A method for analyzing PHC pipe pile soil squeezing effect based on multi-source data fusion

Abstract

The application discloses a PHC pipe pile soil squeezing effect analysis method based on multi-source data fusion, relates to the technical field of geotechnical engineering pile foundation construction analysis, and comprises the following steps: acquiring multi-source geology and monitoring data of a target site; constructing a multi-circle layer space evolution model considering soil structure damage; after defining and assigning model parameters and boundary conditions based on the multi-source data, running the model to calculate the distribution of soil super-pore water pressure, the distribution of radial effective stress increment and the development range of the plastic zone of the soil around the pile caused by the soil squeezing effect; comparing and verifying the model calculation results and sensor monitoring data in time and space; according to the verification results, reversely adjusting key model parameters for iterative calculation until the matching degree reaches the standard, and outputting calibrated soil squeezing effect analysis results. Through dynamic data fusion and model iterative calibration, high-fidelity dynamic simulation and accurate analysis of the soil squeezing effect are realized.

Description

Technical Field

[0001] This invention belongs to the field of geotechnical engineering pile foundation construction analysis technology, specifically a PHC pipe pile soil squeezing effect analysis method based on multi-source data fusion. Background Technology

[0002] PHC pipe pile driving construction severely disturbs the surrounding soil, triggering a soil squeezing effect. Currently, conventional analysis methods mainly rely on static data such as engineering geological exploration and static cone penetration tests before construction, and use theoretical models based on the assumption of homogeneous continuous medium for prediction. These models treat soil parameters as fixed values, and their analysis process is a one-time static calculation.

[0003] Existing technologies have shortcomings. Their input data does not integrate real-time monitoring of the construction process with sensor data from the soil surrounding the pile, failing to reflect the dynamic impact of construction. Furthermore, the models typically simplify the soil as a homogeneous material, making it difficult to accurately depict the damage and failure of the soil structure during penetration and its spatial zoning evolution. Due to the simplified model mechanism and the lack of a feedback correction mechanism using measured data, the predicted results often deviate from actual monitoring data.

[0004] An analytical method is needed that can deeply integrate multidimensional data from the entire construction process and precisely characterize the soil damage and spatial evolution process at the physical mechanism level. This method needs to address how to use dynamic data to achieve continuous model calibration, and how to construct a physical model that reflects the structural damage and zonal development of the soil, in order to achieve accurate dynamic assessment of the soil squeezing effect. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art;

[0006] Therefore, this invention proposes a method for analyzing the soil displacement effect of PHC pipe piles based on multi-source data fusion, including:

[0007] Acquire multi-source geological and monitoring data of the target site, including engineering geological exploration data, in-situ static cone penetration test data, real-time monitoring data of pile foundation construction, and sensor data embedded in the soil around the pile.

[0008] A multi-concentric spatial evolution model considering soil structural damage is constructed. This multi-concentric spatial evolution model is used to describe the spatial partitioning and evolution of the stress state and deformation field of the soil around the pile during the penetration of PHC pipe piles.

[0009] The initial parameters and boundary conditions of the multi-sphere spatial evolution model are defined and assigned based on the multi-source geological and monitoring data.

[0010] By running the multi-concentric spatial evolution model, the distribution of excess pore water pressure, radial effective stress increment, and plastic zone development range of the soil around the pile caused by the entire process of PHC pipe pile penetration were calculated.

[0011] The distribution results calculated by the model are compared and verified with the sensor data in a spatiotemporal synchronization.

[0012] Based on the verification results, the key parameters of the multi-sphere spatial evolution model are adjusted in reverse, and the calculation is iterated until the matching degree between the model's calculated distribution results and the sensor's monitored distribution results reaches a preset threshold. Finally, the calibrated soil squeezing effect analysis results are output.

[0013] Furthermore, the construction of the multi-sphere spatial evolution model considering soil structural damage includes:

[0014] Based on the strain path and stress state experienced by the soil under the penetration of the pile, the soil is divided into the compression and remodeling zone, shear slip zone, plastic deformation zone and elastic disturbance zone from the pile-soil interface outward.

[0015] Within the compression and remodeling zone, the concepts of soil structure yield surface and damage factor are introduced, and the soil within the compression and remodeling zone is defined as exhibiting a fully remodeled state, whose constitutive behavior is controlled by remodeled soil parameters.

[0016] Within the shear slip zone and plastic deformation zone, an elastoplastic constitutive relation based on the state-related dilatation theory is used to describe the plastic volume change and strength change of the soil under shear stress.

[0017] Within the elastic disturbance zone, a nonlinear elastic model is used to describe the small recoverable deformations of the soil.

[0018] Establish continuous displacement and stress field equations connecting the above partitions to ensure deformation coordination and stress continuity at the boundaries of each partition, thereby forming a complete multi-concentric spatial evolution model.

[0019] Furthermore, the definition and assignment of initial parameters and boundary conditions for the multi-sphere spatial evolution model based on the multi-source geological and monitoring data includes:

[0020] The soil profile, initial density, natural water content, initial void ratio and undrained shear strength parameters of each soil layer are extracted from the engineering geological exploration data of the target site and assigned to the corresponding soil units in the multi-concentric spatial evolution model.

[0021] The in-situ static earth pressure coefficient, compression modulus and sensitivity of each soil layer are obtained by inversion from the in-situ static cone penetration test data, which serve as the basis for defining the initial geostress field and stiffness parameters of the model.

