A digital twin system for the entire pile foundation construction process

Abstract

The present invention relates to the field of digital twin technology, specifically a digital twin system for the entire process of pile foundation construction, the system comprising: a twin configuration module, a dynamic mapping module, a coupling analysis module, and a twin synchronization module. In the present invention, a time axis driven model sequence is constructed based on spatiotemporal interpolation, the buried depth coordinates, the grouting time and the segment number are dynamically bound, the three-dimensional evolution mapping of the pile model is realized, the axis coordinate deviation is combined with the verticality threshold extraction, the virtual model coordinates are corrected in real time through point cloud registration, the pump pressure value and the settlement amount are integrated to generate a pile sinking rate index, the two-way coupling relationship between the parameters and the formation response is established in combination with the formation reaction force and the injection rate, the model thickness is adjusted according to the settlement rate difference, the injection rate and the reaction force curve similarity are matched, and a model parameter reverse update mechanism is formed to realize real-time intervention of construction anomalies, dynamic optimization of parameter coupling and iterative correction of the model, thereby improving process controllability and quality traceability accuracy.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a digital twin system for the entire process of pile foundation construction. Background Art

[0002] The field of digital twin technology encompasses the construction of virtual images, real-time interaction, and state mapping for physical entities and their operational processes. The core of this technology lies in utilizing cyber-physical fusion systems to construct mapping relationships that combine the virtual and the real. This technology leverages digital tools such as the Internet of Things (IoT), big data, modeling and simulation, and edge computing to enable multi-dimensional, multi-layered virtual representations of real-world systems. The overall architecture of digital twin technology encompasses the collection of physical entity data, the establishment of virtual models, a two-way interactive communication mechanism, and data-driven real-time mapping and predictive analysis capabilities. It is widely used in various industries, including industrial manufacturing, smart cities, and construction engineering, to model, track, evaluate, and optimize physical processes.

[0003] The digital twin system for the entire pile foundation construction process refers to a digital system used in building foundation engineering to continuously model and fuse data for pile foundation construction by combining multi-source sensor data acquisition technology, 3D modeling methods, and construction process information analysis mechanisms. This system uses construction process video analysis, sensor data calibration, and 3D spatial reconstruction to establish a time-series data model for the entire construction process, targeting key construction phases such as pile foundation drilling, reinforcement cage fabrication and hoisting, and concrete pouring. This system integrates construction equipment operation trajectories, engineering parameter change records, and environmental status data to construct a virtual construction mapping that reflects the logical progress of pile foundation construction, forming a model structure that dynamically corresponds to full-process data and physical operations.

[0004] Existing technologies rely on periodic data collection and one-way model mapping, and virtual model updates lag behind the physical construction process. Multi-source sensor data is stored independently, and a temporal correlation framework between the cage segment number and the pouring timestamp has not been established, resulting in insufficient spatial matching accuracy between the lifting trajectory and the concrete pouring process. Verticality judgment uses a fixed threshold and lacks a coordinate correction mechanism based on real-time point cloud data. When the pile foundation drilling process encounters geological mutations, cumulative deviations are likely to occur. Construction parameters and stratum reaction force data do not form a dynamic weighted fusion relationship, and the adjustment of the pouring rate relies on manual experience. There is an asynchronous delay between the model correction instructions and physical operations. The existing virtual construction mapping body lacks a reverse feedback path, and the model thickness parameters cannot be dynamically iterated based on the settlement rate difference. Under complex working conditions, it is easy to cause axis offset and structural stability risks. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a digital twin system for the entire process of pile foundation construction.

[0006] To achieve the above objectives, the present invention adopts the following technical solutions: A digital twin system for the entire pile foundation construction process includes:

[0007] The twin configuration module is used to generate a virtual pile segment model based on the burial depth coordinate sequence and the concrete pouring timestamp. It calls the spatiotemporal interpolation algorithm to construct a time axis driven model sequence, binds the reinforcement cage segment number and the timestamp, establishes a dynamic mapping relationship, generates the virtual pile segment model, and passes it to the dynamic mapping module.

[0008] A dynamic mapping module is used to extract the axis deviation value of the virtual pile segment model, calculate the Euclidean distance in a unified coordinate unit, call the verticality threshold to determine abnormal nodes, input the point cloud registration algorithm to correct the coordinates, generate a dynamic topological twin, and pass it to the coupling analysis module;

[0009] The coupling analysis module is used to receive the dynamic topological twin, solve the theoretical value of the formation reaction force and calculate the pile driving rate index, dynamically weightedly fuse the injection rate and the formation reaction force measurement value to generate the injection coupling parameter, execute the dynamic weighted fusion algorithm to generate the construction feedback deviation index, trigger the twin correction instruction and pass it to the twin synchronization module.

[0010] As a further solution of the present invention, the spatiotemporal interpolation algorithm performs hash matching on the reinforcement cage segment number and the timestamp, so that the timestamp label of the model attribute node is updated synchronously with the construction pouring progress;

[0011] The verticality threshold is 5% of the pile diameter and is verified by field pile test data;

[0012] The dynamic weighted fusion algorithm superimposes two types of parameters through normalized linear product, so that the error between the construction feedback deviation index and the actual deformation of the pile body is less than 3%.

[0013] As a further solution of the present invention, the twin configuration module includes:

[0014] The coordinate time series processing submodule obtains the depth coordinate sequence and concrete pouring timestamps, spatially matches the longitude, latitude, and elevation components of the coordinate points with the timestamps, calculates the difference between adjacent timestamps based on the moving speed of the pouring machinery, and integrates the spatial nodes and difference parameters to generate a segmented coordinate set for the pile body.

[0015] The spatiotemporal interpolation modeling submodule divides the three-dimensional grid cells based on the node distribution density of the pile segment coordinate set, constructs a time axis increment sequence for the uncovered area using the Lagrangian interpolation method, corrects the continuity of the grid vertex coordinates according to the timestamp labels bound to the steel cage, and outputs a time axis driving parameter group;

[0016] The reinforcement cage time binding submodule extracts the reinforcement cage segment number from the time axis driving parameter group, maps the segment number to the segment interval corresponding to the timestamp, and uses a hash matching algorithm to associate the segment number with the timestamp label of the model attribute node to generate a virtual pile segment model;

[0017] The spatial hierarchical matching is achieved by establishing a real-time association between the longitude and latitude coordinates and the GNSS positioning data of the construction machinery.

[0018] As a further solution of the present invention, the dynamic mapping module includes:

[0019] The axis deviation extraction submodule obtains the axis coordinate sequence of the virtual pile segment model, decomposes the longitude, latitude, and elevation components of the model nodes, extracts the coordinate values ​​corresponding to the theoretical axis, calculates the absolute value of the difference between the actual coordinates and the theoretical coordinates using the Euclidean distance formula, and generates the axis coordinate deviation value;

[0020] The verticality check submodule calls the axial verticality threshold based on the axis coordinate deviation value, compares the deviation value with the threshold item by item in node order, marks the node spatial index whose deviation value exceeds the axial verticality threshold, and generates an abnormal node identification set;

[0021] The point cloud correction submodule calls the point cloud registration algorithm to process the abnormal node identification set, extracts the coordinate offset of the abnormal node, matches the spatial distribution of the abnormal point with the theoretical point cloud through the iterative nearest point algorithm, and introduces the pile elastic deformation parameter to constrain the coordinate correction range to generate a dynamic topological twin.

[0022] As a further solution of the present invention, the theoretical axis coordinate value is obtained by calculating the average value of the pile center coordinates and the construction layout point coordinates in the design drawing.

[0023] As a further solution of the present invention, the coupling analysis module includes:

[0024] The ground reaction force solver module receives the dynamic topological twin, decomposes the pile diameter, pile length, and material elastic modulus in the twin structure parameters, applies the Winkler foundation model to establish a linear equation for ground reaction force and pile displacement, solves the theoretical value of the superposition of pile end resistance and side friction resistance, and generates a theoretical value of ground reaction force;

[0025] The pile driving rate calculation submodule uses real-time pump pressure sensor data and laser rangefinder settlement data to convert the pump pressure value into a pile end pressure component in kilonewtons and unify it with the settlement value to a second-level time reference. Based on the theoretical value of the formation reaction force, the equilibrium equation between the pile driving resistance and the pump pressure is constructed. The forward difference method is used to calculate the settlement increment per unit time and output the pile driving rate index.

