A super-long pile foundation construction method based on a BIM data model

Abstract

The application discloses a kind of based on BIM data model's super-long pile foundation construction method, it is related to building pile foundation construction technical field.The present application includes the following steps: constructing multi-source heterogeneous BIM geological body model and carrying out voxel parameter assignment;Based on stratum feature sequence, pre-optimization drilling parameter is carried out and BIM hole-forming simulation is carried out;Dynamic correction control is carried out using depth weighted perpendicularity algorithm;The thickness of sediment is predicted by multi-field coupling model and the hole cleaning parameter is optimized;Intelligent floating ball measuring point arrangement is optimized based on space coverage algorithm and perfusion BIM pre-play is carried out;BIM digital twin is realized perfusion closed-loop control by floating ball measured data driving;BIM quality thermal map is generated by inverting concrete diffusion parameter;Time-varying settlement model is established to verify the load-carrying capacity classification.The present application realizes the digital closed-loop management and control of the whole process of super-long pile foundation construction by the deep integration of BIM digital twin and intelligent floating ball monitoring, improves the perfusion elevation measurement accuracy and the quality and efficiency of super-long pile foundation construction.

Description

Technical Field

[0001] This invention belongs to the field of building pile foundation construction technology, and in particular relates to a construction method for ultra-long pile foundations based on BIM data models, which is applicable to the construction of ultra-long bored cast-in-place piles with a pile length exceeding 80 meters. Background Technology

[0002] With the rapid development of modern infrastructure construction, the requirements for construction accuracy and structural safety in ultra-long pile foundation projects are increasing. Traditional concrete pouring height measurement technology relies on manual operation, which has revealed significant defects in ultra-long pile foundation construction: human error can easily lead to quality problems such as mud inclusion and faulting, and the increase in pile length amplifies the cumulative error, seriously affecting the quality of pile formation; at the same time, due to insufficient instrument accuracy, it is difficult to meet the needs of ultra-long pile foundations for millimeter-level elevation monitoring. These problems directly threaten the bearing capacity of the pile foundation and the safety of the project.

[0003] Therefore, there is an urgent need for a construction method for ultra-long pile foundations that integrates BIM digital twin and intelligent monitoring technologies to achieve digital closed-loop management and control of the entire process from drilling to pile formation. Summary of the Invention

[0004] The purpose of this invention is to provide a construction method for ultra-long pile foundations based on BIM data models. By deeply integrating BIM digital twins with intelligent buoy monitoring, a digital closed-loop control of the entire construction process of ultra-long pile foundations is achieved, solving the problem that existing instruments are not accurate enough and cannot meet the needs of ultra-long pile foundations for millimeter-level elevation monitoring.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention relates to a construction method for ultra-long pile foundations based on BIM data models, comprising the following steps: Collect survey data to construct a three-dimensional geological BIM model and divide it into voxels according to soil layers. Assign physical, mechanical and drillability parameters to each voxel unit, establish the mapping relationship between the pile location and the geological model coordinate system, and generate a single pile stratum profile parameter chain by traversing along the pile axis. Based on the formation profile parameter chain, the characteristic sequences of each formation are extracted. The drilling pressure, rotation speed and mud density parameters are optimized in segments with the dual objectives of maximizing drilling rate and minimizing deviation risk. A pre-optimized drilling parameter table is generated and imported into BIM for hole formation simulation verification. Real-time monitoring of tilt data during drilling is initiated, and the overall verticality deviation is calculated through a depth-weighted algorithm. Combined with adaptive PID dynamic adjustment of drilling pressure and guide vanes based on formation stiffness, closed-loop correction is implemented, and the actual borehole geometry model is reconstructed. Read the hole formation geometry model, combine the hole formation parameters and drilling data to construct a multi-field coupled sediment prediction model, reverse solve the hole cleaning time and cycle parameters corresponding to the target sediment, and perform a secondary hole cleaning operation until the sediment thickness meets the design threshold. Based on the borehole geometry parameters and formation permeability coefficient, the spatial coverage algorithm is used to optimize the number and planar layout of the float measuring points, establish a BIM grouting digital twin scenario, and conduct a grouting process simulation. The intelligent buoy elevation measurement system is installed at optimized locations. It automatically identifies the interface of the three media layers by the change of buoyancy gradient to determine the concrete elevation. The measured data is transmitted back in real time to drive the synchronous update of the BIM model. The PID dual-variable collaborative control keeps the burial depth of the guide pipe stable within the design range, realizing closed-loop control of the grouting.

[0006] Optionally, the specific process for implementing closed-loop correction includes: A set of dual-axis tilt sensors is installed at the top of the drill pipe and at the middle guide joint to monitor the X and Y tilt angles of two different depth sections respectively. A depth-weighted algorithm was used to calculate the overall verticality deviation value and deviation direction of the pile hole, and a depth-deviation coupling relationship was established. Based on the equivalent dip angle components in the X and Y directions, the spatial azimuth angle of the deviation is synthesized and calculated to quantitatively assess the degree of deviation of the current hole formation. Read the formation compression modulus corresponding to the current drilling depth, and dynamically adjust the proportional, integral, and derivative coefficients of the PID controller according to the formation's hardness. The correction direction is determined based on the deviation azimuth angle, and the total drilling pressure is differentially distributed in the circumferential direction. The drilling pressure distribution and guide vane angle are dynamically adjusted according to the deviation value and the current formation parameters to implement closed-loop correction.

[0007] Optionally, the specific method for calculating the overall verticality deviation using the depth-weighted algorithm includes: The X-axis and Y-axis tilt data collected by each tilt sensor are divided into multiple monitoring segments according to the drilling depth. The X-axis tilt, Y-axis tilt, midpoint depth of the monitoring segment, and height of the monitoring segment are obtained for each monitoring segment. The corresponding depth weight is determined based on the midpoint depth of each monitoring segment. The depth weight is positively correlated with the midpoint depth of the monitoring segment, so that the dip angle data of the deep monitoring segment has a higher weight than that of the shallow monitoring segment. The X-axis dip angle data and Y-axis dip angle data of each monitoring section are respectively subjected to depth weighted fusion to obtain the overall X-axis equivalent dip angle and the overall Y-axis equivalent dip angle. Based on the overall X-axis equivalent dip angle and the overall Y-axis equivalent dip angle, the spatial deviation and deviation direction of the hole are determined. The horizontal offset and verticality deviation rate of the pile bottom are determined based on the spatial deviation and the current total hole depth to characterize the overall degree of deviation of the pile hole relative to the designed pile axis.

[0008] Optionally, the specific process for dynamically adjusting the proportional, integral, and derivative coefficients of the PID controller according to the hardness of the formation includes: Obtain the current drilling depth and read the formation compression modulus corresponding to the current drilling depth from the voxel parameter library of the 3D geological BIM volume model; The formation compression modulus is compared with the preset reference formation compression modulus. The hardness of the current formation is determined based on the change in the formation compression modulus. The proportional coefficient, integral coefficient, and derivative coefficient of the PID controller are adaptively adjusted according to the preset stiffness influence relationship. When the current formation compression modulus changes relative to the reference formation compression modulus, the various control coefficients of the PID controller are corrected accordingly. The difference between the target verticality and the current actual verticality is determined as the verticality control deviation. Based on the verticality control deviation, the cumulative change of the verticality control deviation, and the trend of the verticality control deviation, proportional control quantity, integral control quantity, and derivative control quantity are generated respectively. The proportional control quantity, integral control quantity, and derivative control quantity are superimposed to obtain the correction drill pressure adjustment quantity, and the drill pressure distribution and the working angle of the guide vane are adjusted according to the correction drill pressure adjustment quantity.

[0009] Optionally, the cleaning time and cycle parameters corresponding to the target sediment are solved in reverse, and a second cleaning operation is performed until the sediment thickness meets the design threshold. The specific process includes: After the hole is completed, the average drilling speed, final drilling mud viscosity, mud specific gravity, pile diameter and hole depth data of the final drilling stage are collected. Combined with the particle size distribution and clay content parameters of the strata at the bottom of the hole, a multi-factor coupled sediment thickness prediction model is constructed to calculate the initial sediment thickness prediction value. Using the target sediment thickness required by the design as input, and based on the sediment settling and suspension dynamics model, the three key parameters required to achieve the target thickness are calculated in reverse: mud circulation flow rate, negative pressure of the reverse circulation suction port, and expected hole cleaning time. This generates the initial parameter scheme for the hole cleaning operation. Based on the progress of hole cleaning and the changes in the sand content of the mud in the hole, the mud circulation flow rate and reverse circulation pressure are adjusted in stages; in the early stage of hole cleaning, a large flow rate is used to quickly remove slag, and the flow rate is gradually reduced when the target thickness is approached to avoid turbulent disturbance at the bottom of the hole causing repeated suspension of sediment. The reverse circulation hole cleaning system is started according to the optimized parameters. The density difference sensor of mud inlet and outlet and the ultrasonic sediment detector are used to collect data on the sand content of mud in the hole and the thickness of sediment at the bottom of the hole in real time, and the sediment distribution status in the BIM model is updated synchronously. The thickness of the sediment monitored in real time is continuously compared with the design threshold. When the thickness requirement is met for multiple consecutive sampling cycles and the sand content of the mud is lower than the control value, the hole cleaning is deemed to be up to standard, the hole cleaning operation is automatically stopped, and a hole cleaning quality report is output.

[0010] Optionally, the specific process of establishing the BIM-injected digital twin scene and performing a pre-rehearsal of the injection process includes: The reference monitoring radius of the float is determined based on the pile diameter and concrete slump. Then, it is corrected by combining the lateral permeability coefficient of the surrounding strata and the proportion of the grouting height to obtain the actual effective monitoring radius considering the strata filtration effect, which serves as the basic parameter for the optimization of the monitoring point layout. With the goal of full coverage of the pile hole cross section and the constraints of the number of floats and equipment cost, a point layout optimization model is established to maximize spatial coverage. An intelligent optimization algorithm is used to solve for the optimal number of measuring points and the planar coordinate position of each float, minimizing the number of measuring points while ensuring monitoring coverage. Import the pile hole geometry model, the guide pipe layout model, and the optimized float measuring point position into the BIM platform to construct a digital twin scenario for concrete pouring. Based on concrete rheological properties parameters and preset grouting rate, numerical simulation of concrete diffusion and rising process in pile hole is carried out to simulate concrete surface morphology, duct burial depth variation, laitance layer thickness distribution and concrete flow velocity field distribution under different grouting heights. Extract the time history curve of the duct burial depth, the spatial distribution of the slurry layer and the flatness index of the concrete surface from the simulation output, and compare them with the design control requirements; adjust the grouting rate or the duct lifting rhythm, re-perform simulation verification, and output the final grouting construction parameter table after iterating to the optimal scheme.

[0011] Optionally, when optimizing the spatial coverage by maximizing the number of monitoring points, the pile diameter and effective monitoring radius are input to establish a circular coverage optimization model within a circular area. A simulated annealing algorithm is used for the solution. Initially, n monitoring point positions are randomly generated, and the current coverage and objective function value are calculated. When a monitoring point position is randomly moved, if the objective function increases, a new solution is accepted; if it decreases, the monitoring point position is abandoned. Iteration stops when the temperature decays to a set value, and the current optimal point layout scheme is output. The solution is repeated for different numbers of monitoring points, and a coverage-number curve is plotted. The cost weight is used to determine the economically optimal number of monitoring points and their corresponding coordinates. The optimization results are output as a planar coordinate table for each float to guide on-site installation and positioning.

[0012] Optionally, the optimization of the float placement adopts a spatial coverage maximization algorithm, specifically including: Based on the baseline monitoring radius of a single float, the current grouting height, the total length of the pile hole, and the lateral permeability coefficient of the surrounding strata, the influence of the strata filtration effect on the monitoring range of the float is determined, and the baseline monitoring radius is corrected to obtain the effective monitoring radius of a single float. The proportion of the area within the pile hole cross-section that is effectively monitored by one or more floats to the total area of ​​the pile hole cross-section is used as the coverage evaluation index, and the equipment cost corresponding to the number of floats is used as a constraint factor to establish a deployment optimization target that takes into account both monitoring coverage and the number of equipment. Within the cross-sectional area of ​​the pile hole, a preset number of initial positions of floats are randomly generated, and the coverage and equipment cost evaluation results of the current deployment scheme are calculated. The float position is iteratively adjusted according to the simulated annealing algorithm. When the adjusted placement scheme can improve the overall evaluation result, the placement scheme is accepted. When the overall evaluation result decreases, the placement scheme is accepted with a preset probability based on the current iteration temperature and the degree of change in the evaluation result. Gradually reduce the iteration temperature and repeatedly adjust the float position until the preset stopping condition is reached to obtain the optimal placement scheme for the corresponding number of floats. The deployment of floats is optimized for different numbers of floats, and the monitoring coverage and equipment cost corresponding to each number of floats are compared. Under the condition of meeting the preset coverage requirements, the economically optimal number of floats and their planar coordinates are determined, and the float deployment coordinate table is output.