[0022] The pile foundation design parameters are used as model inputs, including pile diameter, pile length, pile wall thickness, pile driving rate, and final penetration elevation.

[0023] Set the infinitely far boundary around the pile as the fixed displacement boundary, the ground surface as the free drainage boundary, and the bottom of the model as the fixed or rolling support boundary according to the actual situation, thus completing the definition of the model boundary conditions.

[0024] Furthermore, by running the multi-concentric spatial evolution model, the distribution of excess pore water pressure, radial effective stress increment, and plastic zone development range of the soil surrounding the pile caused by the entire penetration process of the PHC pipe pile are calculated, including:

[0025] The large deformation problem is solved numerically using the updated Lagrange scheme to simulate the dynamic process of PHC pipe piles continuously penetrating the soil in a discrete incremental step manner.

[0026] In each incremental step, the equilibrium equations are solved based on the current displacement field, and the total stress of each soil element is updated.

[0027] Based on the modified equation for the dissipation and diffusion of pore water pressure, the generation of excess pore water pressure caused by soil volume compression and shearing and its redistribution over time are calculated in a coupled manner.

[0028] Based on the updated total stress and excess pore water pressure, the radial effective stress increment of each soil element is calculated.

[0029] Based on the yield function in the elastoplastic constitutive relation of the state-related dilatation theory, it is determined whether each soil element has entered the plastic state, and the range of the plastic zone is marked, thereby obtaining the development range of the plastic zone.

[0030] Furthermore, the step of comparing and verifying the distribution results calculated by the model with the sensor data in a spatiotemporal synchronization includes:

[0031] The sensor data collected by the soil pressure sensor and pore water pressure sensor buried at different radial distances and depths around the pile are obtained. The sensor data includes the measured soil pressure value and pore water pressure value at different penetration depths and time points after penetration.

[0032] The calculated earth pressure and calculated pore water pressure values ​​at the same spatial location and time node as the sensor are extracted from the multi-concentric spatial evolution model.

[0033] The measured and calculated excess pore water pressure distribution curves as a function of radial distance, depth, and dissipation over time were plotted respectively.

[0034] Plot the distribution curves of the measured and calculated radial effective stress increment as a function of radial distance, respectively.

[0035] Based on the plotted distribution curves, the coefficient of determination and root mean square error between the measured curve and the calculated curve are calculated as preliminary matching evaluation indicators.

[0036] Furthermore, the step of adjusting the key parameters of the multi-sphere spatial evolution model in reverse based on the verification results includes:

[0037] When the calculated value of excess pore water pressure systematically deviates from the measured value, the parameters controlling the soil volume compressibility and permeability in the model are adjusted in reverse. These parameters include the compression index, rebound index, and permeability coefficient.

[0038] When the calculated value of the radial effective stress increment systematically deviates from the measured value, the parameters controlling the soil dilatation characteristics and strength growth in the model are adjusted in reverse. These parameters include the critical state friction angle, dilatation coefficient, and hardening modulus.

[0039] When the calculated value of the plastic zone development range is inconsistent with the plastic zone characteristics derived from sensor data, the structural yield stress and damage evolution coefficient of the soil in the model are adjusted in reverse.

[0040] The reverse adjustment process follows a preset parameter sensitivity priority rule, prioritizing the adjustment of parameters that have the most significant impact on the matching degree index.

[0041] Furthermore, the iterative calculation until the matching degree between the model-calculated distribution result and the sensor-monitored distribution result reaches a preset threshold includes:

[0042] After completing a key parameter adjustment, the multi-concentric spatial evolution model is rerun to obtain a new round of calculation distribution results;

[0043] The new round of calculated distribution results are compared with the sensor data in a spatiotemporal synchronization to calculate new coefficients of determination and root mean square errors.

[0044] Determine if all new matching evaluation metrics are better than the preset thresholds; if so, stop the iteration.

[0045] If not, then based on the new round of deviation characteristics, the key parameters are adjusted again in a directional manner, and the process of running, comparing, and judging is repeated until the matching degree evaluation index meets the preset requirements or the maximum number of iterations is reached.

[0046] Furthermore, the final output, calibrated soil displacement effect analysis results, also includes:

[0047] Output the final spatial morphology and boundary of the plastic zone of the soil around the pile after calibration;

[0048] Output calibrated contour maps of the soil horizontal displacement field and the predicted surface heave or settlement.

[0049] Output the calibrated initial distribution cloud map of the excess pore water pressure at the moment of penetration completion and its dissipation cloud map at a specific time point after the project;

[0050] Outputs calibrated three-dimensional distribution data of radial and circumferential effective stress increments in the soil surrounding the pile.

[0051] Furthermore, before acquiring the multi-source geological and monitoring data of the target site, the process also includes:

[0052] Based on the site's engineering geological conditions and pile foundation design scheme, a sensor network deployment scheme is planned. The sensor network deployment scheme needs to determine the type, quantity, spatial location, and burial depth of earth pressure sensors, pore water pressure sensors, and deep soil displacement monitoring points.

[0053] According to the sensor network deployment scheme, the sensors and monitoring points are installed and buried on site to ensure good contact between the sensors and the soil and complete the initial reading calibration.

[0054] Establish an automated sensor data acquisition and wireless transmission system, and set the data acquisition frequency and triggering mechanism that are linked to the pile foundation construction progress.