[0026] Based on the pile driving rate index and the pressure sensor measurement, the dynamic fusion feedback submodule sets the injection rate weight coefficient as the inverse of the pump pressure change rate and the formation reaction force weight coefficient as the inverse of the measurement fluctuation variance. The two types of parameters are subjected to normalized linear product superposition through the dynamic weighted fusion algorithm to generate a construction feedback deviation index and trigger the twin correction instruction.

[0027] As a further solution of the present invention, the base bed coefficient of the Winkler foundation model is determined by inversion of on-site static penetration test data.

[0028] As a further embodiment of the present invention, the system further comprises:

[0029] The twin synchronization module is used to adjust the thickness parameters of the virtual pile segment model based on the twin correction instruction, update the thickness according to the settlement rate difference, match the injection rate and reaction force measurement curve morphology similarity, generate updated twin parameters and synchronize them back to the dynamic mapping module and coupling analysis module.

[0030] As a further solution of the present invention, the updated twin parameters specifically refer to the adjusted pile segment thickness parameters and material layer parameters;

[0031] The morphological similarity is obtained by calculating the sum of squares of the slope differences of the two curves at adjacent time nodes, and screening nodes with a difference greater than 0.5 for weight ratio adjustment.

[0032] As a further solution of the present invention, the twin synchronization module includes:

[0033] The thickness parameter adjustment submodule calls the twin correction instruction, extracts the initial thickness parameter of the virtual pile segment model, and generates a thickness correction parameter according to the inverse proportional relationship between the thickness adjustment coefficient of the correction instruction and the pile sinking rate index;

[0034] The sedimentation rate update submodule calculates the absolute value of the difference between the actual sedimentation rate and the theoretical rate based on the thickness correction parameter, and uses the cubic spline interpolation method to distribute the difference to the corresponding model nodes according to the length of the time segment to generate a dynamic thickness update value;

[0035] The curve morphology matching submodule obtains the time domain sampling point sequence of the injection rate curve and the reaction force measurement curve, calculates the sum of the squares of the slope differences of the two curves at multiple time nodes, filters out the nodes that exceed the morphology similarity threshold, redistributes the weight ratio of the dynamic thickness update value, and generates updated twin parameters.

[0036] Compared with the prior art, the advantages and positive effects of the present invention are:

[0037] In the present invention, a time axis driven model sequence is constructed based on the spatiotemporal interpolation algorithm, and the buried depth coordinates, pouring timestamps and reinforcement cage segment numbers are dynamically bound to realize the continuous evolution mapping of the pile segment model in three-dimensional space. The axis coordinate deviation extraction is combined with the verticality threshold judgment mechanism, and the virtual model is corrected in real time through the point cloud registration algorithm to solve the immediate calibration needs of sudden offsets during construction. The real-time pump pressure value and settlement data are fused to generate the pile sinking rate index, and the dynamic weighted parameters of the formation reaction force measurement and the injection rate are synchronously integrated to establish a two-way coupling relationship between the construction parameters and the formation response. The model thickness parameters are adjusted based on the settlement rate difference, and the injection rate and reaction force measurement curve morphology similarity are reversely matched to form a reverse synchronous update mechanism for the virtual model parameters, ensuring a dynamic closed loop of physical operation and data feedback. This processing logic breaks through the limitations of traditional single-stage static modeling, transforms discrete construction links into continuous dynamic mapping, realizes real-time intervention in construction anomalies, dynamic optimization of parameter coupling and reverse iterative correction of the model, and significantly improves the controllability of the construction process and the accuracy of quality traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a system flow chart of the present invention;

[0039] Figure 2 This is a flow chart of the twin configuration module of the present invention;

[0040] Figure 3 This is a flow chart of the dynamic mapping module of the present invention;

[0041] Figure 4 This is a flow chart of the coupling analysis module of the present invention;

[0042] Figure 5 This is the flow chart of the twin synchronization module of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0045] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0046] Example 1

[0047] See also Figure 1 , a digital twin system for the entire pile foundation construction process includes:

[0048] The twin configuration module is used to generate a segmented virtual pile model using a sequence of buried depth coordinates and concrete pouring timestamps. It then uses a spatiotemporal interpolation algorithm to construct a timeline-driven model sequence, binds the reinforcement cage segment numbers and timestamps to model attributes, and establishes a dynamic mapping relationship between the segmented model and the construction schedule. The generated segmented virtual pile model is then passed to the dynamic mapping module.

[0049] The dynamic mapping module is used to extract the axis coordinate deviation value based on the virtual pile segment model, convert the elevation component and plane coordinates into meters, calculate the Euclidean distance, call the verticality threshold to identify abnormal nodes, input the point cloud registration algorithm to correct the coordinates, generate a dynamic topological twin, and pass it to the coupling analysis module;

[0050] The coupling analysis module is used to perform coupling analysis after receiving the dynamic topological twin, solve the theoretical value of the formation reaction force based on the twin structure parameters, calculate the pile driving rate index by combining the real-time pump pressure value and the settlement per unit time, allocate the injection rate weight coefficient and the formation reaction force weight coefficient according to the inverse of the pump pressure change rate and the inverse of the measured value fluctuation variance, perform dynamic weighted fusion on the injection rate and the formation reaction force measurement value to generate the injection coupling parameter, input the dynamic weighted fusion algorithm to generate the construction feedback deviation index, trigger the twin correction instruction and pass it to the twin synchronization module;

[0051] The twin synchronization module is used to adjust the thickness parameters of the virtual pile segment model based on the twin correction instructions, update the thickness according to the settlement rate difference, match the injection rate and reaction force measurement curve morphology similarity, generate updated twin parameters and synchronize them back to the dynamic mapping module and coupling analysis module.

[0052] The virtual pile segment model includes the reinforcement cage segment number and concrete pouring timestamp. The dynamic topological twin specifically refers to the corrected axis coordinates and the topological relationship of continuous pile segments. The construction feedback deviation indicators include the pile driving rate indicator and the pouring coupling parameters. The updated twin parameters specifically refer to the adjusted pile segment thickness parameters and material layer parameters.

[0053] The spatiotemporal interpolation algorithm performs hash matching between the reinforcement cage segment number and the timestamp, so that the timestamp label of the model attribute node is updated synchronously with the construction pouring progress;

[0054] The verticality threshold is 5% of the pile diameter and is verified by field pile test data;

[0055] The dynamic weighted fusion algorithm superimposes two types of parameters through normalized linear product, making the error between the construction feedback deviation index and the actual deformation of the pile less than 3%;

[0056] The morphological similarity is achieved by calculating the sum of squares of the slope differences of the two curves at adjacent time nodes, and then filtering out nodes with a difference greater than 0.5 for weight ratio adjustment.

[0057] See also Figure 2 , the twin configuration module includes:

[0058] The coordinate time series processing submodule obtains the depth coordinate sequence and concrete pouring timestamps, spatially matches the longitude, latitude, and elevation components of the coordinate points with the timestamps, calculates the difference between adjacent timestamps based on the moving speed of the pouring machinery, and integrates the spatial nodes and difference parameters to generate a segmented coordinate set for the pile body.

[0059] The coordinate time series processing submodule obtains the buried depth coordinate sequence and concrete pouring timestamp. Its specific execution process is as follows: the system first collects raw monitoring data from the sensor network installed at the construction site (such as the GNSS high-precision positioning module and depth encoder integrated in the drilling equipment, or the multi-point displacement and pressure sensor pre-fixed on the steel cage). Take a specific engineering pile P001 as an example. The pile is designed to be 30 meters long and 0.8 meters in diameter. During the drilling and lowering of the steel cage, a series of three-dimensional coordinate points representing the pile hole or steel cage at different depths will be recorded. ,in is the longitude, is the latitude, These coordinate point data, along with the corresponding acquisition time, are accurately recorded.

