A digital construction control system and method for bored piles

Abstract

This invention discloses a digital construction control method for bored piles, comprising the following steps: S1, collecting historical data and establishing a bored pile construction information database; S2, establishing a geological model and fitting and generating a three-dimensional stratum in the pile hole area and the soil layer distribution corresponding to each pile hole; S3, based on the bored pile construction information database, identifying the average construction efficiency and cost data of similar strata in each pile hole area under various drilling techniques, and calculating the construction period and cost of different drilling techniques for a single pile, providing construction management personnel with options for drilling techniques and equipment specifications; S4, monitoring, early warning, and decision support during construction; S5, post-construction evaluation and analysis. This application will rely on core construction equipment and devices to automatically monitor key construction parameters of bored piles, and through the system and platform, provide online monitoring and early warning of key parameters, visualize the production dynamics of bored piles, and provide operators with a user-friendly interface to assist in selecting construction parameters.

Description

Technical Field

[0001] This invention relates to the field of digital construction control for bored piles. More specifically, this invention relates to a digital construction control system and method for bored piles. Background Technology

[0002] In recent years, with the acceleration of urbanization in my country, hundreds of millions of bored piles need to be completed annually. The construction process for bored piles involves on-site drilling and pouring, and it is a concealed underground project. Therefore, its construction efficiency, cost, and quality are closely related to the geological conditions of the project area. If the construction parameters do not match the geological conditions, it will lead to problems such as low drilling efficiency, poor verticality, and poor pile integrity, seriously affecting the safety of the superstructure, and making the treatment of pile foundation defects difficult and costly.

[0003] Currently, the control of efficiency, cost, and quality in the construction process of bored piles relies heavily on the construction team's experience in similar geological formations to select appropriate drilling techniques, equipment, drilling parameters, and mud mix ratios. However, this kind of construction experience and summaries cannot be accumulated and iterated in the form of data. Furthermore, bored piles involve numerous regions and geological formations, making it difficult to establish a systematic and generalizable set of experiences from a small number of projects.

[0004] In response to the current trend of digital transformation, adjacent industries such as petroleum, coal, and mining have begun research and application of digital and intelligent technologies. Therefore, combining digital technology with the construction process of bored piles is imperative. It is crucial to establish a closed-loop management system encompassing "pre-prediction, in-process control, and post-evaluation," focusing on controlling key parameters such as process efficiency, cost, and quality. This will improve the intelligent construction and refined management of bridge bored pile projects and ensure the quality of bored piles. Summary of the Invention

[0005] To achieve these objectives and other advantages according to the present invention, a preferred embodiment of the present invention provides a digital construction control method for bored piles, comprising the following steps:

[0006] S1, Historical Data Collection

[0007] A database of drilling and grouting pile construction information was established by compiling statistics on the strata, drilling technology, equipment specifications, drilling efficiency, drilling cost, and corresponding drilling pressure, torque, and rotation speed of historical engineering projects.

[0008] S2. Establish geological modeling

[0009] Based on the geological drilling data and soil physical and mechanical parameters of the new project, a three-dimensional stratum of the pile hole area and the soil layer distribution corresponding to each pile hole are generated by fitting.

[0010] S3, Recommended Pre-construction Plan

[0011] Based on the three-dimensional strata in the pile hole area and the soil layer distribution corresponding to each pile hole, and according to the borehole pile construction information database, the average construction efficiency and cost data of each hole-forming process in similar strata in the pile hole area are found, and the construction period and cost of different hole-forming processes for a single pile are calculated, so as to provide construction management personnel with the option to select hole-forming processes and equipment specifications.

[0012] S4. Monitoring, early warning, and decision support during construction.

[0013] Automatically identify the construction status of each pile hole, monitor construction parameters and provide early warnings, and display them visually; based on the drilling parameters of the constructed pile holes, establish calculation models for drilling rate, hole verticality, and hole cost, and obtain the control range of drilling rig construction parameters that meet the construction requirements based on the improved NSGA-III multi-objective optimization algorithm.

[0014] S5. Post-event evaluation analysis

[0015] Collect construction progress, quality, and cost parameters of bored piles from different project departments for comprehensive evaluation, and establish a database of construction parameters for bored pile companies and a database of quotas for bored cast-in-place pile companies to provide data support for subsequent project schedule and cost calculation.

[0016] According to a preferred embodiment of the present invention, step S4, based on the improved NSGA-III multi-objective optimization algorithm, includes the following steps;

[0017] A41. Drilling Data Collection

[0018] Obtain data on drilling pressure, rotation speed, torque, drill bit diameter, mud discharge, formation drillability index, and drilling cost during the construction of drilled pile foundations in the project.

[0019] A42, Data Segmentation

[0020] Based on data characteristics, drilling rig shutdown data, fault repair data, and drill rod installation and removal data are segmented to obtain pure drilling data of the drilling rig.