[0055] Furthermore, the numerical solution of the multi-concentric spatial evolution model is implemented on a finite element computing platform, and the process of reverse adjustment of key parameters is automatically completed by an embedded optimization algorithm. The optimization algorithm automatically optimizes and adjusts the direction and step size according to the changes in the matching degree evaluation index.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] By forcibly fusing static geological data before construction, real-time operational data during construction, and dynamic response data from sensors embedded in the soil, and establishing a spatiotemporal synchronous comparison and verification process between model calculation results and monitoring data, a dynamic "calculation-verification-inversion-iteration" analysis closed loop is formed. This allows the analysis process to move beyond one-time parameter settings and calculations, enabling continuous model calibration using real data streams generated during construction. Model parameters and boundary conditions can be adjusted in reverse based on actual feedback, reducing prediction errors caused by spatial variability of soil and construction uncertainties. This allows the analysis results to dynamically track and conform to the actual construction conditions, improving the real-time performance and accuracy of soil squeezing effect prediction.

[0058] A multi-concentric physical model was constructed that explicitly incorporates the structural damage mechanism of soil and can simulate the spatial zoning evolution of stress and deformation fields during penetration, breaking through the traditional simplification of treating soil as a homogeneous medium. This model can describe the formation and dynamic development of the soil around the pile, from the fully remodeled zone close to the pile shaft, to the plastic shear zone on the outer side, and finally to the elastic influence zone. This modeling approach more precisely captures the non-uniform distribution characteristics of the soil squeezing effect in the radial and depth directions, as well as its dynamic history with construction progress. The model's output results, such as the distribution of excess pore water pressure, effective stress increment, and plastic zone range, have higher fidelity and engineering interpretability because their underlying physical mechanism is closer to reality. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the steps of the PHC pipe pile soil displacement effect analysis method based on multi-source data fusion described in this invention.

[0060] Figure 2 A flowchart for constructing a multi-sphere spatial evolution model;

[0061] Figure 3 A flowchart defining the initial parameters and boundary conditions of the model;

[0062] Figure 4 A verification diagram of the iterative matching degree of the PHC pipe pile soil squeezing effect model;

[0063] Figure 5 This is a graph showing the dissipation of excess pore water pressure over time and radial distance after PHC pipe pile driving. Detailed Implementation

[0064] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] See Figure 1This invention proposes a method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion. The specific implementation steps are as follows: Comprehensive perception and collection of target site information is achieved through engineering geological exploration, in-situ static cone penetration testing, real-time monitoring of pile foundation construction, and a pre-embedded sensor network to acquire multi-source geological and monitoring data covering geological characteristics, in-situ conditions, and construction responses. A multi-layered spatial evolution model considering structural damage of the soil is constructed to precisely characterize the complex mechanical behavior of the soil. This model aims to describe the spatial zoning and dynamic evolution of stress and deformation in the soil around the pile during PHC pipe pile penetration. Using the aforementioned multi-source data, the initial state parameters and physical boundary conditions of the model are precisely defined and numerically assigned. After initialization, the numerical model is run to dynamically simulate the entire process of pipe pile penetration and calculate and output key results such as the distribution of excess pore water pressure, radial effective stress increment, and the development range of the plastic zone in the soil around the pile caused by this process. The distribution results calculated by these models are synchronously compared and verified with the measured data collected by the pre-embedded sensors in both time and space dimensions. Based on the discrepancies identified through comparison, the key mechanical parameters affecting the accuracy of the results in the model were adjusted in reverse, and the model was rerun for calculation. Through multiple parameter adjustments and model calculation iterations, the matching degree between the model output results and the sensor monitoring data was continuously improved until a preset accuracy threshold was reached. Finally, highly reliable soil squeezing effect analysis results, rigorously calibrated with field data, were output.

[0066] See Figure 2 In one embodiment of the present invention, a multi-concentric spatial evolution model of soil structural damage is constructed based on an example scenario of driving a 0.6-meter diameter, 30-meter length PHC pipe pile in a soft clay stratum. The physical basis for the model construction comes from the comparative analysis of cone tip resistance, pore water pressure, and soil displacement data collected by a pre-embedded sensor array at different radial positions around the pile. The data shows that there are step differences in the mechanical response of the soil at different distances from the pile wall. Based on the significant differences in the strain path and stress state experienced by the soil under pile penetration, the soil is divided into a compression remodeling zone, a shear slip zone, a plastic deformation zone, and an elastic disturbance zone from the pile-soil interface outwards. In a specific implementation, the boundary of the compression remodeling zone is defined as a range of 1.2 times the pile diameter at a radial distance from the outer edge of the pile wall. Static cone penetration test data within this range show that the cone tip resistance fluctuates violently before stabilizing at a low value, and the comparison with the data of the undisturbed soil at a distance shows that the soil structure is completely destroyed.

[0067] In some embodiments, within the compression-remolding zone, the concepts of soil structure yield surface and damage factor are introduced, defining the soil within the compression-remolding zone as exhibiting a fully remolded state, whose constitutive behavior is controlled by remolded soil parameters. Damage factor This is used to quantify the degree of soil structure loss, and its value evolves from 0 (original state) to 1 (complete remodeling) according to the development of plastic shear strain. An optional damage evolution equation is expressed as:

[0068]

[0069] in: Represents damage factors, Represents material constants related to soil properties. This represents the accumulated plastic volumetric strain. When the damage factor... When the value reaches 1, the constitutive parameters of the soil element are completely switched from the undisturbed soil parameters to the remolded soil parameters determined by indoor tests.