[0060] During the concrete pouring phase, the concrete pumping control system will record the key pouring operation timestamps in real time, which are accurate to the second. When the concrete liquid level rises to a predetermined or monitored specific depth range (sensed by the liquid level sensor installed at the end of the conduit or on the steel cage), the system matches the spatial position of the depth range with the corresponding pouring completion timestamp. Specifically, the GNSS positioning system of the construction machinery (such as a concrete pump truck or a drilling rig) provides its planar position information (such as the coordinates of the location near pile P001 when the pump truck is operating). ), which confirms that the current pouring operation is targeting pile P001. The system assigns a specific pouring timestamp to the corresponding vertical segment of the pile based on the theoretical concrete pumping volume and the actual monitored concrete level rise rate in the pile hole.

[0061] Subsequently, the system is based on the controlled lifting speed of the pouring machinery (such as concrete pipe) in the pile hole (the speed is set according to the concrete pouring process requirements, for example, controlled between 0.15 meters and 0.3 meters per minute, which is set here as the average vertical lifting speed). ) and the actual monitored completion time of each segment perfusion, calculate the adjacent effective perfusion timestamps (marked as and , corresponding to the depth from arrive The difference in intervals between the times when the pile body is poured in sections .set up (corresponding to the pouring completion of the depth range of -0.5m to -1.5m), (corresponding to the completion of the injection in the range of -1.5m to -2.5m depth), the time interval difference .

[0062] This difference parameter and the corresponding spatial node set (determined by depth and Finally, the system integrates these spatial node information and time difference parameters to generate a structured pile segment coordinate set. Each element in this set contains the starting and ending three-dimensional coordinates of a pile segment (e.g., segment 1: starting at , ends at ), and the precise timestamp of the injection completion associated with it and the perfusion time of this segment For pile P001, it will be carefully divided into multiple such segments, each with its own unique time and space attributes.

[0063] Table 1: P001 pile foundation depth coordinates and grouting time monitoring data (partial)

[0064]

[0065] Table 1 details the geographic coordinate ranges, elevation ranges, concrete pouring completion timestamps, and pouring times for the first five segments of P001. This precisely recorded data forms the foundation for the subsequent construction of a high-precision digital twin model.

[0066] The spatiotemporal interpolation modeling submodule divides the 3D grid cells based on the node distribution density of the pile segment coordinate set, constructs a time axis increment sequence for the uncovered area using the Lagrange interpolation method, corrects the continuity of the grid vertex coordinates based on the timestamp labels bound to the reinforcement cage, and outputs a time axis driving parameter set.

[0067] The spatiotemporal interpolation modeling submodule divides the 3D grid cells based on the node distribution density of the pile segment coordinate set generated in the previous step. The system analyzes the P001 pile segment coordinate set in detail. This set contains the precise start and end depths of each segment (following the data in Table 1, such as the P001-S1 segment, the depth is from -0.5m to -1.5m) and the corresponding grouting completion timestamp. Based on the geometric dimensions of these segments (pile diameter , segment length The basic size of the three-dimensional grid unit is determined by the data point density. To ensure the full description of the pile body shape, the grid unit size is set to .

[0068] In actual construction monitoring, if there is a temporary sensor failure or data transmission interruption, some pile sections (such as the depth between -5.5m and -6.5m) lack dense sensor data, and only the pouring timestamps at the two ends of the section (i.e., -5.5m and -6.5m) are available. (corresponding to the end of segment P001-S5) and (corresponding to the next valid recording point), the system uses Lagrange interpolation to construct the incremental sequence of these uncovered areas on the time axis. 、 and the length of the segment , and refer to the average perfusion rate of the adjacent covered area (such as P001-S5 segment) (calculated from its length of 1.0m and the time taken 305 seconds to get about 0.00328m / s) to estimate the perfusion rate of any virtual node in the 1-meter uncovered area. (Located between -5.5m and -6.5m, the depth from -5.5m is ) of the approximate infusion completion timestamp To insert a timestamp of a virtual node at -6.0m, this point is in the middle of the 1m segment ( ), whose timestamp is estimated to be Through this method, corresponding time series data are generated for grid cells with sparse or missing data.

[0069] The system then retrieves the pre-recorded and stored timestamp tags for the steel cage. These tags are precisely bound to specific nodes of the steel cage (such as the top, middle reinforcement ring, and bottom of each section) during the section-by-section production and hoisting of the steel cage. The P001 pile has three sections of steel cage, each 10 meters long. The timestamp for the top hoisting of the first section of steel cage (covering the range from 0m to -10m) is The system uses the actual timestamp labels of these steel cages to correct the continuity deviation of the mesh vertex coordinates in the time series that may be introduced by interpolation or other modeling processes. If the pouring timestamp of a mesh vertex obtained by interpolation has a set allowable deviation from the timestamp of the actual placement of the steel cage or the time when it is covered by concrete, Any differences other than the above will be corrected based on the actual time record of the steel cage. The setting is based on the compactness of the construction process and the accuracy of data recording, and is set here to 3 minutes. The specific setting is based on the following: During normal continuous pouring operations, the time interval between the lowering of a specific part of the rebar cage and the time it is covered by concrete is somewhat predictable. Any deviation exceeding 3 minutes is considered to require verification. Setting process: Analyze 100 sets of historical records of the lowering of the rebar cage and the initial setting time of concrete in the corresponding positions in similar projects, calculate the upper limit of the 95% confidence interval of the time difference, and take this upper limit (e.g., 180 seconds) as the allowable deviation. If the deviation is greater than 180 seconds, the timestamp of the mesh vertex is adjusted or marked as requiring further verification.

[0070] After the above processing, the system ultimately outputs a set of timeline-driven parameter sets. This parameter set contains the timestamp information of each 3D grid cell vertex and its corresponding precise spatial coordinates, laying a solid foundation for subsequent dynamic analysis.

[0071] The reinforcement cage time binding submodule extracts the reinforcement cage segment number from the time axis driving parameter group, maps the segment number to the segment interval corresponding to the timestamp, and uses the hash matching algorithm to associate the segment number with the timestamp label of the model attribute node to generate the virtual pile segment model;

[0072] The reinforcement cage time binding submodule extracts the segment number of the reinforcement cage from the time axis driven parameter group generated by the aforementioned "time-space interpolation modeling submodule". The time axis driven parameter group contains the precise spatial coordinates of each vertex of the pile body 3D mesh. and the corresponding timestamp The system has pre-stored detailed information about the P001 pile reinforcement cage: the total length is 30 meters, divided into (0m to -10m), (-10m to -20m), (-20m to -30m) three sections. When each section of steel cage is hoisted into the hole, the exact hoisting completion time of its key positions (such as top and bottom) is recorded and associated with the section number. For example, the section The lifting completion time (i.e. the time when its bottom reaches the depth of -10m) is , segment The lifting completion time (bottom reaches -20m) is .

[0073] The system selects the mesh vertices that are spatially coincident with or immediately adjacent to the physical locations of the cage segments from the timeline-driven parameter set and extracts their corresponding timestamps. These timestamps are then matched with the pre-stored construction time windows of the cage segments (the time period from the completion of the hoisting of the segment to its complete coverage by concrete). For example, the grid cells in the virtual pile model with a depth range of -0.5m to -9.5m have an associated pouring timestamp sequence (as shown in Table 1, starting at 10:02:30) that is later than Segment lifting completion time (09:15:00) and earlier than The time when the segment began to have a significant effect (i.e. Therefore, these grid cells located in the depth range of -0.5m to -9.5m are assigned the reinforcement cage segment number in the model. .

[0074] Next, the system uses a hash matching algorithm to efficiently number the extracted steel cage segments (e.g. ) is accurately associated with the timestamp tag of the corresponding attribute node in the virtual pile model. The specific execution process of hash matching is: for each reinforcement cage segment number (such as "RC_1"), a unique hash value is generated (for example, hash("RC_1") to obtain 0x5D8F1A). For each model attribute node in the timeline drive parameter group (carrying spatial coordinates and pouring timestamp ), the system is based on its And its spatial depth, to determine which steel cage segment the node belongs to in terms of actual impact time interval and spatial range. If the model node N789 is at -15.5m, its pouring timestamp is '2025-05-12 10:55:00', this time point and spatial position clearly correspond to the steel cage segment. (-10m to -20m, the hoisting completion time is 09:50:00) is poured with concrete, the identifier of node N789 is the same as The hash value of the virtual pile model, hash("RC_2"), is associated and stored. This fast hash table-based search and association mechanism enables the system to quickly and accurately assign the correct timestamp labels to the numerous attribute nodes that make up the virtual pile model.