[0013] Optionally, the specific construction steps for automatically identifying the interface of the three media layers and determining the concrete elevation by means of the buoyancy gradient change, and controlling the stabilization of the tremie pipe burial depth within the design range, include: Install each set of intelligent float elevation measurement system according to the layout location, and lower the float into the mud in the hole; perform initial zero point calibration and self-weight calibration, record the initial tension value of each float in the mud, and establish the measurement benchmark for each measuring point; During the grouting process, the float tension data is continuously collected, and the gradient of tension change with depth is calculated. When the tension gradient changes abruptly and the absolute value exceeds the set threshold, it is determined that the float has reached the layer interface. Based on the magnitude of the tension change, the three media layers of mud layer, laitance layer and concrete layer are distinguished, and the precise elevation of the top surface of the concrete is locked. The length of the lowered rope of the float is measured, and the data is fused and filtered using a Kalman filter algorithm. At the same time, steel wire rope temperature drift compensation is introduced to dynamically correct the elevation measurement value and output real-time elevation data with millimeter-level accuracy. The real-time elevation data of each measuring point is transmitted to the BIM digital twin platform. The quadratic surface fitting algorithm is used to reconstruct the shape of the full-section concrete surface, and the state parameters are updated synchronously to drive the BIM model to evolve in sync with the on-site construction. Based on the real-time calculation of the duct burial depth value from the BIM model, the midpoint of the target burial depth range is used as the control target. The PID controller simultaneously adjusts two variables: the duct lifting speed and the concrete pouring rate. When the burial depth is too large, the duct lifting is accelerated and the pouring is slowed down. When the burial depth is too small, the duct lifting is slowed down and the pouring is accelerated. The two variables work together to keep the duct burial depth stably controlled within the design range. Real-time monitoring of the consistency of elevation, abnormal fluctuations in tension, and rate of change in burial depth at each measuring point; setting multiple early warning thresholds; when a risk signal of pile breakage such as a sudden drop in burial depth or abnormal elevation difference is detected, an emergency control program is automatically triggered to suspend pipe lifting and increase the grouting volume, restoring the burial depth of the guide pipe to a safe range.

[0014] The present invention has the following beneficial effects: This invention constructs a full-process BIM digital twin construction system, from multi-source heterogeneous BIM geological modeling, drilling parameter pre-optimization, depth-weighted verticality correction, sediment prediction and hole cleaning optimization, float point optimization and grouting pre-simulation to digital twin closed-loop grouting control. It deeply integrates geological survey data, construction parameters, real-time monitoring data and BIM model, and enables data interconnection and linkage among various processes. This solves the problems of traditional extensive management and control, such as independent and fragmented processes, lack of data flow, and complete reliance on manual experience in ultra-long pile foundation construction. It significantly improves the digital and intelligent management and control level and multi-process collaborative operation efficiency of the entire ultra-long pile foundation construction process. This invention introduces a depth-weighted integral verticality calculation method, which accurately reflects the amplification effect of deep deviations in ultra-long piles using a weight allocation rule based on the inverse square of depth. It also incorporates adaptive adjustment of the PID correction gain based on the formation compression modulus. In soft soil layers, the gain is increased for rapid correction, while in hard soil layers, the gain is decreased for stable drilling. Combined with differential drilling pressure distribution and dual-path collaborative correction using guide vanes, this invention solves the problems of traditional verticality algorithms failing to adequately consider the amplification effect of ultra-long pile depths and the inability of correction parameters to adaptively adjust with formation characteristics. This effectively improves the control accuracy of ultra-long pile drilling verticality and the correction efficiency when traversing complex formations.

[0015] This invention establishes a multi-factor coupled sediment thickness prediction model based on drilling speed, mud parameters, and formation particle characteristics. It also inverts the flow rate, pressure, and time parameters required for borehole cleaning based on the sediment exponential decay kinetic model. Combined with a three-stage dynamic flow control strategy of rapid slag removal, gradual deceleration, and slag stabilization to meet standards, as well as a joint standard-setting mechanism based on thickness, sand content, and stability, this invention solves the problems of traditional borehole cleaning relying on experience-based fixed parameters, the tendency of high-flow-rate turbulence in the later stages to cause repeated sediment suspension, and the tendency of single readings to produce false standards. This invention effectively improves the efficiency of borehole cleaning operations and the stability and reliability of pile end sediment control.

[0016] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a construction method for ultra-long pile foundations based on a BIM data model, according to Embodiment 1 of the present invention. Figure 2This is a flowchart of a construction method for ultra-long pile foundations based on a BIM data model, according to Embodiment 2 of the present invention. Figure 3 This is a schematic diagram illustrating the principle of depth-weighted verticality calculation in this invention. Figure 4 This is a schematic diagram of the intelligent float elevation measurement system. Figure 5 This is a diagram of the BIM digital twin injection closed-loop control system architecture of Embodiment 3 of the present invention; Figure 6 Flowchart for time-varying settlement prediction and graded verification; Figure 7 This is a schematic diagram of the thermal image of the pile top concrete quality in Embodiment 2 of the present invention; Figure 8 This is a schematic diagram of the catheter burial depth time history curve in Embodiment 2 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0020] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0022] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a construction method for ultra-long pile foundations based on a BIM data model, as provided in Embodiment 1 of the present invention.

[0023] This invention relates to a construction method for ultra-long pile foundations based on BIM data models, comprising the following steps: Step S1: Construction of Multi-Source Heterogeneous BIM Geological Model and Parametric Modeling of Pile: Collect site exploration borehole data, geophysical profile data and in-situ test data, construct a three-dimensional geological BIM volume model and perform voxel subdivision, assign physical and mechanical parameters and drillability index to each voxel unit; establish a parametric pile foundation BIM component library, including parametric models of drill rods, guide pipes, steel cages and float measurement systems; establish the mapping and transformation relationship between the pile coordinate system and the geological model coordinate system, and generate the stratigraphic profile parameter chain for each pile; In this embodiment of the invention, step S1 includes the following sub-steps: Step S11: Standardized preprocessing of multi-source exploration data: Collect all exploration borehole data, seismic geophysical profile data and in-situ test data in the site, perform coordinate system I, outlier removal and format standardization processing on various types of data, and establish a unified format exploration raw database. Step S12: Construction of a 3D geological body model based on spatial interpolation: Using standardized exploration data as control points, the elevation values ​​of each stratigraphic interface at any position on the plane are calculated using a spatial interpolation algorithm to generate continuous stratigraphic interfaces and construct a 3D geological BIM model; the interpolation process satisfies the unbiased estimation condition to minimize the estimation variance. In this embodiment of the invention, three parameters of the spherical variogram are fitted based on the distance and attribute difference between all borehole point pairs. , , A spatial correlation model is established; for any point to be interpolated, a system of Kriging equations is established, and the weighting coefficients of each borehole point are solved. The constraints are that the sum of all weights equals 1, and the estimated variance is minimized. The elevation values ​​of each borehole are weighted and summed to obtain the stratum elevation of the point to be interpolated. The elevation of each regular grid point in the entire site is calculated point by point to generate a continuous stratum interface surface. The stratum interface surfaces are stacked in stratum order to construct a three-dimensional geological model.

[0024] It should be noted that the formula for interpolating the elevation of the stratigraphic interface in the Kriging equations is as follows: ( From 1 to ); In the formula, coordinates Elevation of the stratigraphic interface at the location. For the first Measured values ​​of formation elevation from each borehole. For the first Interpolation weighting coefficients for each borehole. The number of boreholes used in the interpolation.

[0025] The specific formula for the spherical variogram model is as follows: when hour, ;when hour, ; In the formula, Distance The corresponding variogram value, This is the nugget value (reflecting measurement error and microscale variation). This represents the sill value (reflecting the total system variation). For variable range (reflecting the range of spatial correlation). This represents the spatial distance between two points.

[0026] Step S13, Stratigraphic Voxelization and Multi-parameter Assignment: The 3D geological model is divided into cubic meshes according to the set spatial resolution to form regularly arranged voxel units; based on the stratigraphic type to which each voxel unit belongs, the corresponding compression modulus is assigned. Cohesion internal friction angle Severe Permeability coefficient Standard penetration test number Drillability rating With lateral permeability coefficient There are a total of eight physical, mechanical, and engineering parameters; In this embodiment of the invention, the spatial resolution of the voxel grid is set (typically 0.5m × 0.5m × 0.5m), and the three-dimensional geological model is divided into regular cubes to generate a three-dimensional voxel matrix. The stratigraphic range within which the center point of each voxel unit is located is determined, thus identifying its stratigraphic type. The physical and mechanical parameters of the corresponding stratigraphic layer are extracted from the exploration database and assigned to the voxel unit. For voxels near stratigraphic interfaces, a linear weighting method is used for parameter transition. A mapping relationship between voxel spatial coordinates and parameter vectors is established, supporting rapid querying of stratigraphic parameters at any point based on spatial location. Specifically, typical parameters for each stratigraphic layer are shown in the table below: Step S14: Establishment of Parametric Family Library for Pile Foundation Construction Components: Establish four parametric component families in the BIM platform: drill rod, guide pipe, rebar cage and intelligent float measurement system. Each component family is configured with adjustable geometric dimension parameters and attribute parameters, which supports the rapid generation of component models of corresponding specifications based on different pile diameters and lengths. In this embodiment of the invention, the ultra-long pile foundation construction technology of the intelligent buoy elevation measurement system determines the liquid level layer by detecting the buoyancy change of the buoy at different liquid levels (mud layer, laitance layer, and concrete layer). As the buoy rises or falls, the buoyancy changes, and the system records the values ​​of different buoyancy and height, thereby monitoring the height of the measured liquid level layer in real time. When the buoy reaches the concrete layer, the tension of the buoy line approaches zero, indicating that it is indeed a concrete layer. Furthermore, the buoy can automatically rise along with the top surface of the concrete, allowing for real-time monitoring of the relative elevation of the concrete top surface. The burial depth of the guide pipe is determined based on its relative elevation; the pressure value at the pipe opening is calculated based on the concrete burial depth data. The concrete diffusion curve is determined by detecting the concrete surface curve between the guide pipes, and the elevation of the concrete top surface curve is monitored in real time on the system display screen.

[0027] like Figure 4 As shown, in specific implementation, based on the buoyancy variation characteristics caused by the density differences of the float in the mud layer, laitance layer, and concrete layer, a high-sensitivity weighing sensor (resolution) is used. Real-time monitoring of the tension on the buoy. When the buoy passes through the mud layer (density...) When entering the slurry layer (density), the difference between buoyancy and gravity is significant; Afterwards, the buoyancy decreases, and the pulling force decreases. ; Reaching the concrete layer (density) When the float tension approaches zero, the system automatically determines the level switch and triggers the elevation lock signal, with the error controlled within ±1mm. Then, dual encoders (absolute + incremental encoders, accuracy 0.001°) work together, combined with the winding wheel groove guide structure, to calculate the release length of the traction rope in real time. , For wheel diameter, For the number of laps, (for the release length of the traction rope), and via an inductive displacement sensor (accuracy) Dynamically calibrate the float height. The PLC system has a built-in Kalman filter algorithm to automatically compensate for temperature drift. Adaptability), rope elongation (nylon rope elongation) To mitigate vibration interference and ensure continuous and stable elevation data, a mathematical model for the burial depth of the guide pipe was finally established. ; ( This is the initial elevation. For the increment of rope length, The angle of the catheter. (For the burial depth of the guide pipe). The system collects the concrete diffusion rate in real time. , For concrete diffusion rate, The concrete diffusion height, (Concrete diffusion time), pipe pressure () , For the pipe pressure, For concrete density, It is the acceleration due to gravity. Based on the height of the concrete inside the pipe and the burial depth data, the servo motor speed is dynamically adjusted using a PID controller to ensure that the burial depth of the guide pipe is always maintained at a certain level. The safety zone is maintained to avoid the risk of pile breakage; the concrete top surface diffusion curve is generated by fitting multi-point float data, and the fitting error is minimized. .