[0070] It is understandable that within the shear slip zone and plastic deformation zone, an elastoplastic constitutive relation based on state-related dilatation theory is used to describe the plastic volume and strength changes of the soil under shear stress. The shear slip zone is defined as the area 1.2 to 3 times the pile diameter from the pile wall, where pore water pressure monitoring data shows peak characteristics. The plastic deformation zone is defined as 3 to 8 times the pile diameter, where soil pressure data shows a continuous increase in effective stress. In specific implementation, state parameters... The calculation is based on the difference between the current void ratio and the critical void ratio under the current stress state. The dilatation equation and the yield surface size are both related to this state parameter. Related.

[0071] In some embodiments, a nonlinear elastic model is used to describe the small recoverable deformations of the soil within the elastic disturbance zone. The elastic disturbance zone is defined as the area outside the plastic deformation zone, where deep displacement monitoring data shows that soil displacement decreases exponentially with increasing distance, and there is a slight rebound in displacement after construction. Optionally, the elastic modulus varies with depth and the current mean effective stress, and is assigned a value using an empirical relationship in the form of a power function. In specific implementations, continuous displacement and stress field equations connecting the above zones are established to ensure deformation compatibility and stress continuity at the boundaries of each zone, thereby forming a complete multi-concentric spatial evolution model. The displacement compatibility condition requires the displacement field to be continuous at the interfaces of different zones; the stress continuity condition requires the continuity of normal stress and shear stress at the interfaces. By introducing an interpolation function with transition characteristics or constructing a unified potential function, a smooth connection between the zone constitutive models is achieved within the finite element framework. It can be understood that the establishment and solution of this series of equations enable the multi-concentric spatial evolution model to continuously simulate the complete mechanical behavior spectrum of soil from complete failure to elastic disturbance, from the pile-soil interface to the far field.

[0072] See Figure 3In one embodiment of the present invention, based on an example scenario of a PHC pipe pile group project with a diameter of 0.8 meters and a length of 40 meters in a deep soft clay stratum along the coast, the initial parameters and boundary conditions of the multi-concentric spatial evolution model are defined and assigned. This scenario has detailed engineering geological exploration profiles and dense in-situ static penetration test borehole data. The planning of the sensor network layout scheme needs to be based on the site engineering geological conditions and the pile foundation design scheme. The pile foundation design scheme specifies the pile location layout and pile driving sequence. The engineering geological exploration profile reveals the distribution depth and thickness of the main soft clay layer and silt interlayer. The sensor network layout scheme needs to determine the type, number, spatial location and burial depth of earth pressure sensors, pore water pressure sensors and deep soil displacement monitoring points. For example, sensor arrays are arranged at radial distances of 1, 2, 4, and 8 times the pile diameter around the target pile and at different soil layer interfaces. Deep soil displacement monitoring points are arranged one measuring point every 2 meters along the depth direction. In some embodiments, sensors and monitoring points are installed and buried on-site according to the sensor network deployment scheme. The drilling and burying method is used to install earth pressure sensors and pore water pressure sensors to ensure good contact between the sensor diaphragm and the surrounding soil. The initial reading calibration of the sensors is completed by the graded loading method. An automated sensor data acquisition and wireless transmission system is established. The system sets the data acquisition frequency and triggering mechanism that are linked to the pile foundation construction progress. For example, it automatically switches to high-frequency acquisition mode when the pile driver starts and switches to low-frequency monitoring mode during the pile driving interval.

[0073] It is understandable that soil profiles, initial densities, natural water content, initial void ratios, and undrained shear strength parameters of each soil layer are extracted from engineering geological exploration data and assigned to corresponding soil units in a multi-concentric spatial evolution model. The soil profiles are divided into five main soil layers based on borehole data and laboratory geotechnical test results. The initial density, natural water content, initial void ratio, and undrained shear strength parameters of each soil layer are assigned as constants or functions varying with depth to the corresponding soil units in the model, on a layer-by-layer basis. In specific implementation, the in-situ static earth pressure coefficient, compression modulus, and sensitivity of each soil layer are inverted from in-situ static cone penetration test data, serving as the basis for defining the initial geostress field and stiffness parameters of the model. The static cone penetration test data includes the continuous curve of cone tip resistance with depth and the sidewall friction curve, and soil parameters are inverted through empirical relationships. An optional formula for inverting the in-situ static earth pressure coefficient from static cone penetration test data is expressed as follows:

[0074]

[0075] in: Represents the coefficient of in-situ earth pressure at rest. This represents the resistance at the tip of the static cone probe. Atmospheric pressure is used as a reference value. , and This represents site-specific inversion parameters obtained through limited indoor testing or regional experience. Optionally, the compressive modulus is expressed as a linear relationship. Inversion, in which The sensitivity is calculated using the ratio of the undisturbed shear strength in the undisturbed state to that in the remodeled state, which is an empirical coefficient.

[0076] In some embodiments, pile foundation design parameters are used as model inputs. These parameters include pile diameter, pile length, pile wall thickness, pile driving rate, and final penetration elevation. These parameters are obtained directly from construction drawings and pile driving records. For example, the pile driving rate is determined to be 0.5 meters per minute based on the impact records of the hydraulic pile hammer. The infinitely far boundary around the pile is set as a fixed displacement boundary, which is achieved in the finite element model by setting the outer boundary of the model at a sufficiently far distance and constraining displacement in all directions. The ground surface is set as a free drainage boundary, allowing pore water pressure to dissipate to atmospheric pressure at this boundary. The bottom of the model is set as a fixed or rolling support boundary depending on the actual situation. When the exploration reveals the presence of hard rock layers below, it is set as a fixed support boundary, constraining displacement in all directions. When the lower part is a deep soft soil layer, it is set as a rolling support boundary, constraining only vertical displacement, thus completing the definition of the model boundary conditions. It can be understood that this series of data extraction, inversion, and assignment operations provide the multi-concentric spatial evolution model with an initial mechanical field and geometric boundary that reflects the actual geological conditions and construction status on site.