[0021] A43. Abnormal Data Removal

[0022] The 3σ rule is used to identify outliers in pure drilling data and replace the mean.

[0023] A44, Parameter Normalization

[0024] Normalization methods are used to normalize various types of raw data so that the output range of various types of data is between [0,1].

[0025] A45. Model Building

[0026] The SVM algorithm was used to establish regression prediction functions for drilling pressure, rotation speed, torque, mud discharge, formation drillability index, hole formation efficiency, and hole verticality, and a drilling cost calculation model was constructed.

[0027] A46, Parameter Optimization

[0028] Using drilling speed, drilling cost, and hole verticality as fitness functions of the improved NSGA-Ⅲ algorithm, an SVM-NSGA-Ⅲ multi-objective optimization model is constructed. Based on the Pareto optimal solution set obtained from the improved NSGA-Ⅲ algorithm, the control range of drilling construction parameters that satisfy drilling rate, hole verticality, and hole cost is determined.

[0029] According to a preferred embodiment of the present invention, in A45, the regression prediction function is f1 =

[0030] -[SVM-regression(Xi)],i=1,2,…,6;

[0031] f2=[SVM-regression(Xi)],i=1,2,…,6

[0032] Where f1 represents drilling efficiency; f2 represents hole verticality; X1, X2, X3, X4, X5, and X6 represent drilling pressure, rotation speed, torque, drill bit diameter, mud discharge, and formation drillability index, respectively; and SVM-regression(Xi) is the SVM regression function.

[0033] According to a preferred embodiment of the present invention, in A45, the drilling cost calculation model is as follows:

[0034]

[0035] Where Cb represents the drill bit cost, C R This indicates the cost of the drilling rig, in tons. T The t represents the tripping time, the t represents the pure drilling time, and the Ht represents the drill bit advance.

[0036] According to a preferred embodiment of the present invention, in step S4, automatically identifying the pile hole process status includes the following operations:

[0037] B41. Hole Formation Stage Process Identification: Obtain the center coordinates of the drilling rig through the Beidou positioning system of the drilling rig, and then calculate the distance with the center coordinates of the pile hole. The pile hole with the closest distance is the pile hole being drilled, and the pile number is associated with the drilling rig; after confirming the completion of the hole, the drilling rig is automatically unbound from the pile number.

[0038] B42. Reinforcing Cage Installation Stage Process Identification: After the pile hole is automatically confirmed and the drilling rig has completed drilling, the reinforcing cage lowering stage begins. The crawler crane's Beidou positioning system obtains the coordinates of the crawler crane lowering the reinforcing cage and the coordinates of the hole where the reinforcing cage is to be lowered, and calculates the distance. The pile hole with the closest distance is the pile hole where the reinforcing cage is being lowered, and the pile number is associated with the crawler crane. During the lowering process, the RFID tag on the reinforcing cage is automatically read by the vehicle-mounted RFID reader to record the lowering time and number of sections. After confirming that the lowering of the reinforcing cage is completed, the crawler crane is automatically unbound from the pile number.

[0039] The start time for lowering the steel cage is: the time when the RFID reader reads the tag of the first steel cage after the crawler crane is associated with the pile number.

[0040] The time when the steel cage is lowered is: the time when the RFID reader reads the tag of the last steel cage after the crawler crane is associated with the pile number;

[0041] B43. Concrete pouring stage process identification: After the steel cage is lowered, the concrete pouring stage begins. The Beidou positioning system of the concrete mixer truck obtains the coordinates of the concrete mixer truck and the coordinates of the pile hole to be filled with the steel cage. The nearest pile hole is the pile hole that is being filled with concrete. The pile number is associated with the concrete mixer truck. After the concrete pouring is completed, the mixer truck is automatically unbound from the pile number.

[0042] Among them, the concrete pouring start time is the moment when the first concrete mixer truck is associated with the chainage.

[0043] Concrete pouring completion time: the latest time among all the times when the concrete mixer trucks are associated with the chainage.

[0044] On the other hand, the present invention also provides a digital construction control system for bored piles, comprising:

[0045] Information entry module: used to enter the geological strata, drilling process, equipment specifications, drilling efficiency, drilling cost and corresponding drilling pressure, torque and rotation speed of historical engineering projects, and to establish a database of information on bored pile construction.

[0046] Geological model building module: Based on the geological drilling data and soil physical and mechanical parameters of historical engineering projects, the module fits and generates a three-dimensional stratum of the pile hole area and the soil layer distribution corresponding to each pile hole.

[0047] Process identification module: Automatically identifies the process status of each pile hole;

[0048] Parameter early warning module: Real-time monitoring of collected construction parameters and comparison with threshold values. If the collected construction parameters are not within the threshold value range, the parameter early warning module will issue an alarm to remind relevant technical personnel and display the specific pile number where the abnormality occurred, the reason for the alarm, and the specific construction time.

