeringly applicable.

[0063] For example, in S434, the second average variation vector is corrected according to the bearing capacity influence coefficient of the second pipe pile to obtain mechanical performance information for calculating the vertical compressive bearing capacity of the second pipe pile, including: S4341, obtain the mechanical constraint boundary corresponding to the calculation of the vertical compressive bearing capacity to be determined.

[0064] It can be understood that mechanical constraint boundaries refer to the boundary conditions used to limit the range of mechanical parameter variations during the calculation of the vertical compressive bearing capacity of the second pipe pile. These boundary conditions reflect the constraints imposed on the pile's stress behavior by the pile structure, the surrounding soil, and the geological layers. For example, the displacement limits at the pile top and bottom, the allowable stress range of the pile, and the maximum friction and shear forces at the pile-soil contact surface all belong to mechanical constraint boundaries. Obtaining these boundaries first requires determining the key depth locations and characteristic points involved in the bearing capacity calculation. Then, combining geological survey data, design specifications, and pile structural parameters, the upper and lower limits of various mechanical parameters are extracted from the database or input data. These boundary conditions can be stored as numerical matrices or functions, with each depth node corresponding to a constraint interval. This allows for numerical correction within the constraint range during subsequent calibration or boundary adaptation of the second average variation vector. By obtaining mechanical constraint boundaries, the bearing capacity calculation can be limited to a reasonable range, avoiding results that do not meet structural safety or geological conditions, and providing necessary engineering constraints for subsequent vector correction and bearing capacity calculation.

[0065] S4342, locate the second average change vector to the depth position corresponding to the second feature point.

[0066] The second average variation vector, corrected by previous steps, is used to characterize the stress variation of the second pipe pile in the depth direction. The second feature point, extracted from the physical parameters and geological information of the second pipe pile foundation, is a key depth node that significantly impacts bearing capacity calculations. The positioning process precisely maps each component of the vector to the actual depth of the pile, ensuring each mechanical parameter matches its corresponding physical location. This can be achieved by reading the depth coordinates of the second feature point and then interpolating or indexing the second average variation vector to map its components to the corresponding depth nodes. For example, if the vector was originally calculated at uniform depth intervals, but the depth distribution of the second feature point is uneven, linear or spline interpolation can be used to adjust the vector to the depth position of the second feature point. Through this positioning process, the mechanical performance information accurately corresponds to the actual pile structure and geological stratification, achieving a spatial mapping from mathematical vectors to the engineering pile, providing a reliable foundation for subsequent boundary adaptation and vertical bearing capacity calculations.

[0067] S4343, based on the second feature point, performs boundary adaptation processing on the second average change vector, and calibrates the second average change vector along the normal vector direction of the mechanical constraint boundary according to the bearing capacity influence coefficient, generating a direction calibration vector.

[0068] Boundary adaptation processing can be understood as adjusting the second average variation vector to conform to the mechanical constraint boundary defined in the vertical compressive bearing capacity calculation of the second pipe pile. The second feature point, as a key depth location, provides a reference node for vector calibration. The depth location and mechanical constraint boundary corresponding to the second feature point can be obtained from the previous step, and the normal vector direction of the constraint boundary in vector space can be calculated, i.e., the maximum allowable direction of change of the constraint boundary. Then, based on the bearing capacity influence coefficient of the second pipe pile, the second average variation vector is projected and adjusted, so that its component along the normal vector direction is increased or decreased according to weights, ensuring that the vector retains its original force variation trend while satisfying the constraint conditions. This can be achieved through matrix operations or vector superposition, where the bearing capacity influence coefficient determines the calibration range, reflecting the contribution of the second pipe pile structure and geological conditions to vector correction. The final generated direction calibration vector not only numerically conforms to the mechanical constraint boundary but also maintains the vertical force trend, and can be directly used for subsequent bearing capacity calculations, improving the accuracy and engineering applicability of the calculation results.