[0028] Figure 4 In the borehole, three layers of media are arranged from top to bottom: mud layer, laitance layer, and concrete layer. The float rises with the concrete surface, and the layer interface is determined by the sudden change in tensile force. The specific labeling distribution is as follows: Step S15, Pile-Ground Coordinate System Mapping and Stratigraphic Parameter Chain Extraction: Establish the transformation relationship between the local coordinate system of each pile and the global coordinate system of the geological model, and complete the spatial alignment of the pile location and the geological model; traverse each stratigraphic interface along the pile axis, extract the depth range and corresponding physical and mechanical parameters of each layer, and generate a unique stratigraphic profile parameter chain for each pile for subsequent construction parameter calculation.

[0029] Step S2: Drilling parameter pre-optimization and BIM simulation based on formation feature sequence: Extract the depth of each formation interface, soil strength parameters and drillability index along the pile axis to generate a formation feature sequence; calculate the optimal combination of drilling pressure, rotation speed and mud specific gravity parameters in segments according to the formation feature sequence to generate a pre-optimized drilling parameter table; import the pre-optimized parameter table into the BIM construction simulation module to perform virtual simulation of the hole formation process and verify the hole verticality and hole collapse risk; Step S2 specifically includes the following sub-steps: Step S21, Extraction and Layer Marking of Stratigraphic Feature Sequence: Traverse all strata interfaces along the pile axis from the pile top to the pile bottom, and mark the top depth, bottom depth and thickness of each soil layer in depth order; extract four key indicators of each soil layer: compression modulus, internal friction angle, standard penetration test blow count and drillability grade, and generate a strata feature sequence sorted by depth. In this invention, the depths of the stratigraphic interfaces are extracted from the stratigraphic parameter chain generated in step S15, sorted by depth from smallest to largest, and the number of layers and the thickness of each layer are determined. For each layer, four representative indicators of that layer are extracted from the voxel parameter library, and the average value of the parameters within the layer is taken as the feature value of that layer. The feature vectors of each layer are combined in depth order to form a one-dimensional stratigraphic feature sequence, the length of which is equal to the number of stratigraphic layers. The location of the interface between soft and hard strata is marked as a key node for subsequent parameter transition processing.

[0030] It should be noted that the formula for calculating the stratigraphic feature vector is as follows: In the formula, For the first Soil layer thickness, This indicates the drillability level of the layer. The compressive modulus of this layer. This represents the standard penetration test (SPT) blow count for this layer. This is the matrix transpose.

[0031] Step S22, Multi-objective optimization calculation of drilling parameters: For each formation, with the dual optimization objectives of maximizing drilling efficiency and minimizing deviation risk, the optimal parameter combination for each formation is obtained by comprehensively considering the constraints of three adjustable parameters: drilling pressure, rotation speed, and mud specific gravity. The solution process takes into account both drilling efficiency and hole quality, forming a set of optimal parameters for each formation. In this embodiment of the invention, the current formation characteristic parameters are input, including drillability grade, compressibility modulus, and SPT blow count; constraints are established on the value ranges of three decision variables: drilling pressure, rotation speed, and mud specific gravity; drilling speed objective function and skewness objective function are constructed respectively, with the two objectives having opposite directions (one maximizing and the other minimizing); a linear weighted summation method is used to transform the dual objectives into a single-objective comprehensive evaluation function, with the weights set according to the engineering quality requirements (usually when quality is prioritized). (Take 0.6); use grid search or genetic algorithm to solve for the minimum value of a single objective, and obtain the optimal combination of drilling pressure, rotation speed, and mud density parameters for that layer; solve for all formations in sequence to generate a table of optimal drilling parameters for each layer.

[0032] Step S23, Formation Interface Parameter Smoothing Transition Processing: For the interface of adjacent formations, the three parameters of drilling pressure, rotation speed and mud specific gravity are smoothed through transition processing; a transition interval is set above and below the interface so that the parameter values ​​change continuously and gradually between the upper and lower layer values, avoiding drilling instability and borehole wall disturbance caused by sudden parameter changes. In this embodiment of the invention, the locations of all soft and hard strata interfaces are identified, and the length of the transition section is determined based on the magnitude of the difference in parameters between the upper and lower layers. The greater the difference, the longer the transition section. Typically, a value of [missing value] is used. Meters; with the interface depth as the center, extend half a transition segment upwards and half a transition segment downwards to define a complete transition interval; for each depth point within the transition interval, use the sine square function to calculate the parameter value, so that the parameter smoothly and gradually changes from the upper layer value to the lower layer value, the first derivative of the transition curve is continuous, and the rate of change at both ends is zero; insert the transition segment parameters into the corresponding positions in the layered parameter table to form a complete parameter sequence that changes continuously along the depth.

[0033] Step S24, Virtual simulation of the hole-forming process in BIM: Integrate the layered optimization parameters and transition section parameters into a complete drilling parameter table and import it into the BIM construction simulation module; Perform virtual simulation of the hole-forming process according to the actual drilling sequence to simulate the stress deformation of the drill rod, the change in hole wall stability and the development of borehole deviation. In this embodiment of the invention, the complete drilling parameter table is discretized into multiple construction steps according to depth, with each step corresponding to a certain drilling depth. For each construction step, the drill pipe stress and deflection deformation are calculated based on the current drilling pressure and formation parameters to predict the borehole deviation increment. The borehole wall stability index is calculated based on the current mud specific gravity and formation parameters to assess the borehole collapse risk level. The deviation amounts of each step are accumulated to obtain the full borehole verticality prediction curve. The stability indices at each depth are organized into a borehole collapse risk distribution curve along the depth. The simulation results are visualized and rendered into a BIM model, with color gradients displaying the verticality deviation and risk level distribution.

[0034] It should be noted that the formula for calculating drill pipe deflection is: ; In the formula, For depth Lateral deflection of the drill pipe at that location. It is a laterally uniformly distributed load. The elastic modulus of the drill pipe. Let the moment of inertia of the cross section be... This is the total length of the drill pipe.

[0035] The formula for calculating the pore wall stability index is: ; In the formula, The pore wall stability index (greater than 1 indicates stability). This refers to the pressure of the mud column. Pore ​​water pressure, For aperture, For cohesion, The soil weight, It is the internal friction angle.

[0036] Step S25, Parameter Feasibility Verification and Iterative Correction: Based on the virtual simulation results, extract the predicted verticality deviation and hole collapse risk index at each depth and compare them with the preset control threshold; if there are sections exceeding the standard, adjust the parameter constraints of the corresponding strata, recalculate the optimization and verify it through simulation again, and iterate until all indicators meet the design requirements, and output the final pre-optimized drilling parameter table.

[0037] In this embodiment of the invention, the full-hole verticality prediction curve and the hole collapse risk curve output by the virtual simulation are read and compared with the control threshold at each depth. If all depths meet the threshold requirements, the verification is passed, and the final parameter table is output. If there are sections exceeding the standard, the corresponding strata are located, and the upper limit of drilling pressure for that stratum is tightened proportionally according to the extent of the exceedance, the upper limit of rotation speed is reduced, and the mud weight is appropriately increased. The optimization calculation in step S22 is re-executed using the modified constraint conditions to generate a new round of parameter tables. The new parameter tables are then imported into the BIM simulation module for verification, and the iteration is repeated until all indicators meet the standards. Typically, the iteration is repeated... It converges in one iteration.

[0038] Please see Figure 3 As shown, step S3, depth-weighted verticality dynamic correction control: Dual-axis tilt sensors are installed at the top and middle of the drill pipe to collect tilt data of different depth sections in real time; the depth-weighted algorithm is used to calculate the overall verticality deviation value and deviation direction of the pile hole, and a depth-deviation coupling relationship is established; the drilling pressure distribution and guide vane angle are dynamically adjusted according to the deviation value and the current formation parameters to implement closed-loop correction. Step S3 specifically includes the following sub-steps: Step S31, Multi-section Inclination Sensor Deployment and Data Acquisition: A set of dual-axis inclination sensors is deployed at the top of the drill pipe and the middle guide section to monitor the X and Y inclination angles of two different depth sections respectively; during drilling, inclination data is continuously acquired at the set sampling frequency. Low-frequency sampling is used during the normal drilling stage, and high-frequency sampling is automatically switched after entering the correction mode. In this embodiment of the invention, during the drill pipe assembly stage, two sets of MEMS dual-axis tilt sensors are respectively installed at the top flange of the drill pipe and inside the middle guide section for initial zero-point calibration. After drilling begins, data is collected at a frequency of 1Hz during the normal drilling stage and transmitted to the ground control system via the drill pipe's built-in cable or wireless transmission. When the tilt angle is detected to exceed the warning threshold, the system automatically switches to correction mode, increasing the sampling frequency to 5Hz to improve the data time resolution. The original tilt angle data is filtered by moving average to eliminate high-frequency noise interference caused by drilling vibration and output a smooth tilt angle time history curve.

[0039] Step S32, Depth-weighted equivalent verticality calculation: Based on the inclination data and corresponding depth of the two monitoring sections, the equivalent verticality deviation of the entire hole is calculated using the depth-weighted integral method; during the calculation process, the inclination data of the deep section is given a significantly higher weight than that of the shallow section, reflecting the amplification effect of the deep deviation of the ultra-long pile, and obtaining an equivalent inclination value that can accurately reflect the degree of pile bottom offset. In this embodiment of the invention, the tilt angle values ​​of two monitoring sections are obtained. , and corresponding depth , The borehole is divided into two calculation segments; assuming a linear change in dip angle between the two segments, the average dip angle and midpoint depth of each segment are calculated separately; the dip angle of each segment is multiplied by the corresponding depth weight using the discrete formula, then summed, and finally multiplied by the normalization coefficient. The depth-weighted equivalent dip angle is obtained; it is updated in real time as the drilling depth increases. Values ​​and weight distributions are used to dynamically calculate the current equivalent verticality.

[0040] It should be noted that the formula for depth-weighted integral verticality is as follows: The formula for the continuous form is: The discrete form formula is: ; In the formula, For depth-weighted equivalent dip angle, Let be the tilt angle of the i-th monitoring segment. The depth is the midpoint of this segment. For Duan Gao, This represents the current total hole depth.

[0041] Figure 3 The left side shows a schematic diagram of the pile inclination measurement point layout, illustrating a multi-section inclination monitoring scheme along the pile's axial direction. Multiple inclination monitoring sections are arranged along the pile depth direction (the diagram shows 5 sections, corresponding to depths of...). Each cross-section is equipped with a dual-axis tilt sensor to independently measure the local tilt angle of the pile at that cross-section. Measurements show that the local inclination angles of the pile body at different depths are independent of each other. The shallow inclination angle is large but the affected depth is short, while the deep inclination angle is small but the affected depth is long. The two have significant differences in their contribution to the total offset of the pile bottom.

[0042] Figure 3 The right side shows a schematic diagram of the pile inclination measurement point layout, illustrating the weight distribution along the depth direction. The weighting function is... The distribution pattern shows a linear increase from the pile top to the pile bottom: Pile top position With a weighting coefficient of 0, the local inclination angle at the top of the pile has almost no effect on the overall verticality. Pile bottom position When the weighting coefficient reaches its maximum value, the deep inclination angle plays a dominant role in the overall verticality.

[0043] The inclination angle of each monitoring section on the left is multiplied by the weighting coefficient of the corresponding depth on the right, and the weighted sum is used to obtain the overall equivalent verticality, realizing the calculation logic of "high weight for deep deviation and low weight for shallow deviation".

[0044] Figure 3The depth-weighted equivalent verticality calculation scheme proposed by the bottom formula is based on the principle that "the greater the depth of the inclined section, the greater its contribution to the overall verticality of the pile". Differentiated weight coefficients are assigned to the local inclination angles at different depths, with higher weights for greater depths. This ensures that the calculated equivalent inclination angles can accurately reflect the actual offset of the pile bottom and prevent the slight deviations in the deep parts of ultra-long piles from being masked.

[0045] formula The calculated depth-weighted equivalent inclination angle essentially equates the variable-angle pile along the depth to a uniformly inclined straight pile. The inclination angle of this straight pile is the equivalent inclination angle, and the total horizontal offset at the pile bottom produced by both is exactly equal. Because the weighting function satisfies full-depth integral normalization (total weight = 1), the equivalent inclination angle retains the linear correspondence between "inclination angle" and "offset," and therefore can be directly used to calculate the verticality deviation rate without additional correction.