[0075] This refined processing ultimately generates a segmented virtual pile model with clear segmentation information and precise temporal attributes. This model not only accurately reproduces the pile's 3D geometry but, more importantly, embeds the sequential formation of each pile component and the robust correspondence between these components and their corresponding reinforcement cage segments. Spatial hierarchical matching is achieved by establishing a real-time correlation between longitude and latitude coordinates and GNSS positioning data from construction machinery.

[0076] See also Figure 3 , the dynamic mapping module includes:

[0077] The axis deviation extraction sub-module obtains the axis coordinate sequence of the virtual pile segment model, decomposes the longitude, latitude, and elevation components of the model nodes, extracts the coordinate values ​​corresponding to the theoretical axis, and uses the Euclidean distance formula to calculate the absolute value of the difference between the actual coordinates and the theoretical coordinates to generate the axis coordinate deviation value;

[0078] The axis deviation extraction submodule is responsible for accurately obtaining the coordinate point sequence of the actual central axis of the pile from the virtual pile segment model generated by the "rebar cage time binding submodule". This sequence is composed of the pile model at different monitoring depths (elevations). The geometric center point For P001 pile, at depth

[0079] For each horizontal section (one section is taken every 1 meter), the system calculates the exact coordinates of the centroid of the section , thus obtaining a series of discrete points on the actual axis: .

[0080] Then, the system decomposes the coordinate components of these actual axis nodes. If the original coordinates are geographic coordinates (latitude and longitude), they are first converted to the project construction coordinate system. Here, click The actual axis coordinates are expressed as Next, extract the coordinate value of the theoretical axis at the corresponding depth. The theoretical axis represents the center line position of the pile under ideal design conditions. Its coordinates are clearly given by the design drawings, that is, the design coordinates of the center of the pile top. and the design coordinates of the pile bottom center For vertical piles, the theoretical axis is at any depth Plane coordinates of The theoretical plane coordinates are obtained by the pile center coordinates of the design drawings (the design pile center coordinates of engineering pile P001 are ) and the pile center coordinates after on-site precision construction and verification (the measured pile center coordinates are ) to reduce the error from a single source: ; Therefore, the theoretical axis plane coordinates of pile P001 are .

[0081] Then, the Euclidean distance formula is used to calculate the The actual axis point With the theoretical axis point The offset distance on the horizontal plane, that is, the axis coordinate deviation value at the depth The calculation formula is: ; at depth The actual axis coordinates of pile P001 obtained by model solution are The axis coordinate deviation value at this depth is Calculated as follows: Difference ;

[0082] Difference

[0083] ;

[0084] sum of squares

[0085] ; that is 67.082mm.

[0086] The system performs this deviation calculation for each actual axis point along the entire length of P001 pile (from -0.5m to -29.5m, a total of 30 calculation sections), and finally generates a sequence of 30 axis coordinate deviation values. The benefit of this formula is that by taking the square root of the sum of the differences between the actual coordinate components and the theoretical coordinate components, it can objectively and directly quantify the degree to which the pile axis deviates from its ideal design position at each depth, providing a key quantitative basis for subsequent verticality verification and point cloud correction. (i.e. 67.082mm) indicates that at a depth of The horizontal offset of the actual axis of pile P001 from its theoretical axis is 67.082 mm. This value will serve as the key input for the subsequent verticality verification submodule.

[0087] The verticality check submodule calls the axial verticality threshold based on the axis coordinate deviation value, compares the deviation value with the threshold item by item in node order, marks the spatial index of the node whose deviation value exceeds the axial verticality threshold, and generates an abnormal node identification set;

[0088] The verticality verification submodule is based on the axis coordinate deviation value sequence generated by the previous step "axis deviation extraction submodule" , to check the compliance of the pile verticality. Each element in the sequence Accurately expressed at a specific depth The horizontal offset between the actual axis of the pile and the theoretical axis. The deviation value calculated for P001 pile at a depth of -10.5m For example.

[0089] The system calls the preset axial verticality allowable deviation threshold The setting of this threshold is strictly in accordance with the current national "Technical Specifications for Building Pile Foundations" (such as JGJ94) and the specific requirements in the project design documents. For P001 piles (diameter , pile length ), the specification usually stipulates that the allowable deviation value of the pile top of a vertical pile is a multiple of the pile diameter or an absolute value, and the allowable deviation value at any section of the pile body is a specific ratio of the pile length. Here, for the verticality at any section of the pile body, the allowable deviation threshold is Set to pile length 1 / 350 of the length and 100mm, whichever is smaller. Calculation process: Permissible deviation of pile length ratio The upper limit of the absolute allowable deviation .but This threshold The rationale for setting the threshold is as follows: the 1 / 350 pile length ratio limit, derived from long-term engineering practice and theoretical analysis of the interaction between pile foundation bearing capacity and structure, is used to control additional stresses caused by excessive bending of the pile shaft. The absolute upper limit of 100mm is based on the prevailing level of construction technology and the superstructure's tolerance for pile position deviation. To ensure the rationality of this threshold, static load tests were conducted on 15 groups of full-scale model piles under conditions similar to those of the P001 pile. The maximum deviations of these model piles ranged from 50mm to 150mm (each group was constructed with 10mm intervals). The test results (selected data shown in Table 2) indicate that when the maximum deviation exceeds 1 / 350 of the pile length (approximately 85.7mm), the horizontal bearing capacity of the pile begins to decline nonlinearly, approaching the critical point where the decline exceeds 10%. When the deviation approaches 100mm, the bearing capacity of some piles decreases by more than 18%, and stress concentration in the pile shaft intensifies. Considering both safety and economic considerations, the selected pile was selected. As a control standard.

[0090] Table 2: Partial test data of model pile bearing capacity under different pile body deviations

[0091]

[0092] Table 2 lists the displacement of some model piles and their corresponding horizontal ultimate bearing capacities. The data shows that when the displacement exceeds 85.7 mm (e.g., M-6 pile), the rate of bearing capacity reduction begins to increase significantly, validating the rationality of the threshold selection.

[0093] The system will generate the 30 axis coordinate deviation values ​​previously In depth order, with this threshold Compare item by item. The comparison criteria are .like Greater than , then it is determined that the verticality of the pile at this node does not meet the specification requirements. For the deviation value at a depth of -10.5m ,because , the verticality of the node is qualified. If the deviation value calculated at the depth of -18.5m is , because , the verticality of the node is unqualified. The system will mark this deviation value The node that exceeds the axial verticality threshold (i.e. the node at a depth of -18.5m) is recorded, and its spatial index is recorded (e.g. the node number in the axis coordinate sequence is 19, and its three-dimensional spatial coordinates are The system collects the spatial indexes of all nodes that fail verticality and generates an abnormal node identification set. If the inspection results for pile P001 show that the axis deviations at depths of -15.5m, -18.5m, and -22.5m all exceed 85.71mm, the abnormal node identification set will include detailed information about nodes at these three depths.

[0094] The point cloud correction submodule uses the point cloud registration algorithm to process the abnormal node identification set, extracts the coordinate offset of the abnormal node, matches the spatial distribution of the abnormal point with the theoretical point cloud through the iterative closest point algorithm, and introduces the pile elastic deformation parameter to constrain the coordinate correction range to generate a dynamic topological twin.

[0095] The point cloud correction submodule calls the point cloud registration algorithm to process the abnormal node identification set generated by the previous step "verticality verification submodule". This set includes those in the P001 pile body whose actual axis deviation exceeds the preset verticality threshold. The spatial index or exact coordinates of the pile node. Assume that the abnormal node set includes node J (located at a depth of -18.5m, with actual coordinates , its axis deviation ) and node K (located at depth -22.5m, actual coordinates , its axis deviation ).