[0069] S4344, based on the bearing capacity influence coefficient of the second pipe pile and the mechanical parameters of the geological layer, maps the direction calibration vector to the bearing capacity calculation dimension through mechanical parameter transformation calculation, and determines the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile.

[0070] It can be understood that the orientation calibration vector is the vector that has undergone boundary adaptation and normal vector correction in the aforementioned steps. Please refer to [link to relevant documentation]. Figure 3 This represents the directional trend of stress variation in the second pipe pile at different depths. The mechanical parameter conversion operation refers to mapping the directional calibration vector from the original computational space to the engineering dimensions required for calculating the vertical compressive bearing capacity, such as vertical stress, vertical displacement, or shear force. This mapping operation not only preserves the vector's inherent trend but also incorporates the bearing capacity influence coefficient of the second pipe pile and the geological layer's mechanical parameters, enabling the vector to reflect the pile's mechanical response under actual geological conditions.

[0071] The system can read the geological layer parameters corresponding to each feature point, including soil elastic modulus, cohesion, and friction angle, and obtain the bearing capacity influence coefficient. This coefficient represents the correction magnitude of the bearing capacity due to the structure and material of the second pipe pile and its correlation with the first pipe pile. Subsequently, the components of the direction calibration vector are calculated with these parameters. Methods such as matrix transformation, linear or nonlinear mapping, or vector projection can be used to adjust the vector to the dimension space for vertical bearing capacity calculation. For example, for each depth node, the vector components are scaled and oriented according to the geological elastic modulus and bearing capacity influence coefficient so that their values ​​in the vertical stress or displacement dimension can accurately reflect the stress state of the pile.

[0072] Through the mapping process, the obtained mechanical performance information includes not only the local stress variation trend but also the structural characteristics of the pile and the mechanical constraints of the soil layer, providing an accurate and engineering-applicable data foundation for calculating the vertical compressive bearing capacity of the second pipe pile. This spatial mapping from vector trend data to engineering mechanical parameters is a crucial step in combining theoretical models with actual pile conditions in bearing capacity prediction.

[0073] S500, determine the vertical compressive bearing capacity of the second pipe pile based on mechanical performance information.

[0074] It is understandable that the vertical compressive bearing capacity of the second pipe pile is a comprehensive parameter calculated by integrating the mechanical performance information obtained in the previous step. This mechanical performance information includes the vertical stress, vertical displacement, and vector data after boundary adaptation and direction calibration at each core feature point, reflecting the stress characteristics of the pile at different depths and the influence of the geological environment. The mechanical parameters of each depth node can be integrated according to a certain numerical model, such as using the layered superposition method, integral method, or finite element analysis method, to accumulate the mechanical contributions of local nodes into the overall vertical bearing capacity value.

[0075] Furthermore, the mechanical property information can provide nonlinear correction parameters, enabling the bearing capacity calculation results to reflect the characteristics of non-uniform soil layers and the uneven distribution of pile materials in actual engineering projects. The final vertical compressive bearing capacity value can serve as a reference indicator for the engineering design and construction of the second pipe pile, used to assess whether the pile bearing capacity meets the design requirements, and to provide basic data for subsequent bearing capacity optimization and structural analysis.

[0076] In one possible implementation, S500 determines the vertical compressive bearing capacity of the second pipe pile based on mechanical performance information, including: S510, acquire numerical information of at least two of the mechanical property information.

[0077] It can be understood that numerical information on mechanical properties refers to quantifiable data that can be directly used for calculations, such as vertical stress, vertical displacement, shear stress, and normal vector components. These values ​​reflect the stress state of the second pipe pile at various depth nodes. The acquisition process refers to extracting these specific values ​​from the previously generated set of mechanical property information and organizing them into a processable data structure, such as a matrix, array, or table, to facilitate subsequent integrated calculations of bearing capacity.