[0046] Specifically, the verticality deviation rate is defined as the ratio of the horizontal offset at the pile bottom to the total hole depth, usually expressed as a percentage: Verticality deviation rate ; Because the verticality deviation in pile foundation engineering is usually much less than 1 (the standard limit is generally 1%). The corresponding tilt angle is extremely small, satisfying the small angle approximation condition: ; Therefore, from an engineering perspective, it can be directly simplified to: Verticality deviation rate .

[0047] Step S33, Calculation of deviation azimuth and pile bottom offset: Based on the equivalent inclination components of the X and Y directions, the spatial azimuth of the deviation is calculated; combined with the current total hole depth, the horizontal offset and verticality deviation rate of the pile bottom position are calculated to quantitatively assess the current degree of hole deviation. In this embodiment of the invention, depth-weighted calculations are performed on the dip angle data in the X and Y directions respectively to obtain the equivalent dip angle components in the two directions. and The two components are multiplied by the total hole depth to obtain the horizontal offset component at the bottom of the pile. The components are then combined to obtain the total offset. The total offset is divided by the hole depth to obtain the verticality deviation rate, which is compared with the allowable value in the specification (usually 1%). The arctangent is calculated based on the ratio of the two components, and the spatial azimuth angle of the deviation is obtained by combining the quadrant judgment to determine the correction direction.

[0048] Step S34, Calculation of formation adaptive PID correction parameters: Read the formation compression modulus corresponding to the current drilling depth, and dynamically adjust the proportional, integral, and derivative coefficients of the PID controller according to the softness and hardness of the formation; automatically increase the correction gain in soft soil formations to speed up the response speed; automatically decrease the correction gain in hard rock formations to avoid damage to the drill pipe caused by forced correction. In this embodiment of the invention, the formation compression modulus corresponding to the current drilling depth is queried. The parameters are obtained in real time from the voxel parameter library in step S13; the PID three-term coefficients corresponding to the current formation are calculated by substituting them into the adaptive formula, for soft soil formations. Smaller coefficients indicate larger hard rock formations. A larger value results in a smaller coefficient; the difference between the target verticality and the current actual verticality is used as the control deviation. Calculate the proportional term, integral term, and derivative term separately, and sum them to obtain the total corrective control quantity. Limit the control input to prevent excessive single adjustment from causing drill pipe vibration. It should be noted that the formula for calculating the formation adaptive scaling factor is as follows: ; In the formula, This is the formation adaptive scaling factor. As the benchmark scaling factor, For reference formation compression modulus, The current formation compression modulus, Stiffness influence index (usually taken as...) ).

[0049] The PID control output formula is: ; In the formula, Adjust the drilling pressure to correct deviation. This is the deviation signal (target tilt angle minus current tilt angle). , , These are the proportional, integral, and differential coefficients, respectively.

[0050] The formula for adjusting the integral and differential coefficients proportionally with formation stiffness is as follows: ; In the formula, As the baseline integral coefficient, The base differential coefficient.

[0051] Step S35, Differential distribution of drilling pressure and coordinated correction by guide vanes: Determine the correction direction based on the deviation azimuth angle, and differentially distribute the total drilling pressure in the circumferential direction. Increase the drilling pressure on the opposite side of the deviation and decrease the drilling pressure on the same side of the deviation to form a correction torque; at the same time, adjust the lateral extension angle of the guide vanes to generate directional correction force through the two paths. In this embodiment of the invention, the correction direction is determined based on the deviation azimuth angle, i.e., which direction the borehole needs to be corrected; the total correction amount output by the PID is converted into a circumferential drilling pressure distribution, with increased drilling pressure on the opposite side of the correction direction and decreased drilling pressure on the same side, forming a correction torque through asymmetrical drilling; the lateral extension of the guide vanes is synchronously adjusted, with the vanes extending on the opposite side of the correction to increase lateral support, forming a synergistic correction effect with the differential drilling pressure; control commands are sent to the drilling rig hydraulic system and the vane adjustment mechanism to execute the correction action.

[0052] It should be noted that the formula for circumferential drilling pressure distribution is: ; In the formula, Inscribed angle Drill pressure distribution at location, Average drilling pressure, To correct the deviation amplitude, To correct the azimuth angle.

[0053] Step S36: Real-time evaluation of correction effect and gain fine-tuning: After each set drilling depth, the depth-weighted verticality is recalculated, and the change in deviation before and after correction is compared to evaluate the actual effect of the current correction strategy. If the correction efficiency is lower than expected, the control gain is appropriately increased. If overshoot oscillation occurs, the control gain is decreased to achieve online adaptive tuning of the correction parameters.

[0054] In this embodiment of the invention, after drilling 0.5 meters, the depth-weighted verticality is recalculated and compared with the deviation value before correction to calculate the actual correction efficiency. The actual correction efficiency is compared with the target efficiency. If the actual efficiency is lower, it indicates that the current gain is insufficient, and the PID coefficient is appropriately increased. If the deviation is detected to oscillate in the opposite direction, it indicates that the gain is too large and overshoot occurs, and the control gain is immediately reduced. The gain adjustment adopts a small step gradual method, and the adjustment range does not exceed 10% each time to avoid system instability. The updated PID parameters are used for the next stage of correction control to form an online self-tuning closed loop.

[0055] Depend on Figure 3 It can be seen that, Figure 3 The left side shows the layout of the pile inclination measurement points. Five inclination sensor sections are evenly distributed along the pile, with depths of [insert depths here]. Measured local dip angles of each section ; Reflects the tilt state of the pile at different depths; Total pile height Pile bottom offset Pile diameter This section visually demonstrates the data acquisition method for multi-section inclination angles, with each measuring point independently measuring the local inclination angle of the pile at its depth.

[0056] Figure 3 The right side shows the weight distribution curve: the horizontal axis represents the weight coefficient. The vertical axis represents depth. Weighting function It exhibits a linearly increasing distribution: at the pile top The time weight is 0, at the bottom of the pile The time weight reaches its maximum value. This distribution satisfies the integral of the entire pile. The normalization condition ensures that the physical meaning of the weighted result is consistent with that of ordinary perpendicularity.

[0057] Step S4, Multi-field Coupled Sludge Thickness Prediction and Hole Cleaning Parameter Optimization: Collect drilling speed, mud viscosity, mud specific gravity and hole depth data, and construct a multi-parameter coupled prediction model for sludge thickness; calculate the hole cleaning time, mud circulation flow rate and reverse circulation pressure parameters corresponding to the target sludge thickness based on the prediction model; perform secondary hole cleaning operation according to the optimized parameters, and monitor the sludge thickness at the bottom of the hole in real time until the design threshold is met. Step S4 specifically includes the following sub-steps: Step S41, Multi-parameter Coupled Prediction of Final Drilling Sediment Thickness: After the final drilling stage, the average drilling speed, final drilling mud viscosity, mud specific gravity, pile diameter and hole depth data are collected. Combined with the particle size distribution and clay content parameters of the strata at the bottom of the hole, a multi-factor coupled sediment thickness prediction model is constructed to calculate the initial sediment thickness prediction value. In this embodiment of the invention, the average drilling rate for the last 2 meters before the end of the hole is extracted from the drilling record. Obtain the current mud specific gravity and viscosity from the mud performance test record; query the particle size distribution data of the formation at the bottom of the borehole from the voxel parameter library in step S13, and extract the median particle size. With soil particle density Substitute the modified Stokes formula to calculate the settling velocity of soil particles in the mud, and make corrections considering the influence of mud thixotropy on viscosity; substitute the drilling rate, hole diameter, particle size, and mud parameters into the coupled prediction formula to calculate the initial sediment thickness at the end of the hole; calibrate by combining the regression coefficients of historical engineering data, and output the predicted sediment thickness and confidence interval.

[0058] It should be noted that the calculation formula for the multi-factor coupled prediction model of sediment thickness is as follows: ; In the formula, The initial sediment thickness, This is an empirical correction factor. This represents the average drilling speed at the final stage of hole drilling. The diameter of the pile hole. The median particle size of the formation at the bottom of the borehole. The dynamic viscosity of the mud. The specific gravity of the mud. This refers to the particle settling velocity; The formula for calculating the free settling velocity of particles is: ; In the formula, For soil particle density, This is the acceleration due to gravity.

[0059] Step S42, Inversion Calculation of Hole Cleaning Target Parameters: Using the target sediment thickness required by the design as input, based on the sediment settling and suspension dynamics model, invert and calculate the three key parameters required to achieve the target thickness: mud circulation flow rate, reverse circulation suction port negative pressure, and expected hole cleaning time, and generate the initial parameter scheme for hole cleaning operation. In this embodiment of the invention, the target sediment thickness is input. (Typically 5cm) and the initial sediment predicted in step S41 Determine the ultimate residual sediment thickness based on the stratigraphic particle composition. The residual value is larger in fine-grained formations and smaller in sandy layers; a reasonable range for mud circulation flow rate is set, and a benchmark flow rate is selected based on the capacity of the reverse circulation equipment. Substitute the values ​​into the inversion formula of the sediment attenuation model to calculate the estimated cleaning time required to reach the target thickness. Based on the relationship between flow rate and pressure, the required negative pressure setting value of the reverse circulation suction port is calculated; taking into account both the hole cleaning efficiency and mud energy consumption, the optimal combination of the three hole cleaning parameters of flow rate, pressure, and time is output.

[0060] It should be noted that the specific formula for the sediment thickness decay model with hole cleaning time is as follows: ; In the formula, for The thickness of sediment over time, The maximum residual sediment thickness, The initial sediment thickness, The slag removal efficiency coefficient, This refers to the mud circulation flow rate. This represents the total volume of the pile hole.

[0061] The formula for reversing the target hole clearing time is: ; In the formula, For the target sediment thickness, The hole cleaning time required to achieve the target.

[0062] The formula for the relationship between reverse circulation suction port pressure and flow rate is: ; In the formula, For traffic, For flow coefficient, The cross-sectional area of ​​the suction port is... The suction port has a negative pressure difference. This refers to the density of the mud.

[0063] Step S43, Dynamic parameter matching during hole cleaning process: Adjust the mud circulation flow rate and reverse circulation pressure in stages according to the progress of hole cleaning and the change of mud sand content in the hole; use a large flow rate to quickly discharge slag in the early stage of hole cleaning, and gradually reduce the flow rate when approaching the target thickness to avoid repeated suspension of sediment caused by turbulent flow at the bottom of the hole. In this embodiment of the invention, the hole cleaning process is divided into three stages: rapid slag discharge, transitional deceleration, and slag stabilization to meet standards. In the initial stage of hole cleaning, the slag is relatively thick, and a large amount of coarse slag is discharged quickly at the maximum flow rate to improve hole cleaning efficiency. When the slag thickness drops to twice the target value, the transition stage begins, and the flow rate decreases linearly with the slag thickness to gradually reduce the flow velocity at the bottom of the hole. When the target thickness is approached, the flow rate is switched to a low-flow slag stabilization mode to prevent the turbulent flow at the bottom of the hole from stirring up the settled fine particles again. Throughout the process, the shear velocity at the bottom of the hole is controlled to not exceed the critical velocity for particle initiation, ensuring that the slag only decreases and does not increase.

[0064] Step S44, Reverse circulation hole cleaning operation execution and real-time monitoring: Start the reverse circulation hole cleaning system according to the optimized parameters, and collect data on the sand content of the mud in the hole and the thickness of the sediment at the bottom of the hole in real time through the mud inlet and outlet density difference sensor and ultrasonic sediment detector, and update the sediment distribution status in the BIM model simultaneously. In this embodiment of the invention, the reverse circulation mud pump and slag suction system are started according to optimized parameters, and the bottom of the guide pipe is kept at a set distance from the bottom of the borehole; density sensors are installed at the mud inlet and outlet to measure the density difference in real time, and the current sand content of the mud is calculated, which indirectly reflects the slag discharge rate; an ultrasonic sediment detector is lowered into the borehole to measure the actual thickness of the sediment at the bottom of the borehole through echo time difference, with a sampling frequency of once per minute; the real-time sediment data is synchronously transmitted to the BIM digital twin platform to update the three-dimensional distribution of sediment at the bottom of the borehole and visualize the cleaning progress; the slag cleaning efficiency coefficient is dynamically corrected according to the deviation between the measured sediment thickness and the predicted curve. , calibrate remaining time prediction.

[0065] Step S45, Sediment Compliance Judgment and Hole Cleaning Termination Control: The real-time monitored sediment thickness is continuously compared with the design threshold. When the thickness requirement is met for multiple consecutive sampling cycles and the mud sand content is lower than the control value, the hole cleaning is judged to be up to standard, the hole cleaning operation is automatically stopped, and a hole cleaning quality report is output.