[0077] In one embodiment of the invention, a multi-concentric spatial evolution model is run and a verification and comparison process is implemented. This is based on a pile driving example of a 0.8-meter diameter PHC pipe pile with a final penetration depth of 25 meters in silty clay strata. In this example, a complete sensor array is deployed at radial distances of 1, 2, 4, and 8 times the pile diameter around the pile, and at depth intervals of 5 meters. The multi-concentric spatial evolution model is run to calculate the distribution of excess pore water pressure, radial effective stress increment, and plastic zone development range in the soil around the pile caused by the entire PHC pipe pile penetration process. This first requires numerically solving the large deformation problem using an updated Lagrangian scheme to simulate the dynamic process of the PHC pipe pile continuously penetrating the soil in discrete incremental steps. In the finite element analysis, the entire 25-meter penetration depth is divided into 500 incremental steps. Each incremental step simulates the pile penetrating downwards by 5 centimeters. In each incremental step, the equilibrium equation is solved based on the current displacement field, and the total stress of each soil element is updated. In some embodiments, in each incremental step, the generation and redistribution of excess pore water pressure caused by soil volume compression and shear are coupled and calculated based on the modified pore water pressure dissipation and diffusion equation. The calculation process considers the anisotropy of soil permeability and the hysteresis effect of pore water pressure diffusion. A modified formula for describing the generation of excess pore water pressure may be:

[0078]

[0079] in: This represents the increase in excess pore water pressure caused by volumetric compression and shear. Represents the Scunpton pore water pressure coefficient. Represents the average effective stress increment. Represents material parameters related to soil dilatation. Represents the volume strain in the current increment step. The representative strain is used. Based on the updated total stress and the calculated excess pore water pressure, the radial effective stress increment of each soil element can be calculated.

[0080] It is understandable that, based on the yield function in the elastoplastic constitutive relation of state-related dilatation theory, it is possible to determine whether each soil element has entered the plastic state and mark the extent of the plastic zone, thereby obtaining the development range of the plastic zone. The yield function is based on the modified Cambridge model framework, but the size of the yield surface and the dilatation equation are related to the current void ratio through state parameters. The distribution results calculated by the model are then compared and verified spatiotemporally with sensor data. This process requires acquiring sensor data from earth pressure sensors and pore water pressure sensors embedded at different radial distances and depths around the pile. The sensor data includes measured earth pressure and pore water pressure values ​​at different penetration depths and post-penetration time points. For example, when the pile tip penetrates to a depth of 15 meters, the real-time readings of the sensor at a radial distance of twice the pile diameter and a depth of 12 meters are recorded. In specific implementation, the calculated earth pressure and pore water pressure values ​​at the same spatial location and time point as the sensors are extracted from the multi-concentric spatial evolution model. This requires the numerical model to output results at specified coordinate points and for specified calculation steps (corresponding to penetration depth and time). The measured and calculated excess pore water pressure distribution curves as a function of radial distance, depth, and dissipation over time are plotted separately. Similarly, the measured and calculated radial effective stress increment distribution curves as a function of radial distance are plotted separately. For example, after the pile has fully penetrated, the measured and calculated curves of the radial distribution of excess pore water pressure at a depth of 10 meters are plotted. Based on the plotted distribution curves, the coefficient of determination and root mean square error (RMSE) between the measured and calculated curves are calculated as preliminary matching indicators. The coefficient of determination is used to assess trend consistency, and the RMS error is used to quantify the magnitude of absolute deviation. In some embodiments, this comparison is dynamic, spanning the entire pile driving process and the pore water pressure dissipation process for a period after pile driving. Optionally, data synchronization accuracy is ensured by aligning the real-time penetration depth monitoring signal of the pile driver with the timestamp of the sensor data acquisition system. It is understood that the above plotting and calculation processes are automated by writing post-processing scripts, enabling batch processing of massive amounts of sensor data and model output data, achieving efficient quantitative comparison.

[0081] In one embodiment of the invention, key parameters of the multi-concentric spatial evolution model are adjusted in reverse and iterative calculations are performed based on the verification results. This process is based on an example of a 0.8-meter diameter PHC pipe pile being driven in silty clay strata. The peak excess pore water pressure calculated by the preliminary model is generally underestimated by about 15% compared to the sensor-measured values, while the calculated values ​​of radial effective stress increment in the shear slip zone are systematically higher than the measured values. The reverse adjustment process follows a preset parameter sensitivity priority rule, prioritizing the adjustment of parameters that have the most significant impact on the matching degree evaluation index. The parameter sensitivity priority is determined through previous single-parameter perturbation analysis. When the calculated value of excess pore water pressure systematically deviates from the measured value, the parameters controlling soil volume compressibility and permeability in the model are adjusted in reverse. These parameters include the compression index, rebound index, and permeability coefficient. When the calculated value of radial effective stress increment systematically deviates from the measured value, the parameters controlling soil dilatation characteristics and strength growth in the model are adjusted in reverse. These parameters include the critical state friction angle, dilatation coefficient, and hardening modulus. When the calculated value of the plastic zone development range is inconsistent with the plastic zone characteristics derived from sensor data, the structural yield stress and damage evolution coefficient of the soil in the model are adjusted in reverse. In some embodiments, the priority and magnitude of parameter adjustments are referenced to a pre-established parameter-response relationship lookup table, see Table 1, which clarifies the relative order of the influence of different parameters on excess pore water pressure, effective stress, and plastic zone range.