[0096] The system first accurately extracts the point cloud data of these abnormal nodes (J, K, etc.) and their surrounding areas from the initial 3D point cloud model of the pile (the model is generated by processing the multi-source sensor data during the construction process). At the same time, the system obtains the theoretical axis position points corresponding to these abnormal nodes (the corresponding point of node J on the theoretical axis is , whose coordinates are ). Based on this, the current coordinate offset vector of each abnormal node is calculated, focusing on its horizontal offset, such as the horizontal offset vector of node J .

[0097] Next, the system uses the iterative closest point (ICP) algorithm to accurately match the spatial distribution of the actual point cloud fragments containing these abnormal areas with the pile design model (or its theoretical point cloud representation). The specific execution steps of the ICP algorithm are as follows: 1. For each point in the abnormal point cloud fragment, search and determine its unique nearest neighbor point in the point cloud of the theoretical design model. 2. Based on these established corresponding point pairs, calculate an optimal rigid transformation parameter (including rotation matrix and translation vector). This transformation aims to minimize the root mean square error (RMSE) between the transformed abnormal point cloud and its corresponding point in the theoretical point cloud, so that the two sets of point clouds overlap to the greatest extent. 3. The calculated transformation Applied to the entire abnormal point cloud segment. 4. Repeat steps 1 to 3 until the change in the transformation parameters between two iterations is less than the preset convergence threshold (convergence threshold Set to 0.1mm, indicating that the average point correction accuracy reaches 0.1mm), or the number of iterations reaches the preset maximum number of iterations (maximum number of iterations The convergence threshold and maximum number of iterations are set based on the trade-off between algorithm efficiency and final registration accuracy. By performing multiple registration tests on standard geometric bodies and observing the RMSE decrease curve, the parameters are selected at the balance point between computational cost and accuracy benefit.

[0098] During the matching and correction process of the ICP algorithm, the system introduces the elastic deformation parameters of the pile material as key constraints. These parameters include the elastic modulus of the C35 concrete used in the P001 pile. and Poisson's ratio The purpose of introducing these parameters is to ensure that the range and method of coordinate correction are consistent with the mechanical properties of the material, and to avoid unrealistic correction results that may lead to excessive deformation of the pile model or abnormal concentration of local stress. This means that the transformation calculated by the ICP algorithm cannot cause the pile to deform beyond its elastic limit. If the ICP directly applies the calculated transformation, the local curvature radius of the pile body will be smaller than the minimum allowable curvature radius determined by the bending resistance of the material. (Calculated based on pile diameter and reinforcement, Set to 250 times the pile diameter, that is ), or if the relative displacement between any two points causes the strain generated to exceed the concrete's allowable strain value (e.g., 0.002, determined by the concrete's compressive strength grade and safety factor), the system will adjust the ICP correction. This adjustment is done by recalculating the allowable correction amplitude based on elastic constraints while maintaining the offset correction direction, or by proportionally reducing the original displacement correction vector to ensure that the corrected pile deformation remains within the elastic range.

[0099] By performing this type of registration and correction, strictly constrained by the material's elastic parameters, on the point cloud data of all identified anomaly areas, the system ultimately generates a dynamic topological twin. This twin's macroscopic geometry more closely approximates the pile's design intent (i.e., minimizes axial deviation), while scientifically preserving necessary local deviation information consistent with physical laws and material properties, enabling it to more realistically reflect the pile's actual state.

[0100] The theoretical axis coordinate value is obtained by calculating the average of the pile center coordinates in the design drawing and the construction layout point coordinates.

[0101] See also Figure 4 , the coupling analysis module includes:

[0102] The ground reaction force solver module receives the dynamic topological twin, decomposes the twin structural parameters such as pile diameter, pile length, and material elastic modulus, applies the Winkler foundation model to establish a linear equation for ground reaction force and pile displacement, solves the theoretical value of the superposition of pile end resistance and side friction resistance, and generates the theoretical value of ground reaction force.

[0103] The ground reaction solver module receives the finely modified and optimized dynamic topological twin model output by the point cloud correction module. This model accurately represents the actual 3D geometry of the P001 pile and its spatial position in the soil in the form of a 3D mesh or dense point cloud.

[0104] The system first decomposes and extracts the key parameters necessary for accurate calculation of formation reaction force from the structural parameter database of the twin model. These parameters mainly include: pile diameter This value is the equivalent average diameter calculated based on the modified model at different depths. For P001 pile, the overall average modified pile diameter is Pile length , is the exact length from the designed elevation of the pile top to the actual depth of the pile bottom, taken as ; and the elastic modulus of the pile material (C35 concrete) .

[0105] Next, the system applies the basic theory of the Winkler foundation beam model to establish the mathematical relationship between the ground reaction force and the pile displacement. The Winkler model simplifies the foundation around the pile into a series of independent discrete elastic springs distributed along the pile body. The reaction force generated by each spring is proportional to its own compression displacement (i.e. the displacement of the pile at that point). The relationship is: ,in is in depth The ground reaction force per unit area is is the lateral or vertical displacement of the pile at that depth, and It is the foundation reaction coefficient (also called base bed coefficient) that changes with depth. The accurate determination of is crucial to the calculation results. It is done by analyzing the detailed geotechnical engineering investigation report of the P001 pile construction site, especially the on-site in-situ test data (such as the number of blows in the standard penetration test (SPT) and the cone tip resistance of the static penetration test (CPT). and sidewall friction , and the results of the pressure test (PMT) and combined with the physical and mechanical properties of each soil layer, the results are estimated using empirical formulas or numerical inversion methods. The main soil layers that the P001 pile passes through and their corresponding base bed coefficient values ​​are as follows: -0m~-8m: silty clay, average Silty clay, average Fine sand, average These The value is set based on the analysis of the CPT data of the corresponding soil layer in the survey report. For example, for the silt-fine sand layer, the average cone tip resistance is , according to the empirical relationship (in is an empirical coefficient, which ranges from 0.8 to 1.5 for sandy soil, and is taken as 1.0 here). In practical applications, more refined stratification and more complex inversion models are used. For simplicity, the stratified average value is used here.

[0106] The system uses these parameters to solve the pile displacement and internal force through finite element method or finite difference method, and then calculates the ultimate resistance of the pile end. Total ultimate friction resistance of pile side The theoretical value of the pile end resistance is based on the bearing capacity characteristics of the pile end bearing layer and the pile end area. Calculation. For the total side friction of the pile, the unit side friction provided by each soil layer is integrated (or accumulated in sections) along the length of the pile. Unit side friction The properties of the soil at this depth, the effective stress between the pile and the soil, the relative displacement, and the foundation coefficient After calculation, the theoretical ultimate resistance of the pile end of P001 pile is obtained , the total theoretical ultimate friction resistance of the pile side Therefore, the theoretical value of the total ultimate ground reaction force after superposition (i.e. the vertical ultimate bearing capacity of the pile) is This 7150kN is the theoretical limit of the formation reaction force generated under current conditions.

[0107] The pile driving rate calculation submodule uses real-time pump pressure sensor data and laser rangefinder settlement data to convert the pump pressure value (MPa) into a pile end pressure component in kilonewtons and unify it with the settlement value (mm) to a second-level time reference. Based on the theoretical value of the formation reaction force, it constructs a balance equation between the pile driving resistance and the pump pressure. The forward difference method is used to calculate the settlement increment per unit time and output the pile driving rate index.

[0108] The pile sinking rate calculation submodule uses real-time monitoring of pump pressure sensor data during construction and pile settlement measurements from a laser rangefinder (or high-precision static level). The system collects two key data streams from the construction monitoring platform's database in real time: the first is pressure sensor data from the concrete pumping system (or, in static pile construction, from the hydraulic system), which is recorded in megapascals (MPa). Assume that at a specific moment, the pumping system cylinder pressure reading is The second is the real-time settlement of the pile measured by a laser rangefinder installed at the reference point on the pile top or a fixed reference object. The data is in millimeters (mm). At the same time, the cumulative settlement of the top of pile P001 is .