[0066] In this embodiment of the invention, sediment thickness monitoring data is continuously collected and compared with a design threshold. Real-time comparison is performed; when the sediment thickness is detected to meet the standard for the first time, the process does not stop immediately, but starts a continuous verification window, usually taking 5 consecutive sampling cycles for about 5 minutes; the verification window checks three judgment conditions at the same time: the thickness is consistently up to standard, the sand content is below the limit, and the thickness change rate is close to stabilizing; if all three conditions are met, the hole cleaning is judged to be up to standard, and a stop command is issued to shut down the reverse circulation system; a hole cleaning quality report is generated, including the initial sediment, the final sediment, the hole cleaning time, the sand content change curve and the comprehensive quality score, and archived to the BIM construction file.

[0067] It should be noted that the criteria for continuous compliance include: Condition 1: For all (continuous All sampling points met the standards. Condition 2: (The sand content of the mud is lower than the limit). Condition 3: (The rate of change of sediment thickness approaches zero, reaching a stable state). The hole cleaning standard is determined when all three conditions are met simultaneously.

[0068] Compared to traditional methods that rely solely on a single rope reading for borehole cleaning termination, which are prone to misjudgment due to instantaneous fluctuations and result in excessive sediment rebound after drilling, this method requires that thickness, sand content, and stability indicators be met simultaneously, and that verification be performed over multiple consecutive cycles to ensure that the sediment has indeed reached a stable and compliant state, eliminating false compliance phenomena. The stable compliance determination effectively avoids sediment rebound problems during the lowering of the reinforcing cage and installation of the guide pipe, and has significant engineering value in ensuring the stability of the bearing capacity at the end of ultra-long piles.

[0069] Step S5: BIM-based intelligent float point optimization and grouting simulation: Based on the pile diameter, guide pipe layout scheme and stratum permeability characteristics, the number of float measuring points and planar layout are optimized using a spatial coverage algorithm; a digital twin scenario of concrete grouting is established in the BIM model, and the grouting rate, guide pipe lifting rhythm and float response threshold are preset; BIM virtual simulation of the grouting process is performed to predict the concrete diffusion pattern, guide pipe burial depth change and slurry layer thickness distribution; Step S5 specifically includes the following sub-steps: Step S51, Calculation of effective monitoring radius of float and formation permeability correction: Determine the reference monitoring radius of float based on pile diameter and concrete slump, and then correct it by combining the lateral permeability coefficient of the surrounding formation and the proportion of grouting height to obtain the actual effective monitoring radius considering the formation permeability effect, which serves as the basic parameter for point optimization. In this embodiment of the invention, the reference monitoring radius and slump of a single float are determined based on the designed pile diameter and the designed concrete slump. The time reference coverage factor is set to 0.8; the lateral permeability coefficients of each stratum within the pile body range are extracted from the voxel parameter library in step S13, and the average lateral permeability coefficient of the entire pile is obtained by thickness-weighted averaging; considering that during the grouting process, as the grouting height increases, the lateral filtration effect accumulates, causing the concrete rise in the edge area to lag, and the effective monitoring radius shrinks as the grouting height increases; the effective monitoring radius under different grouting heights is calculated by substituting into the correction formula, and the minimum radius corresponding to the most unfavorable working condition (maximum grouting height) is taken as the input parameter for the point layout optimization; a lower limit protection is set for the calculation results to ensure that the effective monitoring radius of each float is not less than 0.3 times the pile diameter; It should be noted that the formula for calculating the benchmark monitoring radius is: ; In the formula, As the benchmark monitoring radius, The diameter of the pile hole. The baseline coverage factor is determined based on the concrete slump; a higher slump indicates better fluidity. The larger the value, the better.

[0070] The formula for calculating the effective monitoring radius after formation permeability correction is: ; In the formula, To effectively monitor the radius, The lateral permeability coefficient of the soil surrounding the pile. For reference permeability coefficient, This is the current injection height. This represents the total pile length. When the calculated result is less than the lower limit, the lower limit is used to ensure the minimum effective monitoring range of each float.

[0071] Step S52, Optimization of Spatial Coverage Layout: With the goal of full coverage of the pile hole cross section and the constraints of the number of floats and equipment cost, establish a layout optimization model to maximize spatial coverage; use intelligent optimization algorithm to solve for the optimal number of measuring points and the planar coordinate position of each float, minimizing the number of measuring points while ensuring monitoring coverage; In this embodiment of the invention, parameters such as pile diameter, effective monitoring radius, and cost weighting coefficient are input to establish a circular coverage optimization problem within a circular area; the simulated annealing algorithm is used to solve the problem, with initial random generation... For each measurement point, calculate the current coverage and objective function value; randomly move a measurement point to a new location. If the objective function increases, accept the new solution; if it decreases, accept it with a certain probability to avoid getting trapped in local optima; gradually decrease the acceptance probability as the number of iterations increases, stop iterating when the temperature decays to a set value, and output the current optimal point layout scheme; and then... The solution is repeated for different numbers of measuring points, and a coverage-number curve is plotted. The cost weight is combined to determine the economically optimal number of measuring points and their corresponding coordinates. The optimization results are output as a plane coordinate table for each float to guide on-site installation and positioning.

[0072] Step S53, BIM-based digital twin scenario construction and parameter preset: Import the pile hole geometry model, guide pipe layout model and optimized float measuring point position into the BIM platform to construct a concrete pouring digital twin scenario; preset key construction parameters such as initial pouring rate, guide pipe burial depth control range, guide pipe segment lifting rhythm and float tension response threshold. In this embodiment of the invention, the three-dimensional model of the pile hole and the parametric model of the guide pipe are imported into the BIM platform. Following step S52, the coordinates of five float sensor models are optimized and placed. The data interfaces of each float measuring point are bound, and mapping channels for three types of real-time data—tension, rope length, and elevation—are defined. The volume of concrete required for the first batch of pouring is calculated to ensure that the burial depth of the guide pipe after the first batch of pouring is not less than 1.5m. Control parameters for the pouring process are set, including the target burial depth range. The single-section guide pipe is lifted to a height of 3m, and the tensile threshold for judging the concrete surface of the float is set. The density threshold for judging the slurry layer and the over-filling height control value are set to complete the preset of all parameters for the digital twin scenario.

[0073] Step S54, Coupled Simulation of Concrete Diffusion and Rise Process: Based on the rheological properties of concrete and the preset grouting rate, numerical simulation of the concrete diffusion and rise process in the pile hole is carried out to simulate the concrete surface morphology, tremie pipe burial depth variation, laitance layer thickness distribution and concrete velocity field distribution under different grouting heights. In this embodiment of the invention, rheological parameters such as concrete slump and spread are input to determine the diffusion shape index. The greater the slump, The larger the value, the flatter the concrete surface; time-step simulation is performed according to the preset pouring rate, and the volume of injected concrete is calculated at each time step to update the concrete surface height distribution; considering the influence of the duct position on diffusion, the concrete rises fastest directly below the duct and gradually slows down towards the edge, forming a curved surface shape that is high in the center and low at the edge; the duct is raised section by section according to the lifting rhythm, and the duct burial depth is calculated in real time, and the burial depth time history curve is output; the laitance layer thickness distribution is calculated according to the difference in the rising rate at each location, with the edge area rising slowly and the laitance accumulating thicker, while the center area rising quickly and the laitance is thinner; the simulation results are rendered into the BIM model in the form of a cloud map to intuitively display the concrete surface shape and laitance distribution.

[0074] It should be noted that the formula for the radial diffusion profile model of concrete is: ; In the formula, radius The height of the concrete surface at that location The height of the concrete at the center of the pile. Where is the pile radius, The diffusion shape index reflects the fluidity of concrete; the higher the value, the flatter the top surface.

[0075] Step S55: Analysis of Pre-cast Grouting Results and Iterative Optimization of Scheme: Extract the simulation output of the tremie pipe burial depth time history curve, laitance layer spatial distribution, and concrete surface smoothness index, and compare them with the design control requirements; if there are problems such as excessive burial depth or excessive laitance thickness, adjust the grouting rate or tremie pipe lifting rhythm, re-perform simulation verification, iterate until the optimal scheme is reached, and then output the final grouting construction parameter table; like Figure 8 As shown in the embodiment of the invention, the time history curve of the duct burial depth is extracted from the simulation results, the proportion of time the burial depth is within the design range is statistically analyzed, and the burial depth qualification rate is calculated; the thickness distribution of the slurry layer at the pile top is extracted, and the maximum thickness, minimum thickness, and uniformity index are calculated; efficiency indexes such as total grouting time and average grouting rate are extracted; the three indexes are weighted and summed to obtain the comprehensive score of the scheme, which is compared with the preset qualification threshold; if the burial depth qualification rate is too low, the duct lifting rhythm is adjusted, the single lifting height is reduced, or the lifting interval is shortened; if the slurry uniformity is poor, the grouting rate is appropriately reduced to improve the diffusion uniformity; after adjusting the parameters, the simulation is re-executed, and iterative optimization is carried out until the comprehensive score meets the standard, and the final grouting construction parameter table and pre-simulation report are output.

[0076] Step S6: BIM Digital Twin Grouting Closed-Loop Control Driven by Float Measurement: Install an intelligent float elevation measurement system according to the optimized layout, and collect float tension, rope length and elevation data at each measuring point in real time; automatically identify the interface between the mud layer, laitance layer and concrete layer based on the buoyancy gradient change, and dynamically determine the top surface elevation of the concrete; transmit the measured elevation, duct depth and concrete diffusion curve back to the BIM digital twin platform in real time to drive the model to update synchronously; according to the duct depth value calculated in real time by the BIM model, dynamically adjust the grouting rate and duct lifting speed through the PID controller to keep the duct depth stable within the design range; Step S6 specifically includes the following sub-steps: Step S61, Installation and initial calibration of the intelligent float system: Install each set of intelligent float elevation measurement system at the optimized layout location according to step S52, and lower the float into the mud in the hole; perform initial zero-point calibration and self-weight calibration, record the initial tension value of each float in the mud, and establish the measurement benchmark for each measuring point; In this embodiment of the invention, each set of float measurement systems, including a bracket, winding wheel, encoder, weighing sensor, and guide wheel, is installed at the borehole according to the optimized layout coordinates. The float is slowly lowered to a certain depth below the mud surface. After the tension reading stabilizes, the initial value is recorded to complete the zero-point calibration. Based on the measured mud density and float volume, the initial tension is theoretically calculated and compared with the measured value. If the deviation exceeds 2%, recalibration is performed. The initial encoder reading, initial tension value, and mud density benchmark of each measuring point are recorded to establish an independent measurement benchmark file for each measuring point. The data transmission link is tested to confirm that the data of each measuring point can be transmitted to the BIM digital twin platform in real time.

[0077] Step S61 involves in-situ calibration by lowering the float into the mud on-site. The measured initial tensile force is used as the benchmark for stratification, eliminating the influence of mud density fluctuations and float processing errors. Independent calibration at each measuring point ensures that the judgment threshold of each float accurately matches its own characteristics, providing a reliable benchmark for subsequent high-precision stratification identification.

[0078] Step S62, Buoyancy Gradient Layer Identification and Concrete Surface Elevation Determination: During the pouring process, continuously collect the buoy tension data and calculate the gradient of tension change with depth; when the tension gradient changes abruptly and the absolute value exceeds the set threshold, it is determined that the buoy has reached the layer interface; based on the magnitude of the tension change, distinguish the three media layers of mud layer, laitance layer and concrete layer, and lock the precise elevation of the top surface of the concrete. In this embodiment of the invention, after the grouting begins, the rising concrete surface pushes the float to rise synchronously, and the tension changes continuously with the density of the medium in which the float is located. A sliding window differential calculation is performed on the tension data to obtain the time history curve of the tension gradient. When the tension gradient shows its first abrupt change and the tension value decreases, it is determined that the flow has entered the laitance layer from the mud layer, and the elevation of the top surface of the laitance layer is recorded. Monitoring continues, and when the tension gradient shows a second, larger abrupt change and the tension drops sharply to near zero, it is determined that the flow has entered the concrete layer from the laitance layer, and the elevation of the top surface of the concrete is locked. An elevation locking signal is triggered, and the current rope length reading is used as the concrete surface elevation at that measuring point, while the laitance layer thickness is recorded. During subsequent grouting, the rise of the concrete surface is continuously tracked, and the elevation value is updated every certain distance.