[0082] Table 1: Priority and Example Directions for Reverse Adjustment of Key Parameters

[0083] Deviation phenomenon Prioritize adjusting parameters Example of parameter adjustment direction Priority The calculated values ​​of excess pore water pressure are generally too low. Compression Index Increase high Excess pore water pressure dissipates too slowly Permeability coefficient Increase middle The calculated value of radial effective stress increment is too high. Critical friction angle Decrease high The calculated value of radial effective stress increment is too high. shear dilatation coefficient Decrease high The calculated range of the plastic zone is too small. Structural yield stress Decrease middle

[0084] It is understandable that after completing a key parameter adjustment, the multi-sphere spatial evolution model is rerun to obtain a new round of calculated distribution results. These new results are then compared spatiotemporally with the sensor data to calculate new coefficients of determination and root mean square errors (RMSEs). It is determined whether all new matching indicators are better than preset thresholds, which could be set as a coefficient of determination greater than 0.85 and an RMSE less than 15 kPa. If so, the iteration stops. If not, based on the new round of deviation characteristics, the key parameters are again directionally adjusted, and the process of running the model, comparing results, and judging matching is repeated until the matching indicators meet preset requirements or the maximum number of iterations is reached. In some embodiments, an adaptive formula for parameter adjustment is used to guide the directional adjustment process, and the formula is as follows:

[0085]

[0086] in: This represents the adjustment amount of parameter θ in the (k+1)th iteration. Represents the learning rate factor. The sign representing the sensitivity coefficient J. This represents the difference between the root mean square error (RMSE) of the current iteration step and the target threshold. This formula allows the adjustment amount to adaptively change according to the error magnitude and the direction of parameter sensitivity. The numerical solution of the multi-concentric spatial evolution model is implemented on a finite element analysis (FEM) platform, and the process of reverse adjustment of key parameters is automatically completed through an embedded optimization algorithm. Optionally, the optimization algorithm adopts a Bayesian optimization framework, which constructs a surrogate model based on the parameter combinations and corresponding matching evaluation indicators (coefficient of determination, RMS error) in historical iterations to predict the matching performance of the untried parameter space and automatically optimizes the adjustment direction and step size of the next set of key parameters to be tried. It can be understood that the entire reverse adjustment and iterative calculation process is automatically executed under the coupling of the finite element platform and the optimization algorithm until the output calibration result meets the preset matching threshold or the maximum number of iterations is reached, at which point it terminates.

[0087] See Figure 4 This is a graph verifying the iterative matching degree of the PHC pipe pile soil displacement effect model, showing the relationship between the number of iterations and the changes in model accuracy indicators. Both indicators simultaneously meet the standards in the 4th iteration, indicating that the model has met the preset accuracy requirements at this point. This graph visually reflects the parameter iterative calibration process of the multi-concentric model of the PHC pipe pile soil displacement effect. It intuitively displays the speed and accuracy changes of iterative convergence, helping engineers quickly locate the model's compliance nodes, avoiding excessive iterations that waste computational resources, and preventing insufficient iterations that lead to insufficient model accuracy. The synchronous changes in R² and RMSE quantify the improvement effect of key parameter adjustments on model accuracy, providing a reusable reference for parameter optimization in subsequent similar projects. The changes in the slope of the curves can be used to infer the sensitivity differences in parameter adjustments at different stages, providing data support for further optimization of parameter adjustment priority rules.

[0088] In one embodiment of the present invention, the final output is a calibrated analysis result of the soil squeezing effect. This implementation is based on an example of driving a 1.0-meter diameter, 50-meter long PHC pipe pile in a soft clay foundation. After five rounds of iterative calibration, the multi-concentric spatial evolution model has met the preset thresholds for all matching degree evaluation indicators. The final spatial morphology and boundary of the plastic zone of the soil around the pile after calibration are output. This output is provided in the form of a three-dimensional geometric model file or a set of boundary coordinates on a specific profile. For example, on a two-dimensional profile with depth as the ordinate and radial distance as the abscissa, the boundary line between the plastic zone and the elastic zone is clearly marked. The boundary line is formed by a series of discrete points connected together, and its functional relationship can be expressed as the correspondence between depth and radial distance. In some embodiments, the output is a calibrated contour map of the horizontal displacement field of the soil and the predicted contour map of surface heave or settlement. The horizontal displacement field contour map shows the displacement distribution of soil layers at different depths in the horizontal direction, while the surface deformation contour map shows the range and magnitude of surface heave or settlement centered on the pile location. The contour map is generated based on all nodal displacement data calculated by the finite element model and drawn by an interpolation algorithm.