[0109] The system first converts the unit and time base of the collected raw data. (MPa) is converted to the equivalent pile top driving force component in kilonewtons (kN) This conversion needs to take into account the effective area of ​​the hydraulic cylinder and mechanical and pressure transmission efficiency coefficients . It is a known parameter determined by the model of the static pile driver used. . It is a comprehensive efficiency coefficient that reflects the loss from the cylinder pressure to the effective pressure actually applied to the pile top. Its value is determined by the equipment calibration test. The calibration test process is: before the pile foundation construction, use a standard force sensor to directly measure the actual output of the pile driver under different cylinder pressures. At least 5 groups of tests with different pressure levels are carried out, each group is repeated 3 times, and the average value is calculated. Through this calibration, It is determined to be 0.88. Unit conversion rule: 1MPa=1000kN / m². Therefore, the equivalent pile top driving force is .calculate:

[0110] .

[0111] (Note: 132.0kN here is the total driving force applied. The effective force actually used to overcome the ground resistance and produce settlement also needs to consider factors such as the deadweight of the pile. Here, it is simplified to the equivalent driving force directly balancing with the ground reaction force or producing movement.) At the same time, the settlement (mm) is uniformly converted to meters (m), that is, All data points are precisely aligned to a unified second-level time base .

[0112] Then, the theoretical limit value of the formation reaction force output by the "formation reaction force solution module" is obtained. (This value represents the total ultimate resistance that the soil can provide at the current total depth of the pile, and the actual resistance provided by the stratum at each moment of actual pile sinking. is less than or equal to this limit value and changes with the development of settlement), and the real-time calculated pile weight (increases with the length of the pile), construct the force balance equation or motion equation during the pile sinking process. In uniform pile sinking or quasi-static analysis, the driving force is approximately equal to the total resistance. In the dynamic pile sinking process, the instantaneous resultant force (If the deadweight of the pile is considered as a favorable factor for pile sinking, then ) drives the pile body to move. The system uses the forward difference method to calculate the settlement increment per unit time, that is, the pile sinking rate. The specific operation is: at two adjacent sampling time points and (Time interval , the sampling interval of this system is fixed at ), the cumulative settlement of the pile top was measured as and In this time period Average pile sinking rate Calculated as If hour , in the subsequent Measured at The average pile sinking rate within 2 seconds is The result of this calculation is the output of the current pile sinking rate index.

[0113] The dynamic fusion feedback submodule uses the pile driving rate index and pressure sensor measurements to set the injection rate weight coefficient to the inverse of the pump pressure change rate and the formation reaction force weight coefficient to the inverse of the measured value fluctuation variance. Using a dynamic weighted fusion algorithm, the two parameters are subjected to normalized linear product superposition to generate a construction feedback deviation index, triggering the twin correction instruction.

[0114] The dynamic fusion feedback submodule is based on the real-time pile sinking rate indicator output by the previous step "pile sinking rate calculation submodule" (The current value is ) and the real-time equivalent pile top driving pressure directly measured by the pressure sensor (here pump pressure Represents, the current value is , whose changing trend indirectly reflects the changing characteristics of the stratum reaction force or the stability of construction control) for comprehensive analysis.

[0115] The system sets two key weight coefficients: injection rate (or construction efficiency, here represented by pile sinking rate) weight coefficient of characterization) , and the formation reaction response (caused by pump pressure The weight coefficient of the stability characterization Injection rate weight coefficient Set as pile driving rate The normalized form of the inverse of the rate of change over a period of time (e.g., 60 seconds). Calculation: First, calculate the pile sinking rate every 2 seconds over the past 60 seconds, obtaining 30 rate values, and then calculate the standard deviation of these rate values. .like Very small (e.g. less than 0.05 mm / s, indicating that the rate is very stable and is judged as "high" stability), then Take the larger value; if If the value is larger (e.g., greater than 0.2 mm / s, indicating that the rate fluctuation is “significant” and the stability is judged to be “low”), then Take the smaller value. ,in is a small positive constant that prevents the denominator from being zero and adjusts the sensitivity. Assume that the last 60 seconds have been calculated (This volatility level is defined as "medium"), then .

[0116] Formation reaction force (pump pressure) weight coefficient Set to pump pressure measurement value The fluctuation variance in the past period of time (such as 60 seconds) Calculation process: Collect the pump pressure data for the past 60 seconds (one point every 2 seconds, a total of 30 points) and calculate its variance If the variance is small (e.g. less than , pressure fluctuation "low"), then Take the larger value; if the variance is large (such as greater than , pressure fluctuation "high"), then Take the smaller value. ,in For adjustment. Assuming that the calculation is (This volatility level is defined as "moderately low"), then The logic behind setting these two weight coefficients is that the higher the stability of the parameter (i.e. the smaller the rate of change or variance), the higher its representativeness and confidence in the current construction status, and therefore a greater weight is given to it. These stability ranges of "high", "medium", and "low" are determined based on statistical analysis of data from a large number of historical high-quality construction cases (selecting 100 completed and high-quality pile foundation projects). For example, statistics on the steady-state pile sinking stage of these 100 projects The distribution is defined as “high” stability if the distribution is below the 25th percentile and “low” stability if the distribution is above the 75th percentile.

[0117] Then, the system uses a dynamic weighted fusion algorithm to perform normalized linear product superposition on the two core parameter indicators to generate a comprehensive construction feedback deviation indicator. First, the current pile sinking rate and pump pressure Compare with their respective expected values ​​or ideal intervals to obtain the normalized deviation index. is the normalized pile driving rate compliance (e.g. ,in is the target pile sinking rate at the current depth, such as ,but ), is the consistency index between the normalized pump pressure and the pressure predicted by the formation model. For simplicity, a comprehensive scoring method is adopted here. ,in and are the values ​​of pile sinking rate and pump pressure after standardization (e.g., conversion to performance scores in the range of 0-1, with 1 being the best). (Indicates that the current rate is performing well), (Indicates that the current pressure status is acceptable).

[0118] This fusion value (Range 0-1) and a preset ideal state reference value (like , this benchmark value represents the expected good construction status) for comparison, and the difference This is the generated construction feedback deviation index. When the absolute value of this deviation index is Exceeding a certain pre-set action threshold , the system will trigger a twin correction instruction. Set to 0.15. The threshold is set based on the simulation analysis of different sizes. Potential impact on the final pile foundation quality (such as bearing capacity, uniformity), when If the error exceeds 0.15 for a certain period of time (e.g., 5 consecutive minutes), it is considered that the construction status deviates significantly from the ideal state, which may lead to increased quality risks. The parameters of the digital twin model need to be adjusted to better reflect the actual working conditions, and the operator may be prompted to pay attention. , does not exceed 0.15, so no correction is triggered for the time being. If it is greater than 0.15, it will be triggered.

[0119] The base coefficient of the Winkler foundation model is determined by inversion of on-site static penetration test data.

[0120] See also Figure 5 , the twin synchronization module includes:

[0121] The thickness parameter adjustment submodule calls the twin correction instruction to extract the initial thickness parameters of the virtual pile segment model and generates the thickness correction parameters based on the inverse proportional relationship between the thickness adjustment coefficient of the correction instruction and the pile sinking rate index;

[0122] The thickness parameter adjustment submodule is activated after receiving the twin correction instruction triggered by the "dynamic fusion feedback submodule". This instruction clarifies the type and approximate extent of the significant deviation between the current construction status and the model prediction. Assume that the received instruction indicates: "The current actual average pile sinking rate (such as ) is significantly lower than the theoretical average pile sinking rate predicted by the twin model based on current parameters (e.g. ), it is inferred that the model does not fully reflect the actual ground resistance encountered, and the geometric parameters related to the pile-soil interaction in the model need to be adjusted to match the actual situation.

[0123] The system first extracts the "thickness parameter" of the current virtual pile segment model (P001 pile) at the time of the previous iteration or initial setting. The "thickness parameter" here is the key geometric dimension that affects the interaction area between the pile and the surrounding soil, and thus affects the calculation of side friction and end resistance. For a circular pile like P001, it is its equivalent effective diameter. Assume that the current average equivalent diameter of each segment in the model is (From paragraph 7 ).