[0079] It should be noted that the formula for calculating the rate of change of buoyancy gradient is: In the formula, The tensile force values ​​are between two adjacent sampling times. For the corresponding depth, This represents the gradient of tensile force as a function of depth.

[0080] The characteristic values ​​of tensile force in the three-layer medium are as follows: Mud layer: (High tensile strength); Floating slurry layer: (Medium tensile strength) ); Concrete layer: (The tension is close to zero,) (The buoyancy is greater than its own weight). The stratification criteria are as follows: when and When a value falls within the corresponding range, it is determined that the corresponding layer interface has been traversed.

[0081] Step S63, Dual encoder Kalman filter dynamic elevation calibration: The length of the float lowering rope is measured by both an absolute encoder and an incremental encoder. The two data are fused and filtered by the Kalman filter algorithm. At the same time, the wire rope temperature drift compensation is introduced to dynamically correct the elevation measurement value and output real-time elevation data with millimeter-level accuracy. In this embodiment of the invention, an absolute encoder provides an absolute position reference, which can obtain the current rope length upon power-up. It has no cumulative error but has limited resolution. An incremental encoder provides high-resolution relative displacement with high accuracy but has cumulative drift. The data from both encoders are used as observation inputs. The cumulative drift is corrected by the absolute encoder value, and the time resolution is improved by the incremental encoder value. A state equation for elevation change is established, and the current elevation is predicted based on the previous elevation and the rising speed. The Kalman gain is calculated, and the predicted and observed values ​​are fused to obtain the optimal estimated elevation, while the error covariance is updated. A temperature sensor is introduced to measure the ambient temperature, and the elevation is corrected for temperature based on the linear expansion coefficient of the wire rope to eliminate systematic errors caused by changes in ambient temperature. The final elevation data after filtering and compensation is output, with a sampling frequency of 1Hz and a measurement accuracy of ±1mm.

[0082] Step S64, Multi-point elevation surface fitting and real-time update of BIM digital twin: Transmit the real-time elevation data of each measuring point to the BIM digital twin platform, use the quadratic surface fitting algorithm to reconstruct the full-section concrete surface morphology, and update the state parameters such as the duct embedment depth, concrete diffusion morphology and laitance distribution in sync, driving the BIM model to evolve synchronously with the on-site construction. In this embodiment of the invention, real-time elevation data of each measuring point is transmitted to the BIM digital twin platform via a bus, with a data refresh frequency of once per second. The coordinates and corresponding elevations of five measuring points are collected to form an overdetermined system of equations with six unknowns (five measuring points + central symmetry constraints). The least squares method is used to solve for the quadratic surface coefficients, reconstructing the three-dimensional shape of the concrete surface of the entire pile section. The local average elevation at the location of the guide pipe is calculated, and the real-time burial depth of the guide pipe is obtained by combining it with the bottom depth of the guide pipe. The thickness distribution of the laitance layer and the concrete diffusion shape are calculated based on the surface morphology, and the corresponding parameters in the BIM model are updated synchronously. The updated concrete surface, laitance layer, guide pipe burial depth, and other states are rendered and displayed in a three-dimensional visualization form, realizing the synchronization between the digital twin and the site.

[0083] Step S65, Real-time Calculation of Conduit Burial Depth and PID Dual-Variable Cooperative Control: Based on the real-time calculated conduit burial depth value from the BIM model, the midpoint of the target burial depth range is used as the control target. The PID controller simultaneously adjusts two variables: the conduit lifting speed and the concrete pouring rate. When the burial depth is too large, the lifting speed is increased and the pouring speed is decreased; when the burial depth is too small, the lifting speed is decreased and the pouring speed is increased. The dual-variable cooperative control keeps the conduit burial depth stably controlled within the design range. In this embodiment of the invention, the real-time duct burial depth value is read from the BIM digital twin platform and compared with the target burial depth of 4m to obtain the control deviation. The PID controller calculates the adjustment amount of the duct lifting speed based on the deviation; if the burial depth is too large, the lifting speed is increased, and if the burial depth is too small, the lifting speed is decreased. The concrete surface rising rate is monitored simultaneously, and the output flow rate of the grouting pump is adjusted accordingly; if the rising speed is too fast, the grouting volume is reduced, and if the rising speed is too slow, the grouting volume is increased. The two variables, duct lifting speed and grouting rate, are regulated in a coordinated manner, one controlling the duct position and the other controlling the concrete supply, to jointly maintain a stable burial depth. The control output is limited and smoothed to avoid fluctuations in the concrete surface caused by sudden speed changes. Continuous closed-loop control ensures that the duct burial depth remains stable at a certain value. Within the designed range, the entire process operates automatically without human intervention.

[0084] Step S66, Grouting Anomaly Early Warning and Emergency Control: Real-time monitoring of the consistency of elevation, abnormal fluctuation of tension and rate of change of burial depth at each measuring point, and setting multi-level early warning thresholds; when a risk signal of pile breakage such as a sudden drop in burial depth or abnormal elevation difference is detected, the emergency control program is automatically triggered to suspend pipe lifting and increase the grouting volume, and quickly restore the burial depth of the guide pipe to a safe range.

[0085] In this embodiment of the invention, three types of abnormal indicators are monitored in real time: consistency of elevation at each measuring point, rate of change of burial depth, and abnormal fluctuation of tension. A three-level early warning threshold is set: a minor abnormality triggers a yellow warning with no action required; a moderate abnormality triggers an orange warning and slows down the pipe lifting speed; and a severe abnormality triggers a red warning and initiates an emergency procedure. During a red warning, pipe lifting is immediately suspended, and the flow rate of the grouting pump is increased to quickly replenish concrete, causing the burial depth to rapidly recover to a safe range. During the emergency, the recovery of burial depth is continuously monitored; once the burial depth recovers to more than 3 meters, the emergency state is lifted, and the normal grouting rhythm is gradually restored. The occurrence time, duration, handling process, and recovery status of abnormal events are recorded, and an abnormal event report is automatically generated and archived. The entire abnormality handling process is automatically executed by the system without manual decision-making, saving valuable emergency response time.

[0086] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a construction method for ultra-long pile foundations based on a BIM data model, as provided in Embodiment 2 of the present invention.

[0087] Example 2, based on Example 1, adds an evaluation and verification of the construction process after the construction of ultra-long pile foundations. The specific process includes: Step S7: Concrete quality BIM assessment based on float data inversion: Based on the concrete surface elevation data measured by multi-point floats, the concrete diffusion radius and flowability parameters are calculated by inversion; the inversion parameters are substituted into the BIM model to correct the concrete diffusion field distribution and generate a heat map of the concrete quality at the pile top; the quality of the pile head concrete is assessed based on the distribution of the laitance layer thickness and the overfill height, and the scope and depth of removal are determined. Step S7 specifically includes the following sub-steps: Step S71: Data Acquisition and Preprocessing of Multi-Point Float Ball Measured Elevation: After the grouting is completed, extract the final concrete surface elevation data of the pile top recorded by each float ball measuring point; remove abnormal data points such as stuck or jumping, and perform spatial coordinate matching on the valid measuring points to form an elevation measured dataset for inversion calculation; In this embodiment of the invention, the final concrete surface elevation value at the end of the pouring and the corresponding plane coordinates are extracted from the historical records of each float measuring point; the mean and standard deviation of the elevation of all measuring points are calculated, and the 3σ criterion is used to identify abnormal measuring points and eliminate erroneous data caused by float jamming or sensor failure; abnormal measuring points are marked and removed, and valid measuring points are retained to form a measured elevation dataset; the plane coordinates of each measuring point are uniformly transformed to the local coordinate system of the pile hole to ensure consistency with the BIM model coordinate system; the number of valid measuring points is checked to see if it meets the inversion requirements (at least 3), and if not, interpolated data from nearby times are added.

[0088] Step S72, Concrete diffusion parameter inversion calculation: Using the measured elevation of each measuring point as known conditions, based on the concrete radial diffusion profile model, the nonlinear least squares algorithm is used to invert and solve the two key parameters of concrete diffusion shape index and center height; the actual flow characteristics of concrete in the pile hole are restored by the inversion parameters, and the workability of concrete is evaluated. In this embodiment of the invention, the preprocessed measured elevation dataset and geometric parameters such as pile radius are input; initial guess values ​​for the parameters to be inverted are set. The initial value is the elevation of the center measuring point. The initial value is set to 2.0; the model-predicted elevation of each measuring point is calculated based on the current parameters, and the residual vector is obtained by comparing it with the measured values; the Jacobian matrix is ​​calculated, and the parameter increment is solved using the Gauss-Newton method to update the parameter values; it is determined whether the residual sum of squares has converged. If it has not converged, the process returns to step three to continue iterating. The iteration is typically repeated... It can converge in one pass; output the diffusion shape index after convergence. With center height This serves as a quantitative characterization of the actual diffusion properties of concrete.

[0089] Step S73, BIM Concrete Diffusion Field Correction and Quality Heat Map Generation: Substitute the diffusion parameters obtained from the inversion into the BIM model to correct the original simulation prediction of the concrete diffusion field distribution; calculate the density distribution index of the entire cross section of the pile top based on the differences in concrete rise rate and residence time at each location, generate a quality heat map of the pile top concrete and display it visually. like Figure 7 As shown in the embodiment of the present invention, the inverted result is... and The parameters are input into the BIM model to replace the original simulation-predicted diffusion parameters; the elevation distribution of the concrete surface of the entire pile top section is recalculated based on the measured inversion parameters to correct the original diffusion field; the rising rate and residence time distribution of concrete at each location are calculated by combining the time history data of the pouring process; the density index distribution of the entire section is calculated according to the correlation model between residence time and density; the density index is mapped to a color gradient to generate a heat map of the concrete quality at the pile top, with green indicating dense and high-quality, yellow indicating transition, and red indicating weak; the heat map is superimposed on the BIM pile top model to display the three-dimensional visualization of the pile head quality distribution.

[0090] It should be noted that the formula for reconstructing the diffusion field across the entire cross-section is: ; In the formula, The coordinates of the pile top section are The concrete surface elevation at any location. The reference height of the concrete surface at the center of the pile cross-section. The design radius of the pile hole, This is the concrete diffusion shape index.

[0091] Substitute the inversion parameters into the diffusion model to calculate the concrete surface elevation at any location across the entire cross section.

[0092] The formula for the density distribution index is: ; In the formula, For density index ( (The larger and denser) This refers to the dwell time of the concrete during its ascent at that location. Total infusion time, This is the influence coefficient. The longer the residence time, the more laitance and bleed water accumulate, and the lower the density; the specific quality grade classification is as follows: It is a high-quality area; This area is considered qualified. This is a weak area; The specific radial distribution data table is as follows: Traditional BIM-based grouting simulations are merely pre-construction predictions, resulting in discrepancies with actual construction. This method utilizes parameters derived from real buoy data to inversely correct the BIM model, ensuring precise alignment between the digital twin model and the actual construction conditions. The density heatmap transforms abstract quality concepts into intuitive spatial distribution images, allowing construction personnel to immediately identify areas of good quality and weak points, providing accurate visual guidance for subsequent pile head treatment.

[0093] Figure 7 The left side shows a radial cross-sectional view of the concrete quality at the pile top. This vertical cross-section along any diameter of the pile hole visually illustrates the morphology of the concrete surface and the radial variation of the laitance layer thickness. The diagram depicts the interface between the concrete layer and the laitance layer at the pile top, showing a curved surface with a higher center and lower edges, corresponding to the flow pattern of concrete diffusing from the tremie pipe opening. The laitance layer thickness values ​​at different radial positions are also labeled (unit: ...). ) and density index The numerical values ​​correspond one-to-one with the radial distribution parameter table.

[0094] Figure 7 The right side shows a heat map of the concrete density quality at the pile top (top view), a full-section visualization of the pile head quality. The pile hole boundary is represented by a circular outline, and the density distribution is characterized by a combination of grayscale (or color gradation) gradients and concentric contour lines: darker colors represent higher density indices and better concrete quality; lighter colors represent lower density and a higher proportion of laitance. Due to the centrosymmetric nature of concrete diffusion, the heat map exhibits a regular concentric ring distribution, with the central area representing a high-quality, dense zone, transitioning outwards to a qualified zone and then a weaker edge zone, perfectly corresponding to the variation pattern of the left-side profile. Based on the diffusion shape index and center height parameters obtained from the measured elevation of multi-point floats, the density index at any location is calculated using the full-section diffusion field reconstruction formula, and then mapped to color gradations to generate the heat map.