[0078] It can traverse all core feature points and their corresponding depth locations, reading and storing the mechanical parameters of each feature point. The requirement of at least two numerical information ensures that the calculation process can reflect mechanical changes in different directions and cover different types of mechanical indicators, thus comprehensively describing the stress characteristics of the pile. For example, vertical stress and vertical displacement can be selected as two key parameters and stored in different columns of a matrix to form an operable numerical dataset. These values ​​can be weighted, layered, or optimized in subsequent steps to obtain the initial bearing capacity value, providing a solid data foundation for accurately calculating the vertical compressive bearing capacity of the second pipe pile.

[0079] S520, based on the numerical information corresponding to all the mechanical performance information, performs layered integration calculations on all the mechanical performance information to obtain the initial bearing capacity value.

[0080] It can be understood that layered integration calculation refers to accumulating the mechanical performance information of the second pipe pile at different depths in layers to form a preliminary estimate of the overall vertical compressive bearing capacity of the pile body. The mechanical performance information of each layer includes vertical stress. Vertical displacement and the vector components after orientation calibration ,in Indicates the first The location of each depth node. This information has already been combined with the bearing capacity influence coefficient in previous steps. and geological layer mechanical parameters (such as elastic modulus) Cohesion (etc.) has been revised.

[0081] For example, the pile body can be divided along its depth into There are 3 layers, each corresponding to one or more core feature points. For the 1st layer... The mechanical properties of a layer can be obtained by weighted summation to determine the local bearing capacity contribution of that layer. ,in Contributes to the local bearing capacity of the i-th pile layer. Let be the bearing capacity influence coefficient corresponding to the i-th layer. Indicates the first The number of core feature points in the layer Indicates the first The weights of each feature point in this layer, It is a function that comprehensively maps stress, displacement, and vector components into a function of bearing capacity contribution, and can usually be achieved by linear superposition or nonlinear mapping. The vertical stress at the j-th feature point in the i-th layer is... The vertical displacement of the j-th feature point in the i-th layer is... This represents the vector component of the j-th feature point in the i-th layer after direction calibration. Then, the initial bearing capacity value is obtained by vertically summing or integrating the local bearing capacities of all layers. : or ,in, Here, n represents the initial bearing capacity value of the pile, and n represents the total number of layers along the depth of the pile. For depth variables (representing different depth positions of the pile), The bearing capacity influence coefficient at depth z. This represents the pile length, and the integration method is suitable for continuous depth distribution or non-uniform soil layers. If the depth intervals of certain layers are large, linear or higher-order interpolation methods can be used to complete the mechanical property information, making the cumulative calculation continuous and smooth. Furthermore, differences in nonlinear soil layer response or pile structure can be further corrected for the bearing capacity contribution of each layer using nonlinear integration or the piecewise finite element method. The final result... It can simultaneously reflect the local stress characteristics and overall bearing capacity of the pile, providing a quantitative basis for subsequent mechanical optimization.

[0082] S530, the initial bearing capacity value is optimized by mechanical performance processing to obtain the vertical compressive bearing capacity of the second pipe pile.

[0083] It can be understood that mechanical performance optimization treatment refers to optimizing the initial load-bearing capacity value. Based on this, through further analysis and correction of each core feature point and its corresponding mechanical performance information, the final vertical compressive strength and bearing capacity are improved. This better reflects the actual stress characteristics and geological conditions of the second-stage pipe pile. Although the initial bearing capacity value has already incorporated the local stress characteristics of each depth layer, errors may still exist due to local mechanical anomalies, uneven pile structure, or deviations in geological parameters. Therefore, optimization is necessary. We can first statistically analyze the mechanical performance information of all core feature points, such as calculating the contribution weight of each feature point to the initial bearing capacity. Sum of deviation coefficients Then, these weights and deviation coefficients are used to correct the initial bearing capacity value, forming the optimization coefficients. : ,in, It is the contribution weight of each feature point to the initial bearing capacity. This is a function that calculates the correction amount based on the characteristic point deviation; it can be a linear proportional function, a nonlinear mapping function, or a correction function based on finite element simulation. Subsequently, the optimization coefficients are applied to the initial bearing capacity value to obtain the final vertical compressive bearing capacity. ,in, To optimize the coefficients, This is the initial load-bearing capacity value. To determine the vertical compressive bearing capacity, in actual engineering, this process can be iteratively optimized multiple times, taking into account the non-uniformity of the geological layers, the characteristics of the pile material, and the bearing capacity influence coefficient, to achieve the final result. This method can reflect both the overall stress trend and local anomalies. In this way, the vertical compressive bearing capacity of the second pipe pile can more accurately reflect the actual working conditions, providing reliable data for construction design and safety assessment, and providing an operable numerical basis for subsequent decision-making and verification of pile foundation bearing capacity.