[0124] Next, the system adjusts the direction indicated in the correction instruction (need to increase the resistance reflected by the model) and the actual pile sinking rate. Theoretical pile sinking rate predicted by the model to generate a specific thickness correction parameter The relationship between the thickness adjustment coefficient and the pile driving rate index is logically as follows: when the actual pile driving rate is significantly lower than the theoretically predicted rate, it indicates that the actual stratum resistance encountered is greater than the resistance calculated by the current parameters of the model. In order for the model to reproduce this greater resistance (and thus cause the theoretical rate to decrease to approach the actual rate), it is necessary to moderately increase the equivalent effective diameter of the pile in the model (i.e., the "thickness parameter"), because an increase in diameter will lead to an increase in the calculated area of ​​​​side friction resistance and the end bearing area (or its influence coefficient). Thickness adjustment amount is calculated as follows: .in, It is a calibrated adjustment sensitivity coefficient, whose value is determined based on a large number of simulation experiments and back analysis of historical data. It is used to control the amplitude of a single adjustment and avoid model oscillation. The value is set to 0.15. The basis for setting this value is: through retrospective analysis of 20 historical pile foundation cases, it is found that under similar deviation conditions, the value within the range of 0.1 to 0.2 is used. The value is iteratively corrected, and the model converges quickly and has good stability. Calculation:

[0125] ;

[0126] This calculated This is the thickness (diameter) correction increment for this iteration. Therefore, the new equivalent diameter of the P001 pile in the twin model will be adjusted to The purpose of this adjustment is to enable the digital twin model to more accurately simulate the actual observed pile driving behavior (i.e., a lower pile driving rate) in the next iterative calculation.

[0127] The sedimentation rate update submodule calculates the absolute value of the difference between the actual sedimentation rate and the theoretical rate based on the thickness correction parameter, and uses the cubic spline interpolation method to distribute the difference to the corresponding model nodes according to the length of the time segment to generate a dynamic thickness update value;

[0128] The sedimentation rate update submodule is based on the thickness correction parameters generated by the previous step "thickness parameter adjustment submodule" And the adjusted new equivalent action diameter The system also records that the model was based on the old diameter before applying this thickness correction. Predicted theoretical average sedimentation rate , and the average sedimentation rate actually observed during the same assessment period .

[0129] The system first uses the updated pile equivalent action diameter , combined with the existing formation parameters and load conditions, a complete pile sinking process simulation analysis or simplified theoretical calculation is re-performed in the digital twin model to obtain a new theoretical average settlement rate corresponding to the new diameter As the pile diameter increases, the total formation resistance (mainly lateral friction resistance) calculated by the model will increase accordingly, so the new theoretical settlement rate is expected to decrease. After calculation, the new theoretical average settlement rate .

[0130] The system then calculates this new theoretical sedimentation rate The average sedimentation rate observed The absolute difference between . This residual difference It represents the difference between the model-predicted sedimentation rate and the observed sedimentation rate after the model parameters (equivalent diameter) have been adjusted. Ideally, this difference should be close to zero.

[0131] Next, the system uses cubic spline interpolation to convert the overall equivalent diameter from Adjust to (Total increment The geometric changes of the pile body caused by the change in the depth of the virtual pile body P001 are smoothly and reasonably distributed to the nodes of each segment model along its entire length (30.15m), forming a specific dynamic thickness (diameter) update value for each segment. The use of cubic spline interpolation is to ensure the smoothness and continuity of the pile body diameter change along the depth, and avoid unnatural geometric mutations at the segment connection. The specific operation is: P001 pile is divided into control nodes (for example, a control node is set every 1 meter, for a total of about 30 nodes). The diameter value before adjustment at each control node is known, as well as the average diameter increase that needs to be achieved overall. The cubic spline interpolation algorithm will construct a series of piecewise cubic polynomial functions. These functions are continuous not only in function value (diameter) at each control node, but also in first-order derivative (diameter change rate) and second-order derivative (rate of change of diameter change rate). By solving the coefficients of these polynomials, the specific diameter increment at each control node can be obtained. .These The weighted average effect of (taking into account the length of each segment) should be able to achieve the goal of increasing the overall equivalent diameter by about 45.17 mm. For example, for the segments in the middle area of ​​the pile shaft, the diameter increase allocated to them is The increments may be slightly higher than the average, while those near the pile top and bottom may be slightly lower, depending on the boundary conditions set by the interpolation algorithm and the assumed deformation pattern (for example, assuming that pile deformation occurs primarily in the middle). Ultimately, the system generates a precise sequence of dynamic thickness (diameter) updates for each segment or key node in the P001 pile model. These updates are directly used to modify the geometric parameters of the twin model, ensuring that it better matches the actual working conditions in the next simulation or analysis.

[0132] The curve morphology matching submodule obtains the time domain sampling point sequence of the injection rate curve and the reaction force measurement curve, calculates the sum of the squares of the slope differences of the two curves at multiple time nodes, filters out the nodes that exceed the morphology similarity threshold, redistributes the weight ratio of the dynamic thickness update value, and generates updated twin parameters.

[0133] The curve morphology matching submodule obtains the time domain sampling point sequence of two types of key dynamic parameters during the construction process: one is the injection rate curve. For the static pressure pile sinking process of P001 pile, this refers to the actual penetration depth change rate of the pile body per unit time, that is, the real-time pile sinking rate calculated above. The second is the reaction force measurement curve, which refers to the total jacking pressure monitored in real time by the hydraulic system of the static pile driver. This pressure directly reflects the total reaction force of the stratum on the pile during the sinking process. The system extracts these two sets of precisely timed data sequences from the construction monitoring database, with a sampling interval of 2 seconds. Injection rate (pile sinking rate) sequence:

[0134] . Reaction force measurement (total jacking pressure) sequence: . (Equivalent to 12.3MPa pump pressure)

[0135] ;

[0136] The system calculates these two time series at each sampling time point The instantaneous change trend at , that is, calculate its first-order difference or local slope. The change in pile driving rate: ; Change in total jacking pressure: ;exist This point (using the next point to calculate the change): (The rate is increasing) (Pressure is increasing);

[0137] Next, the system calculates the two sets of variation sequences and A key evaluation indicator is the consistency of their changing directions. Under normal circumstances, when the pile driving rate increases (pile penetration is accelerated), if the formation conditions remain unchanged, the jacking pressure may decrease slightly or remain stable (inertia effect or soil disturbance softening); but more commonly, to maintain or increase the rate, especially in harder soil layers, the jacking pressure needs to be increased accordingly. Conversely, if the jacking pressure increases significantly, the pile driving rate will usually slow down. A morphological similarity evaluation rule is set here: within a certain time window (such as 5 consecutive sampling points, i.e. 10 seconds), if and The sign of the relationship (positive or negative, representing an increasing or decreasing trend) shows an "inverse" relationship at more than 60% of the points (i.e., at least 3 points) (for example, the rate increases while the pressure decreases, or the rate decreases while the pressure increases, which may indicate a special response of the soil or construction control adjustment), or the amplitude ratio of the two changes in the same direction ( ) significantly deviates from the normal range predicted by the current formation model (this range is obtained through model simulation, for example, the normal range is 10kN / (mm / s) to 30kN / (mm / s)), then the node morphology matching degree in this time window is considered to be "low". Morphological similarity threshold Definition: within a 10-second window, if there are more than 3 "reverse" trend points, or If the ratio exceeds the range of [5,35] kN / (mm / s) (this range represents 80% of normal construction conditions based on historical data statistics), it is determined to be a morphological mismatch.