[0089] The system outputs calibrated initial distribution cloud maps of excess pore water pressure at the moment of penetration completion and dissipation cloud maps at specific post-construction time points. The initial distribution cloud map shows the spatial distribution of excess pore water pressure immediately after pile driving, while the dissipation cloud map shows the residual excess pore water pressure distribution at specific time points such as 24 hours, 7 days, and 30 days post-construction. The cloud maps use a color spectrum from warm to cool colors to represent the pressure value from high to low, and display the pressure distribution with depth and radial distance in the form of a profile. The system also outputs calibrated three-dimensional distribution data of radial and circumferential effective stress increments in the soil surrounding the pile. This distribution data is stored in a structured array or database format, containing the coordinate information of each calculation unit and its corresponding radial and circumferential effective stress increment values. In some embodiments, for ease of engineering application, the effective stress increment distribution data can be further processed to output a curve data table of stress increment radial distribution at specific depths (such as the pile tip plane or the middle plane of the pile body). Optionally, all output results are integrated into a visualization post-processing platform, supporting users to interactively query analysis results at any location.

[0090] In practical implementation, the calibrated plastic zone boundary can be parameterized using a boundary function to facilitate reference as an empirical formula in other similar projects. A simplified formula for describing the maximum radial influence range of the plastic zone in a specific soil layer can be selected as follows:

[0091]

[0092] in: Represents depth Radial distance from the outer boundary of the plastic zone Represents the diameter of the pipe pile. , and This represents site-specific and soil-specific parameters obtained through calibration analysis. It is understood that predicted contour maps of surface uplift or settlement can be output in a common geographic information format for direct import into construction monitoring systems for comparison with measured topographic change data. Optionally, a sequence of excess pore water pressure dissipation cloud maps can be synthesized into an animation to dynamically demonstrate the process of excess pore water pressure dissipating over time. In some embodiments, the three-dimensional distribution data of radial and circumferential effective stress increments can be further used to calculate the theoretical value of pile side friction distribution, providing input for pile foundation bearing capacity assessment.

[0093] See Figure 5 This is a graph showing the dissipation of excess pore water pressure over time and radial distance after PHC pipe pile driving. It clearly demonstrates the decay process of excess pore water pressure at different radial distances around the pile in silty clay strata over time. At any given time point, the excess pore water pressure decreases rapidly with increasing radial distance around the pile. At the completion of penetration, the peak excess pore water pressure near the pile can reach 500 kPa; at a radial distance of 5 m, it has almost dissipated to 0 kPa. This reflects the limited spatial range of the soil squeezing effect, mainly concentrated within a 3 m radius around the pile. This can be used to determine a reasonable pile driving interval, avoiding the risk of pile misalignment or settlement of surrounding buildings due to the superposition of excess pore water pressure. It provides direct evidence for judging the effective stress growth and strength recovery of the soil around the pile, guiding the safety window period for foundation pit excavation and surrounding construction.

[0094] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion, characterized in that, include: Acquire multi-source geological and monitoring data of the target site, including engineering geological exploration data, in-situ static cone penetration test data, real-time monitoring data of pile foundation construction, and sensor data embedded in the soil around the pile. A multi-concentric spatial evolution model considering soil structural damage is constructed. This multi-concentric spatial evolution model is used to describe the spatial partitioning and evolution of the stress state and deformation field of the soil around the pile during the penetration of PHC pipe piles. The initial parameters and boundary conditions of the multi-sphere spatial evolution model are defined and assigned based on the multi-source geological and monitoring data. By running the multi-concentric spatial evolution model, the distribution of excess pore water pressure, radial effective stress increment, and plastic zone development range of the soil around the pile caused by the entire process of PHC pipe pile penetration were calculated. The distribution results calculated by the model are compared and verified with the sensor data in a spatiotemporal synchronization. Based on the verification results, the key parameters of the multi-sphere spatial evolution model are adjusted in reverse, and the calculation is iterated until the matching degree between the model calculation distribution results and the sensor monitoring distribution results reaches a preset threshold. Finally, the calibrated soil squeezing effect analysis results are output. The construction of the multi-concentric spatial evolution model considering soil structural damage includes: Based on the strain path and stress state experienced by the soil under the penetration of the pile, the soil is divided into the compression and remodeling zone, shear slip zone, plastic deformation zone and elastic disturbance zone from the pile-soil interface outward. Within the compression and remodeling zone, the concepts of soil structure yield surface and damage factor are introduced, and the soil within the compression and remodeling zone is defined as exhibiting a fully remodeled state, whose constitutive behavior is controlled by remodeled soil parameters. Within the shear slip zone and plastic deformation zone, an elastoplastic constitutive relation based on the state-related dilatation theory is used to describe the plastic volume change and strength change of the soil under shear stress. Within the elastic disturbance zone, a nonlinear elastic model is used to describe the small recoverable deformations of the soil. Establish continuous displacement field and stress field equations connecting the above partitions to ensure deformation coordination and stress continuity at the boundaries of each partition, thereby forming a complete multi-concentric spatial evolution model. The pile foundation design parameters are used as model inputs, including pile diameter, pile length, pile wall thickness, pile driving rate, and final penetration elevation. Set the infinitely far boundary around the pile as the fixed displacement boundary, the ground surface as the free drainage boundary, and the bottom of the model as the fixed or rolling support boundary according to the actual situation, thus completing the definition of the model boundary conditions.

2. The method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion according to claim 1, characterized in that, The definition and assignment of initial parameters and boundary conditions for the multi-sphere spatial evolution model based on the multi-source geological and monitoring data includes: The soil profile, initial density, natural water content, initial void ratio and undrained shear strength parameters of each soil layer are extracted from the engineering geological exploration data of the target site and assigned to the corresponding soil units in the multi-concentric spatial evolution model. The in-situ static earth pressure coefficient, compression modulus, and sensitivity of each soil layer are obtained from the in-situ static cone penetration test data, which serve as the basis for defining the initial geostress field and stiffness parameters of the model.