[0084] Optionally, in S530, the initial bearing capacity value is optimized for mechanical properties to obtain the vertical compressive bearing capacity of the second pipe pile, including: S531 analyzes the mechanical performance information of all core feature points to obtain the mechanical optimization coefficients for the initial bearing capacity value.

[0085] It is understandable that although the initial bearing capacity value has already incorporated the local stress characteristics of each depth layer, there may still be errors due to local mechanical anomalies, uneven pile structure, or deviations in geological parameters. Therefore, optimization is necessary. One approach is to first statistically analyze the mechanical performance information of all core feature points, such as calculating the contribution weight of each feature point to the initial bearing capacity. Sum of deviation coefficients Then, these weights and deviation coefficients are used to correct the initial bearing capacity value, forming the mechanical optimization coefficients. : ,in, It is the contribution weight of each feature point to the initial bearing capacity. It is a function that calculates the correction amount based on the deviation of feature points, and can be a linear proportional function, a nonlinear mapping function, or a correction function based on finite element simulation.

[0086] For example, in S531, the mechanical performance information of all core feature points is analyzed to obtain mechanical optimization coefficients for the initial bearing capacity value, including: S5311, analyze the matching relationship between the bearing capacity characteristics of the geological layer where each core feature point is located and the basic physical parameters, and calculate the actual influence weight of each core feature point on the overall bearing capacity of the pipe pile based on the mechanical performance information.

[0087] It can be understood that core feature points refer to key nodes selected along the depth direction of the second pipe pile. These nodes have had their mechanical performance information, such as vertical stress, vertical displacement, and direction calibration vector, extracted through the aforementioned steps. Each core feature point is located in a specific geological layer, and different geological layers have different mechanical properties, such as elastic modulus. Cohesion internal friction angle These are the fundamental physical parameters. These parameters determine the contribution characteristics of the pipe pile to its overall bearing capacity within that depth range.

[0088] The mechanical properties of each core feature point can be compared with the basic physical parameters of its geological layer to form a matching matrix. Each element in the matrix Indicates the first The feature point at the th ... The degree of matching across multiple physical parameter dimensions. The degree of matching can be calculated using nonlinear functions or empirical formulas, such as weighting functions: in The stress or displacement value of a feature point in a certain mechanical dimension. The elastic modulus of the corresponding geological layer. For the i-th core feature point, this matrix represents the sum of the mechanical parameter values ​​multiplied by the elastic modulus for all geological layers. This matrix quantifies the bearing capacity contribution of each core feature point under different geological conditions. The actual influence weight of each feature point on the overall bearing capacity of the pipe pile can be calculated based on the matching matrix and mechanical performance information. The formula can be expressed as: ,in, Let be the weight of the actual impact of the i-th core feature point on the overall bearing capacity of the pipe pile. To map local mechanical parameters into functions of bearing capacity contribution, The vertical stress at the j-th feature point in the i-th layer is... The vertical displacement of the j-th feature point in the i-th layer is... The vector components of the j-th feature point in the i-th layer after orientation calibration can be obtained using weighted linear superposition or nonlinear mapping. The final result is... This indicates the weight that each core feature point should be assigned in the overall bearing capacity calculation. It is used for subsequent mechanical optimization processing so that the overall bearing capacity takes into account both local features and geological layer differences, thus achieving a more accurate bearing capacity assessment.