[0138] The system filters out all time nodes or segments that are judged to be morphologically mismatched. For these segments with "low" morphological matching, the system will re-examine and adjust the weight ratio of the dynamic thickness (diameter) update value generated by the "settlement rate update submodule". The specific operation is: in those segments where the pile sinking rate and jacking pressure response pattern show inconsistent behavior with the conventional model prediction (such as During this period, a sudden increase in velocity without a corresponding increase in pressure may indicate a sudden drop in local soil bearing capacity or a special situation in the hole. The system will reduce the application weight of the thickness (diameter) correction value of this section, which was previously calculated mainly based on the deviation of the overall settlement rate (for example, the original calculated thickness (diameter) correction value will be reduced). The weight is adjusted from 1.0 to 0.4), and an additional, targeted adjustment factor for local model parameters (such as local soil parameters or pile-soil contact model parameters) may be introduced based on the characteristics of the current morphological mismatch (such as delayed or oversensitive pressure response), rather than just adjusting the diameter. Through this curve morphological matching-based weight redistribution and fine-tuning of the dynamic thickness update value (and other potentially associated model parameters), a comprehensive set of parameters for updating the digital twin model is ultimately generated. This ensures that the updated twin not only matches reality in terms of overall average settlement behavior, but also more realistically reproduces real-time construction conditions in terms of the critical rate-reaction dynamic response characteristics that reflect complex pile-soil interactions.

[0139] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A digital twin system for the entire pile foundation construction process, characterized in that: The system comprises: The twin configuration module is used to generate a virtual pile segment model based on the burial depth coordinate sequence and the concrete pouring timestamp. It calls the spatiotemporal interpolation algorithm to construct a time axis driven model sequence, binds the reinforcement cage segment number and the timestamp, establishes a dynamic mapping relationship, generates the virtual pile segment model, and passes it to the dynamic mapping module. A dynamic mapping module is used to extract the axis deviation value of the virtual pile segment model, calculate the Euclidean distance in a unified coordinate unit, call the verticality threshold to determine abnormal nodes, input the point cloud registration algorithm to correct the coordinates, generate a dynamic topological twin, and pass it to the coupling analysis module; The coupling analysis module is used to receive the dynamic topological twin, solve the theoretical value of the formation reaction force and calculate the pile driving rate index, dynamically weightedly fuse the injection rate and the formation reaction force measurement value to generate the injection coupling parameter, execute the dynamic weighted fusion algorithm to generate the construction feedback deviation index, trigger the twin correction instruction and pass it to the twin synchronization module.

2. The digital twin system for the entire pile foundation construction process according to claim 1 is characterized in that: The spatiotemporal interpolation algorithm performs hash matching on the reinforcement cage segment number and the timestamp, so that the timestamp label of the model attribute node is updated synchronously with the construction pouring progress; The verticality threshold is 5% of the pile diameter and is verified by field pile test data; The dynamic weighted fusion algorithm superimposes two types of parameters through normalized linear product, so that the error between the construction feedback deviation index and the actual deformation of the pile body is less than 3%.

3. The digital twin system for the entire pile foundation construction process according to claim 2 is characterized in that: The twin configuration module includes: The coordinate time series processing submodule obtains the depth coordinate sequence and concrete pouring timestamps, spatially matches the longitude, latitude, and elevation components of the coordinate points with the timestamps, calculates the difference between adjacent timestamps based on the moving speed of the pouring machinery, and integrates the spatial nodes and difference parameters to generate a segmented coordinate set for the pile body. The spatiotemporal interpolation modeling submodule divides the three-dimensional grid cells based on the node distribution density of the pile segment coordinate set, constructs a time axis increment sequence for the uncovered area using the Lagrangian interpolation method, corrects the continuity of the grid vertex coordinates according to the timestamp labels bound to the steel cage, and outputs a time axis driving parameter group; The reinforcement cage time binding submodule extracts the reinforcement cage segment number from the time axis driving parameter group, maps the segment number to the segment interval corresponding to the timestamp, and uses a hash matching algorithm to associate the segment number with the timestamp label of the model attribute node to generate a virtual pile segment model; The spatial hierarchical matching is achieved by establishing a real-time association between the longitude and latitude coordinates and the GNSS positioning data of the construction machinery.

4. The digital twin system for the entire pile foundation construction process according to claim 3 is characterized in that: The dynamic mapping module includes: The axis deviation extraction submodule obtains the axis coordinate sequence of the virtual pile segment model, decomposes the longitude, latitude, and elevation components of the model nodes, extracts the coordinate values ​​corresponding to the theoretical axis, calculates the absolute value of the difference between the actual coordinates and the theoretical coordinates using the Euclidean distance formula, and generates the axis coordinate deviation value; The verticality check submodule calls the axial verticality threshold based on the axis coordinate deviation value, compares the deviation value with the threshold item by item in node order, marks the node spatial index whose deviation value exceeds the axial verticality threshold, and generates an abnormal node identification set; The point cloud correction submodule calls the point cloud registration algorithm to process the abnormal node identification set, extracts the coordinate offset of the abnormal node, matches the spatial distribution of the abnormal point with the theoretical point cloud through the iterative nearest point algorithm, and introduces the pile elastic deformation parameter to constrain the coordinate correction range to generate a dynamic topological twin.

5. The digital twin system for the entire pile foundation construction process according to claim 4 is characterized in that: The theoretical axis coordinate value is obtained by calculating the average value of the pile center coordinates in the design drawing and the construction layout point coordinates.

6. The digital twin system for the entire pile foundation construction process according to claim 5, characterized in that: The coupling analysis module includes: The ground reaction force solver module receives the dynamic topological twin, decomposes the pile diameter, pile length, and material elastic modulus in the twin structure parameters, applies the Winkler foundation model to establish a linear equation for ground reaction force and pile displacement, solves the theoretical value of the superposition of pile end resistance and side friction resistance, and generates a theoretical value of ground reaction force; The pile driving rate calculation submodule uses real-time pump pressure sensor data and laser rangefinder settlement data to convert the pump pressure value into a pile end pressure component in kilonewtons and unify it with the settlement value to a second-level time reference. Based on the theoretical value of the formation reaction force, the equilibrium equation between the pile driving resistance and the pump pressure is constructed. The forward difference method is used to calculate the settlement increment per unit time and output the pile driving rate index. Based on the pile driving rate index and the pressure sensor measurement, the dynamic fusion feedback submodule sets the injection rate weight coefficient as the inverse of the pump pressure change rate and the formation reaction force weight coefficient as the inverse of the measurement fluctuation variance. The two types of parameters are subjected to normalized linear product superposition through the dynamic weighted fusion algorithm to generate a construction feedback deviation index and trigger the twin correction instruction.

7. The digital twin system for the entire pile foundation construction process according to claim 6, characterized in that: The base bed coefficient of the Winkler foundation model is determined by inversion of on-site static penetration test data.

8. The digital twin system for the entire pile foundation construction process according to claim 7, characterized in that: The system further comprises: The twin synchronization module is used to adjust the thickness parameters of the virtual pile segment model based on the twin correction instruction, update the thickness according to the settlement rate difference, match the injection rate and reaction force measurement curve morphology similarity, generate updated twin parameters and synchronize them back to the dynamic mapping module and coupling analysis module.

9. The digital twin system for the entire pile foundation construction process according to claim 8, characterized in that: The updated twin parameters specifically refer to the adjusted pile segment thickness parameters and material layer parameters; The morphological similarity is obtained by calculating the sum of squares of the slope differences of the two curves at adjacent time nodes, and screening nodes with a difference greater than 0.5 for weight ratio adjustment.

10. The digital twin system for the entire pile foundation construction process according to claim 9, characterized in that: The twin synchronization module includes: The thickness parameter adjustment submodule calls the twin correction instruction, extracts the initial thickness parameter of the virtual pile segment model, and generates a thickness correction parameter according to the inverse proportional relationship between the thickness adjustment coefficient of the correction instruction and the pile sinking rate index; The sedimentation rate update submodule calculates the absolute value of the difference between the actual sedimentation rate and the theoretical rate based on the thickness correction parameter, and uses the cubic spline interpolation method to distribute the difference to the corresponding model nodes according to the length of the time segment to generate a dynamic thickness update value; The curve morphology matching submodule obtains the time domain sampling point sequence of the injection rate curve and the reaction force measurement curve, calculates the sum of the squares of the slope differences of the two curves at multiple time nodes, filters out the nodes that exceed the morphology similarity threshold, redistributes the weight ratio of the dynamic thickness update value, and generates updated twin parameters.

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