3. The method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion according to claim 2, characterized in that, The multi-concentric spatial evolution model was used to calculate the distribution of excess pore water pressure, radial effective stress increment, and plastic zone development range in the soil surrounding the pile during the entire PHC pipe pile penetration process, including: The large deformation problem is solved numerically using the updated Lagrange scheme to simulate the dynamic process of PHC pipe piles continuously penetrating the soil in a discrete incremental step manner. In each incremental step, the equilibrium equations are solved based on the current displacement field, and the total stress of each soil element is updated. Based on the modified equation for the dissipation and diffusion of pore water pressure, the generation of excess pore water pressure caused by soil volume compression and shearing and its redistribution over time are calculated in a coupled manner. Based on the updated total stress and excess pore water pressure, the radial effective stress increment of each soil element is calculated. Based on the yield function in the elastoplastic constitutive relation of the state-related dilatation theory, it is determined whether each soil element has entered the plastic state, and the range of the plastic zone is marked, thereby obtaining the development range of the plastic zone.

4. The method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion according to claim 3, characterized in that, The step of comparing and verifying the distribution results calculated by the model with the sensor data in a spatiotemporal synchronization includes: The sensor data collected by the soil pressure sensor and pore water pressure sensor buried at different radial distances and depths around the pile are obtained. The sensor data includes the measured soil pressure value and pore water pressure value at different penetration depths and time points after penetration. The calculated earth pressure and calculated pore water pressure values ​​at the same spatial location and time node as the sensor are extracted from the multi-concentric spatial evolution model. The measured and calculated excess pore water pressure distribution curves as a function of radial distance, depth, and dissipation over time were plotted respectively. Plot the distribution curves of the measured and calculated radial effective stress increment as a function of radial distance, respectively. Based on the plotted distribution curves, the coefficient of determination and root mean square error between the measured curve and the calculated curve are calculated as preliminary matching evaluation indicators.

5. The method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion according to claim 4, characterized in that, The key parameters of the multi-sphere spatial evolution model are adjusted in reverse based on the verification results, including: When the calculated value of excess pore water pressure systematically deviates from the measured value, the parameters controlling the soil volume compressibility and permeability in the model are adjusted in reverse. These parameters include the compression index, rebound index, and permeability coefficient. When the calculated value of the radial effective stress increment systematically deviates from the measured value, the parameters controlling the soil dilatation characteristics and strength growth in the model are adjusted in reverse. These parameters include the critical state friction angle, dilatation coefficient, and hardening modulus. When the calculated value of the plastic zone development range is inconsistent with the plastic zone characteristics derived from sensor data, the structural yield stress and damage evolution coefficient of the soil in the model are adjusted in reverse. The reverse adjustment process follows a preset parameter sensitivity priority rule, prioritizing the adjustment of parameters that have the most significant impact on the matching degree index.

6. The method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion according to claim 5, characterized in that, The iterative calculation continues until the matching degree between the model's calculated distribution result and the sensor's monitored distribution result reaches a preset threshold, including: After completing a key parameter adjustment, the multi-concentric spatial evolution model is rerun to obtain a new round of calculation distribution results; The new round of calculated distribution results are compared with the sensor data in a spatiotemporal synchronization to calculate new coefficients of determination and root mean square errors. Determine if all new matching evaluation metrics are better than the preset thresholds; if so, stop the iteration. If not, then based on the new round of deviation characteristics, the key parameters are adjusted again in a directional manner, and the process of running, comparing, and judging is repeated until the matching degree evaluation index meets the preset requirements or the maximum number of iterations is reached.

7. The method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion according to claim 6, characterized in that, The final output, calibrated analysis results of the soil squeezing effect, also includes: Output the final spatial morphology and boundary of the plastic zone of the soil around the pile after calibration; Output calibrated contour maps of the soil horizontal displacement field and the predicted surface heave or settlement. Output the calibrated initial distribution cloud map of the excess pore water pressure at the moment of penetration completion and its dissipation cloud map at a specific time point after the project; Outputs calibrated three-dimensional distribution data of radial and circumferential effective stress increments in the soil surrounding the pile.

8. The method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion according to claim 1, characterized in that, Before acquiring the multi-source geological and monitoring data of the target site, the following steps are also included: Based on the site's engineering geological conditions and pile foundation design scheme, a sensor network deployment scheme is planned. The sensor network deployment scheme needs to determine the type, quantity, spatial location, and burial depth of earth pressure sensors, pore water pressure sensors, and deep soil displacement monitoring points. According to the sensor network deployment scheme, the sensors and monitoring points are installed and buried on site to ensure good contact between the sensors and the soil and complete the initial reading calibration. Establish an automated sensor data acquisition and wireless transmission system, and set the data acquisition frequency and triggering mechanism that are linked to the pile foundation construction progress.

9. The method for analyzing the soil squeezing effect of PHC pipe piles based on multi-source data fusion according to claim 1, characterized in that, The numerical solution of the multi-concentric spatial evolution model is realized on the finite element computing platform. The process of reverse adjustment of key parameters is automatically completed by the embedded optimization algorithm. The optimization algorithm automatically seeks optimization and adjusts the direction and step size according to the change of matching degree evaluation index.

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