[0089] S5312, based on the influence weights of all core feature points, calculates the mechanical optimization coefficients used to adjust the initial bearing capacity value.

[0090] It can be understood that the mechanical optimization coefficient is used to correct the initial bearing capacity value. The quantification parameters comprehensively map the contributions of each core feature point to the overall bearing capacity calculation, thereby optimizing the initial values. The influence weights of the core feature points... It is calculated in the previous step S5311 and is used to characterize the relative contribution of each feature point to the overall bearing capacity under different geological conditions.

[0091] The influence weights of all core feature points can be normalized to ensure that the sum of the weights is 1, thereby avoiding the excessive influence of certain local anomalies on the overall bearing capacity. The normalization formula can be expressed as: ,in The total number of core feature points, The normalized weights This is the sum of the original weights for all core feature points. Subsequently, the normalized weights can be combined with the mechanical performance information of each feature point (such as vertical stress). Vertical displacement and orientation calibration vector We perform a weighted combination to obtain the optimized contribution of each feature point to the overall bearing capacity: ,in It is a function that maps mechanical performance information to bearing capacity correction values. The vertical stress at the i-th feature point is... Let be the vertical displacement of the i-th feature point. The orientation calibration vector for the i-th feature point is determined. Finally, the optimization contributions of all core feature points are summed to obtain the overall mechanical optimization coefficients. : This mechanical optimization coefficient It can reflect the combined effect of various characteristic points of the pile at different depths and geological layers, and is used to determine the initial bearing capacity value. The values ​​are then adjusted to generate optimized load-bearing capacity values ​​that more closely reflect actual working conditions.

[0092] S532, the initial bearing capacity value is optimized according to the mechanical optimization coefficient to obtain the vertical compressive bearing capacity of the second pipe pile.

[0093] It can be understood that mechanical property optimization processing refers to using the mechanical optimization coefficients calculated in the previous step S5312. For the initial bearing capacity value Make corrections to achieve the final vertical compressive bearing capacity. This better reflects the stress characteristics of the second pipe pile under actual working conditions. Initial bearing capacity value. The mechanical performance information of each core feature point has been integrated through layers, but the differences in geological environment and pile structure at each depth point have not been fully considered. Therefore, further adjustments are needed through mechanical optimization coefficients.

[0094] The mechanical optimization coefficients can be Compared with the initial bearing capacity value The final bearing capacity is generated through numerical calculations. A linear correction model can be used. ,in,, To optimize the coefficients, This is the initial load-bearing capacity value. For vertical compressive bearing capacity, A positive value indicates that the core feature point makes a positive contribution to the bearing capacity, increasing the initial bearing capacity; A negative value indicates that local mechanical conditions reduce the overall load-bearing capacity, requiring a corresponding downward adjustment. To further enhance accuracy, the value can be... Decomposed into local optimization coefficients of each core feature point And accumulate based on depth weighting: , To optimize the coefficients, This is the initial load-bearing capacity value. For vertical compressive bearing capacity, among which This refers to the normalized weights of feature points, ensuring that the contribution of each feature point to the overall bearing capacity is distributed proportionally. The entire optimization process can be implemented through vectorization operations. First, the initial bearing capacity value is multiplied and added element-wise with the optimized contribution vector of each feature point, and then the scalarized final vertical compressive bearing capacity is obtained. By coupling local mechanical characteristics with overall bearing capacity, the bearing capacity prediction of the second pipe pile takes into account both local variations in the depth direction and incorporates geological parameters and pile structure information, thereby obtaining a vertical compressive bearing capacity value that is both in line with engineering practice and convenient for design and safety assessment.