[0095] Step S74, Latex Layer Thickness Distribution and Pile Head Compaction Assessment: Based on the elevation difference between the top surface of the slurry layer and the concrete surface recorded at each measuring point during the grouting process, calculate the spatial distribution of the slurry layer thickness across the entire cross section; and assess the quality grade of the pile head concrete by region based on the dual indicators of slurry thickness and compactness index. In this embodiment of the invention, the elevation of the top surface of the laitance layer and the elevation of the concrete surface corresponding to each measuring point are extracted from the layered records of each float measuring point, and the laitance thickness of a single measuring point is calculated; the laitance thickness distribution of the entire cross section is fitted by a radial quadratic polynomial to restore the spatial morphology of the laitance layer; the laitance thickness distribution and the density index distribution are spatially aligned, and the two indicators are superimposed to calculate the comprehensive quality score; the pile top section is divided into three types of areas: high-quality area, qualified area and weak area according to the quality grade threshold; the area ratio of each type of area is statistically analyzed to evaluate the overall quality level of the pile head; the location and range of the weak area are marked, and special attention is paid to areas where laitance is easy to accumulate, such as the edge of the pile hole.

[0096] Step S75, Intelligent Determination and Construction Guidance of Optimal Removal Depth: Using the pile head compaction threshold required by the design as the judgment standard, the concrete quality of the entire cross section is checked layer by layer along the depth direction. The minimum depth at which all locations meet the compaction requirements is found, which is the optimal removal depth. The removal depth guidance value and the mark of the weak quality area are output to guide the subsequent pile head breaking construction.

[0097] In this embodiment of the invention, the input is the required pile head compaction threshold, which is typically required. Based on the surface density distribution and depth attenuation model, calculate the full cross-section density distribution at each depth layer by layer from the pile top downwards; check whether all locations on each depth section meet the density requirements, and weak areas need to be removed more deeply; find the minimum depth at which the entire cross-section meets the standard at the same time, which is the optimal removal depth of the unified removal scheme; if differentiated removal is allowed, a non-uniform removal surface can be generated according to the standard depth at each location to further reduce waste; output the removal depth guidance value, weak area marking map and expected removal volume to generate a pile head removal construction guide.

[0098] It should be noted that the density decay model along depth is as follows: ; In the formula, This is the depth from the top of the pile. This represents the density distribution on the pile top surface. This is the depth recovery coefficient; the density increases the deeper you go.

[0099] Step S8, Time-varying Settlement Prediction and Bearing Capacity Grading Verification: After pile formation, collect pile integrity test data and pile end grouting data to establish a single pile time-varying settlement calculation model; considering the soil consolidation time and the gradual development characteristics of pile side friction, predict the long-term settlement value of the pile foundation in stages; compare the prediction results with the design allowable value to determine whether the single pile bearing capacity meets the requirements; if not, start the pile supplementation or reinforcement procedure.

[0100] Step S8 specifically includes the following sub-steps: Step S81: Pile Parameter Acquisition and Calculation Model Initialization: After pile formation, collect pile integrity test data, pile tip grouting data, and pile concrete strength test data. Combine the layered parameters in the geological model in Step S1 to initialize the single pile time-varying settlement calculation model. Input the side skin resistance limit value, utilization rate coefficient, and pile tip consolidation parameters of each soil layer around the pile into the model. In this embodiment of the invention, pile testing data is collected, including low-strain integrity test results, concrete test block strength reports, and pile tip grouting construction records; the pile elastic modulus reduction coefficient is determined according to the integrity test level, with no reduction for Class I piles and appropriate reduction for Class II piles; physical and mechanical parameters of the surrounding strata are extracted from the voxel parameter library in step S13, including ultimate side skin friction, compression modulus, and permeability coefficient; and the side skin friction utilization rate coefficient is determined based on the permeability coefficient and consolidation characteristics of each soil layer. The higher the permeability coefficient The larger the value, the faster the effect; estimate the grouting reinforcement effect based on the grouting volume at the pile tip, and determine the improvement coefficient of the soil compression modulus at the pile tip; organize all parameters according to the stratum number and input them into the time-varying settlement calculation model to complete the model initialization.

[0101] Step S82, Calculation of the time-varying development law of side friction resistance: Based on the load transfer principle, considering the influence of different soil layer permeability characteristics on the development rate of side friction resistance, the gradual development process of side friction resistance at each depth over time is calculated; the friction resistance of sandy soil with good permeability develops quickly, while the friction resistance of clay soil with poor permeability develops slowly, forming a time-varying side friction distribution curve along the depth. In this embodiment of the invention, the ultimate side friction resistance and permeability coefficient of each soil layer are input, and the friction resistance utilization rate coefficient of each layer is calculated; for any given time... The system calculates the current side friction resistance at each location point by point according to depth, gradually increasing from 0 to the limit value. The increase is faster in sandy soil and slower in clay soil. The side friction resistance is accumulated by integrating from the top of the pile downwards to obtain the time history curve of the axial force distribution of the pile body at each depth. Based on the axial force distribution and the elastic modulus of the pile body, the change of the elastic compression of the pile body with time is calculated by integrating along the pile body. At the same time, the additional settlement caused by the relative displacement of the pile and soil is calculated and combined with the elastic compression to obtain the settlement component related to the side friction resistance. The system outputs three time-varying results: the side friction resistance utilization rate, the axial force distribution of the pile body, and the compression of the pile body.

[0102] Step S83, Time-varying calculation of pile end consolidation settlement: Based on the compression modulus and consolidation coefficient of the underlying soil layer, the one-dimensional consolidation theory is used to calculate the consolidation settlement development process of the pile end soil over time; considering the improvement effect of pile end grouting on soil compressibility, the pile end settlement calculation parameters are corrected to obtain the time-varying curve of pile end settlement. In this embodiment of the invention, the axial force at the pile end section is extracted as the pile end reaction force from the pile body axial force calculation result in step S82. Determine the thickness and compression modulus of the main compressible layer beneath the pile tip, and multiply by the improvement factor to consider the reinforcement effect of pile tip grouting; calculate the final consolidation settlement of the pile tip as the upper limit of settlement development; calculate the time factor at different times based on the consolidation coefficient, and substitute it into Terzaghi's consolidation degree formula to obtain the degree of consolidation at each time; multiply the final settlement by the degree of consolidation to obtain the time-varying curve of pile tip settlement over time; for multiple underlying soil layers, calculate each layer and then superimpose the results to obtain the total pile tip consolidation settlement time history.

[0103] Step S84, Time-varying prediction and graded verification of total settlement of a single pile: The relative displacement of the pile and soil caused by the elastic compression of the pile body, the exertion of lateral resistance, and the consolidation settlement at the pile end are superimposed to obtain the prediction curve of the total settlement of a single pile over time; graded verification is carried out according to three time nodes: short-term, medium-term, and long-term, and the value is compared with the allowable settlement value of the corresponding stage to determine whether the bearing capacity meets the design requirements. In this embodiment of the invention, three time-varying curves—elastic compression of the pile body, lateral resistance settlement, and pile end consolidation settlement—are aligned and superimposed over time to obtain a total settlement time-varying curve. Predicted settlement values ​​for three time nodes—7 days, 90 days, and 365 days—are read from the total settlement curve. The predicted values ​​for each of the three nodes are compared with the design allowable settlement values ​​for the corresponding stages. Short-term verification mainly examines construction quality and pile integrity; medium-term verification examines settlement control during the main construction period; and long-term verification examines whether post-construction settlement meets building usage requirements. If all three levels of verification pass, the single pile bearing capacity is deemed to meet the requirements, and a verification qualification report is output. If any level fails to meet the requirements, the next step of the reinforcement decision-making process is initiated.

[0104] Step S85, Decision on reinforcement when bearing capacity is insufficient: If the predicted settlement exceeds the allowable value at any stage of the graded verification, the reinforcement scheme evaluation is automatically started. The required additional bearing capacity is calculated based on the deviation range, and the optimized suggestions for the number of additional piles, the location of additional piles, or grouting reinforcement at the pile end are output to guide the subsequent reinforcement construction.

[0105] In this embodiment of the invention, the settlement deviation during the exceeding stage of the graded verification is read to assess the severity of the deviation. For minor deviations, the pile tip grouting scheme is evaluated first, and the required amount of additional grout is estimated to reduce settlement by increasing the soil stiffness at the pile tip. When the deviation is significant, the pile supplementation scheme is evaluated, the number of supplementary piles is calculated based on the required additional bearing capacity, and the location of the supplementary piles is optimized in conjunction with the pile cap layout. The construction period, cost, and effect of the grouting reinforcement and pile supplementation schemes are compared, and a recommended scheme is given. The parameters of the reinforcement scheme are backfilled into the settlement model for recalculation to verify whether the settlement after reinforcement meets the requirements. A reinforcement decision report is output, including the recommended scheme, parameter suggestions, and expected effects to guide subsequent reinforcement construction.

[0106] like Figure 5 As shown, Figure 5This is a diagram of a BIM digital twin injection closed-loop control system architecture provided in Embodiment 3 of the present invention.

[0107] The BIM digital twin infusion closed-loop control system of this invention comprises a five-layer architecture, namely: perception layer, data transmission layer, BIM digital twin layer, control layer, and execution layer; each layer contains the following modules: The sensing layer is set up at the pile hole site and includes an intelligent float measuring point module, a dual encoder module, and a tension sensor module. The intelligent float measuring point module is used to lower the float to various measuring points in the pile hole according to the optimized layout, serving as the sensing terminal for the concrete surface elevation. The dual encoder module uses absolute and incremental dual encoders to collaboratively measure the length of the rope lowering the float, providing raw elevation data. The tension sensor module is used to collect changes in the tension on the float in real time, providing a basis for layer identification. The data transmission layer is located between the pile hole and the platform. It includes a data acquisition and preprocessing module, which is used to collect the raw data of tension and rope length at each buoy measuring point, remove outliers and align coordinates, and then transmit them to the BIM digital twin layer. The BIM digital twin layer is the core data processing layer of the system, including the BIM digital twin platform module, the elevation surface fitting module, the tremie pipe embedment depth calculation module, and the concrete diffusion field update module. The BIM digital twin platform module serves as a digital carrier for the geometric model of the pile hole and its construction status. The elevation surface fitting module is used to reconstruct the full-section concrete surface morphology based on multi-point measured elevation data. The tremie pipe embedment depth calculation module is used to calculate the tremie pipe embedment depth in real time based on the concrete surface elevation and the tremie pipe position. The concrete diffusion field update module is used to synchronously update the concrete diffusion morphology and laitance layer distribution. The control layer is the system decision output layer, which includes a PID dual-variable controller module, a duct lifting control module, and a grouting rate control module. The PID dual-variable controller module outputs two control signals with the target burial depth as the set value and the real-time burial depth as the feedback value. The duct lifting control module is used to adjust the duct lifting speed. The grouting rate control module is used to adjust the concrete pumping flow rate. The two control signals work together to maintain the duct burial depth stable within the design range. The execution layer is the on-site construction execution mechanism, which includes a guide pipe lifting mechanism module and a concrete pouring pump module. The guide pipe lifting mechanism module receives lifting control signals and executes the lifting and lowering actions of the guide pipe. The concrete pouring pump module receives flow control signals and executes the adjustment of the concrete pouring volume. The action results of the execution layer are fed back in real time through the sensing layer, forming a complete closed-loop control loop. The emergency early warning layer serves as a safety guarantee branch for the system. It includes an emergency early warning module, which is used to monitor the consistency of elevation, the rate of change of burial depth, and abnormal fluctuations in tension in real time. It sets multiple early warning thresholds and automatically triggers the emergency control program when the risk of pile breakage is detected, suspending pipe lifting and increasing the grouting volume to quickly restore the burial depth.

[0108] It is worth noting that the various units included in the above system embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0109] Furthermore, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the corresponding program can be stored in a computer-readable storage medium.