[0095] Corresponding to the method for determining the vertical compressive bearing capacity of concrete pipe piles in the above embodiments, this application also provides a system for determining the vertical compressive bearing capacity of concrete pipe piles. Each unit of the system can implement each step of the method for determining the vertical compressive bearing capacity of concrete pipe piles. Figure 4 The diagram shows a structural block diagram of the system for determining the vertical compressive bearing capacity of concrete pipe piles provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0096] Reference Figure 4 The system for determining the vertical compressive bearing capacity of concrete pipe piles includes: The acquisition unit is used to acquire the actual vertical compressive bearing capacity parameters of the first pipe pile and the foundation physical parameters of the second pipe pile; the second pipe pile and the first pipe pile are related in the same geological environment; The coefficient unit is used to determine the bearing capacity influence coefficient of the second pipe pile based on the actual vertical compressive bearing capacity parameters of the first pipe pile. The extraction unit is used to extract core feature points reflecting the stress characteristics of the second pipe pile from the foundation physical parameters of the second pipe pile. An information unit is used to determine mechanical performance information for calculating the vertical compressive bearing capacity of the second pipe pile based on the core feature points and the bearing capacity influence coefficient of the second pipe pile. The result unit is used to determine the vertical compressive bearing capacity of the second pipe pile based on the mechanical performance information.

[0097] It should be noted that the information interaction and execution process between the above systems / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0098] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0099] This application also provides an electronic device. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 5 Only one is shown in the image), at least one memory 61 ( Figure 5 (Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, it causes the electronic device 6 to perform the steps in any of the above embodiments of the method for determining the vertical compressive bearing capacity of concrete pipe piles, or causes the electronic device 6 to perform the functions of each unit in the above embodiments of the system.

[0100] For example, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the electronic device 6.

[0101] Electronic device 6 can be a computing device or terminal device such as a mobile phone, tablet computer, desktop computer, laptop, handheld computer, and cloud server. This electronic device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0102] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0103] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 61 may be an external storage device of the electronic device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 6. Furthermore, the memory 61 may include both internal and external storage units of the electronic device 6. The memory 61 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0104] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0105] This application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the steps in any of the above method embodiments.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0107] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0108] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0109] In the embodiments provided in this application, it should be understood that the disclosed method and equipment for determining the vertical compressive bearing capacity of concrete pipe piles can be implemented in other ways. For example, the embodiments of the method and equipment for determining the vertical compressive bearing capacity of concrete pipe piles described above are merely illustrative. For instance, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining the vertical compressive bearing capacity of concrete pipe piles, characterized in that, The method includes: Obtain the actual vertical compressive bearing capacity parameters of the first pipe pile and the foundation physical parameters of the second pipe pile; the second pipe pile and the first pipe pile are related in the same geological environment; Based on the actual vertical compressive bearing capacity parameters of the first pipe pile, determine the bearing capacity influence coefficient of the second pipe pile; Extract core feature points that reflect the stress characteristics of the second pipe pile from the foundation physical parameters; Based on the core feature points and the bearing capacity influence coefficient of the second pipe pile, the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile is determined; The vertical compressive bearing capacity of the second pipe pile is determined based on the mechanical performance information.

2. The method as described in claim 1, characterized in that, The step of determining the bearing capacity influence coefficient of the second pipe pile based on the actual vertical compressive bearing capacity parameters of the first pipe pile includes: The first bearing capacity characteristic is determined based on the actual vertical compressive bearing capacity parameters of the first pipe pile, and the second bearing capacity characteristic is determined based on the foundation physical parameters of the second pipe pile. The first bearing capacity characteristic and the second bearing capacity characteristic are weighted and averaged to obtain the average bearing capacity characteristic; The bearing capacity characteristic parameters of the second pipe pile are generated based on the average bearing capacity characteristics.

3. The method as described in claim 1, characterized in that, The extraction of core feature points reflecting the stress characteristics of the second pipe pile from its foundation physical parameters includes: Obtain geological survey information for the second pipe pile; The depth interval between the core feature points is determined based on the geological survey information of the second pipe pile; Based on the depth interval, core feature points reflecting the bearing capacity characteristics are identified from the foundation physical parameters of the second pipe pile.