[0110] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A construction method for ultra-long pile foundations based on BIM data models, characterized in that, Includes the following steps: Collect survey data to construct a three-dimensional geological BIM model and divide it into voxels according to soil layers. Assign physical, mechanical and drillability parameters to each voxel unit, establish the mapping relationship between the pile location and the geological model coordinate system, and generate a single pile stratum profile parameter chain by traversing along the pile axis. Based on the formation profile parameter chain, the characteristic sequences of each formation are extracted. The drilling pressure, rotation speed and mud density parameters are optimized in segments with the dual objectives of maximizing drilling rate and minimizing deviation risk. A pre-optimized drilling parameter table is generated and imported into BIM for hole formation simulation verification. Real-time monitoring of tilt data during drilling is initiated, and the overall verticality deviation is calculated through a depth-weighted algorithm. Combined with adaptive PID dynamic adjustment of drilling pressure and guide vanes based on formation stiffness, closed-loop correction is implemented, and the actual borehole geometry model is reconstructed. Read the hole formation geometry model, combine the hole formation parameters and drilling data to construct a multi-field coupled sediment prediction model, reverse solve the hole cleaning time and cycle parameters corresponding to the target sediment, and perform a secondary hole cleaning operation until the sediment thickness meets the design threshold. Based on the borehole geometry parameters and formation permeability coefficient, the spatial coverage algorithm is used to optimize the number and planar layout of the float measuring points, establish a BIM grouting digital twin scenario, and conduct a grouting process simulation. The intelligent buoy elevation measurement system is installed at optimized locations. It automatically identifies the interface of the three media layers by the change of buoyancy gradient to determine the concrete elevation. The measured data is transmitted back in real time to drive the synchronous update of the BIM model. The PID dual-variable collaborative control keeps the burial depth of the guide pipe stable within the design range, realizing closed-loop control of the grouting.

2. The method for constructing ultra-long pile foundations based on a BIM data model according to claim 1, characterized in that, The specific process for implementing closed-loop correction includes: A set of dual-axis tilt sensors is installed at the top of the drill pipe and at the middle guide joint to monitor the X and Y tilt angles of two different depth sections respectively. A depth-weighted algorithm was used to calculate the overall verticality deviation value and deviation direction of the pile hole, and a depth-deviation coupling relationship was established. Based on the equivalent dip angle components in the X and Y directions, the spatial azimuth angle of the deviation is synthesized and calculated to quantitatively assess the degree of deviation of the current hole formation. Read the formation compression modulus corresponding to the current drilling depth, and dynamically adjust the proportional, integral, and derivative coefficients of the PID controller according to the formation's hardness. The correction direction is determined based on the deviation azimuth angle, and the total drilling pressure is differentially distributed in the circumferential direction. The drilling pressure distribution and guide vane angle are dynamically adjusted according to the deviation value and the current formation parameters to implement closed-loop correction.

3. The method for constructing ultra-long pile foundations based on a BIM data model according to claim 2, characterized in that, The specific method for calculating the overall verticality deviation using the depth-weighted algorithm includes: The X-axis and Y-axis tilt data collected by each tilt sensor are divided into multiple monitoring segments according to the drilling depth. The X-axis tilt, Y-axis tilt, midpoint depth of the monitoring segment, and height of the monitoring segment are obtained for each monitoring segment. The corresponding depth weight is determined based on the midpoint depth of each monitoring segment. The depth weight is positively correlated with the midpoint depth of the monitoring segment, so that the dip angle data of the deep monitoring segment has a higher weight than that of the shallow monitoring segment. The X-axis dip angle data and Y-axis dip angle data of each monitoring section are respectively subjected to depth weighted fusion to obtain the overall X-axis equivalent dip angle and the overall Y-axis equivalent dip angle. Based on the overall X-axis equivalent dip angle and the overall Y-axis equivalent dip angle, the spatial deviation and deviation direction of the hole are determined. The horizontal offset and verticality deviation rate of the pile bottom are determined based on the spatial deviation and the current total hole depth to characterize the overall degree of deviation of the pile hole relative to the designed pile axis.

4. The method for constructing ultra-long pile foundations based on a BIM data model according to claim 2, characterized in that, The method for dynamically adjusting the proportional, integral, and derivative coefficients of the PID controller based on the hardness of the formation includes: Obtain the current drilling depth and read the formation compression modulus corresponding to the current drilling depth from the voxel parameter library of the 3D geological BIM volume model; The formation compression modulus is compared with the preset reference formation compression modulus. The hardness of the current formation is determined based on the change in the formation compression modulus. The proportional coefficient, integral coefficient, and derivative coefficient of the PID controller are adaptively adjusted according to the preset stiffness influence relationship. When the current formation compression modulus changes relative to the reference formation compression modulus, the various control coefficients of the PID controller are corrected accordingly. The difference between the target verticality and the current actual verticality is determined as the verticality control deviation. Based on the verticality control deviation, the cumulative change of the verticality control deviation, and the trend of the verticality control deviation, proportional control quantity, integral control quantity, and derivative control quantity are generated respectively. The proportional control quantity, integral control quantity, and derivative control quantity are superimposed to obtain the correction drill pressure adjustment quantity, and the drill pressure distribution and the working angle of the guide vane are adjusted according to the correction drill pressure adjustment quantity.

5. The method for constructing ultra-long pile foundations based on a BIM data model according to claim 1, characterized in that, The process involves reverse engineering to determine the cleaning time and cycle parameters corresponding to the target sediment, and performing a second cleaning operation until the sediment thickness meets the design threshold. The specific steps include: After the hole is completed, the average drilling speed, final drilling mud viscosity, mud specific gravity, pile diameter and hole depth data of the final drilling stage are collected. Combined with the particle size distribution and clay content parameters of the strata at the bottom of the hole, a multi-factor coupled sediment thickness prediction model is constructed to calculate the initial sediment thickness prediction value. Using the target sediment thickness required by the design as input, and based on the sediment settling and suspension dynamics model, the three key parameters required to achieve the target thickness are calculated in reverse: mud circulation flow rate, negative pressure of the reverse circulation suction port, and expected hole cleaning time. This generates the initial parameter scheme for the hole cleaning operation. Based on the progress of hole cleaning and the changes in the sand content of the mud in the hole, the mud circulation flow rate and reverse circulation pressure are adjusted in stages; in the early stage of hole cleaning, a large flow rate is used to quickly remove slag, and the flow rate is gradually reduced when the target thickness is approached to avoid turbulent disturbance at the bottom of the hole causing repeated suspension of sediment. The reverse circulation hole cleaning system is started according to the optimized parameters. The density difference sensor of mud inlet and outlet and the ultrasonic sediment detector are used to collect data on the sand content of mud in the hole and the thickness of sediment at the bottom of the hole in real time, and the sediment distribution status in the BIM model is updated synchronously. The thickness of the sediment monitored in real time is continuously compared with the design threshold. When the thickness requirement is met for multiple consecutive sampling cycles and the sand content of the mud is lower than the control value, the hole cleaning is deemed to be up to standard, the hole cleaning operation is automatically stopped, and a hole cleaning quality report is output.

6. The method for constructing ultra-long pile foundations based on a BIM data model according to claim 1, characterized in that, The specific process for establishing the BIM-injected digital twin scenario and conducting a pre-simulation of the injection process includes: The reference monitoring radius of the float is determined based on the pile diameter and concrete slump. Then, it is corrected by combining the lateral permeability coefficient of the surrounding strata and the proportion of the grouting height to obtain the actual effective monitoring radius considering the strata filtration effect, which serves as the basic parameter for the optimization of the monitoring point layout. With the goal of full coverage of the pile hole cross section and the constraints of the number of floats and equipment cost, a point layout optimization model is established to maximize spatial coverage. An intelligent optimization algorithm is used to solve for the optimal number of measuring points and the planar coordinate position of each float, minimizing the number of measuring points while ensuring monitoring coverage. Import the pile hole geometry model, the guide pipe layout model, and the optimized float measuring point position into the BIM platform to construct a digital twin scenario for concrete pouring. Based on concrete rheological properties parameters and preset grouting rate, numerical simulation of concrete diffusion and rising process in pile hole is carried out to simulate concrete surface morphology, duct burial depth variation, laitance layer thickness distribution and concrete flow velocity field distribution under different grouting heights. Extract the time history curve of the duct burial depth, the spatial distribution of the slurry layer and the flatness index of the concrete surface from the simulation output, and compare them with the design control requirements; adjust the grouting rate or the duct lifting rhythm, re-perform simulation verification, and output the final grouting construction parameter table after iterating to the optimal scheme.

7. The method for constructing ultra-long pile foundations based on a BIM data model according to claim 6, characterized in that, When optimizing the spatial coverage by maximizing the number of monitoring points, the pile diameter and effective monitoring radius are input to establish a circular coverage optimization model within a circular area. A simulated annealing algorithm is used for the solution. Initially, n monitoring point positions are randomly generated, and the current coverage and objective function value are calculated. When a monitoring point position is randomly moved, if the objective function increases, a new solution is accepted; if it decreases, the monitoring point position is abandoned. Iteration stops when the temperature decays to a set value, and the current optimal monitoring point layout is output. The solution is repeated for different numbers of monitoring points, and a coverage-number curve is plotted. The cost weight is used to determine the economically optimal number of monitoring points and their corresponding coordinates. The optimization results are output as a planar coordinate table for each buoy to guide on-site installation and positioning.

8. The method for constructing ultra-long pile foundations based on a BIM data model according to claim 7, characterized in that, The optimization of float placement employs a spatial coverage maximization algorithm, specifically including: Based on the baseline monitoring radius of a single float, the current grouting height, the total length of the pile hole, and the lateral permeability coefficient of the surrounding strata, the influence of the strata filtration effect on the monitoring range of the float is determined, and the baseline monitoring radius is corrected to obtain the effective monitoring radius of a single float. The proportion of the area within the pile hole cross-section that is effectively monitored by one or more floats to the total area of ​​the pile hole cross-section is used as the coverage evaluation index, and the equipment cost corresponding to the number of floats is used as a constraint factor to establish a deployment optimization target that takes into account both monitoring coverage and the number of equipment. Within the cross-sectional area of ​​the pile hole, a preset number of initial positions of floats are randomly generated, and the coverage and equipment cost evaluation results of the current deployment scheme are calculated. The float position is iteratively adjusted according to the simulated annealing algorithm. When the adjusted placement scheme can improve the overall evaluation result, the placement scheme is accepted. When the overall evaluation result decreases, the placement scheme is accepted with a preset probability based on the current iteration temperature and the degree of change in the evaluation result. Gradually reduce the iteration temperature and repeatedly adjust the float position until the preset stopping condition is reached to obtain the optimal placement scheme for the corresponding number of floats. The deployment of floats is optimized for different numbers of floats, and the monitoring coverage and equipment cost corresponding to each number of floats are compared. Under the condition of meeting the preset coverage requirements, the economically optimal number of floats and their planar coordinates are determined, and the float deployment coordinate table is output.

9. The method for constructing ultra-long pile foundations based on a BIM data model according to claim 1, characterized in that, The specific construction steps for automatically identifying the interface of the three media layers and determining the concrete elevation by means of the buoyancy gradient change, and controlling the immersion depth of the guide pipe to remain stable within the design range, include: Install each set of intelligent float elevation measurement system according to the layout location, and lower the float into the mud in the hole; perform initial zero point calibration and self-weight calibration, record the initial tension value of each float in the mud, and establish the measurement benchmark for each measuring point; During the grouting process, the float tension data is continuously collected, and the gradient of tension change with depth is calculated. When the tension gradient changes abruptly and the absolute value exceeds the set threshold, it is determined that the float has reached the layer interface. Based on the magnitude of the tension change, the three media layers of mud layer, laitance layer and concrete layer are distinguished, and the precise elevation of the top surface of the concrete is locked. The length of the lowered rope of the float is measured, and the data is fused and filtered using a Kalman filter algorithm. At the same time, steel wire rope temperature drift compensation is introduced to dynamically correct the elevation measurement value and output real-time elevation data with millimeter-level accuracy. The real-time elevation data of each measuring point is transmitted to the BIM digital twin platform. The quadratic surface fitting algorithm is used to reconstruct the shape of the full-section concrete surface, and the state parameters are updated synchronously to drive the BIM model to evolve in sync with the on-site construction. Based on the real-time calculation of the duct burial depth value from the BIM model, the midpoint of the target burial depth range is used as the control target. The PID controller simultaneously adjusts two variables: the duct lifting speed and the concrete pouring rate. When the burial depth is too large, the duct lifting is accelerated and the pouring is slowed down. When the burial depth is too small, the duct lifting is slowed down and the pouring is accelerated. The two variables work together to keep the duct burial depth stably controlled within the design range. Real-time monitoring of the consistency of elevation, abnormal fluctuations in tension, and rate of change in burial depth at each measuring point; setting multiple early warning thresholds; when a risk signal of pile breakage such as a sudden drop in burial depth or abnormal elevation difference is detected, an emergency control program is automatically triggered to suspend pipe lifting and increase the grouting volume, restoring the burial depth of the guide pipe to a safe range.

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