4. The method as described in claim 1, characterized in that, The core feature points include a first feature point, a second feature point, and a third feature point; there is a depth progression relationship between the first feature point and the second feature point, and a depth progression relationship between the second feature point and the third feature point; The mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile, based on the core feature points and the bearing capacity influence coefficient of the second pipe pile, includes: Obtain a first change vector reflecting the change in mechanical parameters from the first feature point to the second feature point, and obtain a second change vector reflecting the change in mechanical parameters from the second feature point to the third feature point; A weighted average is performed on the first change vector and the second change vector to obtain the first average change vector; Based on the first average change vector and the bearing capacity influence coefficient of the second pipe pile, the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile is determined.

5. The method as described in claim 4, characterized in that, The step of determining the mechanical performance information for calculating the vertical compressive bearing capacity of the second pipe pile based on the first average variation vector and the bearing capacity influence coefficient of the second pipe pile includes: Obtain the calculation dimension of the vertical compressive bearing capacity to be determined; Map the first average change vector to the depth position corresponding to the second feature point; At the second feature point, the first average change vector is adjusted by a specified ratio towards the calculation dimension to obtain the second average change vector; the specified ratio is determined based on the pile body structural parameters of the second pipe pile. The second average variation vector is corrected based on the bearing capacity influence coefficient of the second pipe pile to obtain the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile.

6. The method as described in claim 5, characterized in that, The step of correcting the second average variation vector based on the bearing capacity influence coefficient of the second pipe pile to obtain mechanical performance information for calculating the vertical compressive bearing capacity of the second pipe pile includes: Obtain the mechanical constraint boundary corresponding to the calculation of the vertical compressive bearing capacity to be determined; The second average change vector is located at the depth position corresponding to the second feature point; Based on the second feature point, the second average change vector is subjected to boundary adaptation processing. According to the bearing capacity influence coefficient, the second average change vector is calibrated along the normal vector direction of the mechanical constraint boundary to generate a direction calibration vector. Based on the bearing capacity influence coefficient of the second pipe pile and the mechanical parameters of the geological layer, the direction calibration vector is mapped to the bearing capacity calculation dimension through mechanical parameter conversion calculation to determine the mechanical performance information used to calculate the vertical compressive bearing capacity of the second pipe pile.

7. The method as described in claim 1, characterized in that, The step of determining the vertical compressive bearing capacity of the second pipe pile based on the mechanical performance information includes: Obtain numerical information for at least two of the aforementioned mechanical property information; Based on the numerical information corresponding to all the mechanical performance information, all mechanical performance information is integrated and calculated in layers to obtain the initial bearing capacity value. The initial bearing capacity value is optimized for mechanical properties to obtain the vertical compressive bearing capacity of the second pipe pile.

8. The method as described in claim 7, characterized in that, The process of optimizing the mechanical properties of the initial bearing capacity value to obtain the vertical compressive bearing capacity of the second pipe pile includes: Analyze the mechanical performance information of all the core feature points to obtain the mechanical optimization coefficients for the initial bearing capacity value; The initial bearing capacity value is optimized based on the mechanical optimization coefficient to obtain the vertical compressive bearing capacity of the second pipe pile.

9. The method as described in claim 8, characterized in that, The analysis of the mechanical performance information of all the core feature points to obtain the mechanical optimization coefficients for the initial bearing capacity value includes: The matching relationship between the bearing capacity characteristics of the geological layer where each core feature point is located and the basic physical parameters is analyzed, and the actual influence weight of each core feature point on the overall bearing capacity of the pipe pile is calculated based on the mechanical performance information. Based on the actual influence weights of all the core feature points, mechanical optimization coefficients for adjusting the initial bearing capacity value are calculated.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 9.

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