[0049] According to a preferred embodiment of the present invention, it further includes

[0050] Visualization module: Visualizes the overall project status and completion status, as well as the construction progress of the current pile location. It displays corresponding 3D digital twin models for each of the drilling, rebar cage lowering, and concrete pouring conditions, thus making the hidden works visible.

[0051] Log push module: After each pile construction process is completed, information such as the construction duration and energy consumption of the process is automatically pushed to the construction management personnel; the construction log is automatically pushed to the construction management personnel every day, showing the amount of work completed for the pile location that day, including hole drilling, rebar cage placement, and concrete pouring.

[0052] According to a preferred embodiment of the present invention, it further includes

[0053] Decision Support Module: Based on the actual drilling parameters collected by the project, and using the NSGA-III multi-objective optimization algorithm, dynamic drilling parameter optimization decisions are made based on the constraints of drilling efficiency, drilling verticality, and drilling energy consumption.

[0054] Enterprise Management Module: Used by enterprises to collect project efficiency, cost, and quality data, conduct comprehensive assessments of bored pile construction in different project departments, and establish a database of construction parameters for bored pile enterprises and a database of quotas for bored cast-in-place pile enterprises, providing data support for subsequent project schedule and cost calculations.

[0055] The present invention has at least the following beneficial effects:

[0056] (1) This application proposes a digital construction control method for bored cast-in-place piles. During the project decision-making stage, it relies on enterprise-level big data to guide the project to select matching construction technology, drilling equipment and construction parameters according to the geological conditions of the area.

[0057] (2) This application proposes a digital construction control device for bored cast-in-place piles, which collects various construction data during the construction process, establishes a comprehensive and thorough perception system, realizes online monitoring and timely early warning of key parameters of key processes, and saves the workload of manual measurement and statistics.

[0058] (3) This application proposes a digital construction control system for bored cast-in-place piles. Through the OCR-based quick import function of basic project information and intelligent acquisition device, the system parameters are automatically filled in and collected, greatly reducing the workload of manual statistics and data entry. It is easy to operate and has strong project promotion potential.

[0059] (4) This application proposes a digital construction control system for bored cast-in-place piles, which realizes the automatic identification of construction procedures and status, automatically generates various construction reports, and at the same time assists project managers to understand the production dynamics of bored cast-in-place piles in a timely, accurate and comprehensive manner through a visual data dashboard, and make timely construction management decisions.

[0060] (5) This application proposes a digital construction control system for bored cast-in-place piles, establishes an enterprise-level database of bored cast-in-place pile production and cost, and reflects the project’s management level of bored piles through multi-dimensional data statistics and analysis, so as to realize the accumulation and reuse of project efficiency, cost and quality data.

[0061] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0062] The present invention will be further described in detail below with reference to embodiments, so that those skilled in the art can implement it based on the description.

[0063] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0064] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0065] A preferred embodiment of the present invention provides a digital construction control method for bored piles, comprising the following steps:

[0066] S1, Historical Data Collection

[0067] A database of drilling and grouting pile construction information was established by compiling statistics on the strata, drilling technology, equipment specifications, drilling efficiency, drilling cost, and corresponding drilling pressure, torque, and rotation speed of historical engineering projects.

[0068] An example of a database of information on bored pile construction is as follows:

[0069] Table 1 Database of Construction Information for Bored Piles

[0070]

[0071] S2. Establish geological modeling

[0072] Based on the geological drilling data and soil physical and mechanical parameters of the new project, a three-dimensional stratum of the pile hole area and the soil layer distribution corresponding to each pile hole are generated by fitting.

[0073] S3, Recommended Pre-construction Plan

[0074] Based on the three-dimensional strata in the pile hole area and the soil layer distribution corresponding to each pile hole, and according to the borehole pile construction information database, the average construction efficiency and cost data of each hole-forming process in similar strata in the pile hole area are found, and the construction period and cost of different hole-forming processes for a single pile are calculated, so as to provide construction management personnel with the option to select hole-forming processes and equipment specifications.

[0075] Table 2 Construction period and cost of different drilling techniques for single piles

[0076]

[0077] S4. Monitoring, early warning, and decision support during construction.

[0078] Automatically identify the construction status of each pile hole, monitor construction parameters and provide early warnings, and display them visually; based on the drilling parameters of the constructed pile holes, establish calculation models for drilling rate, hole verticality, and hole cost, and obtain the control range of drilling rig construction parameters that meet the construction requirements based on the improved NSGA-III multi-objective optimization algorithm.

[0079] The improved NSGA-III multi-objective optimization algorithm includes the following steps:

[0080] A41. Drilling Data Collection

[0081] Obtain data on drilling pressure, rotation speed, torque, drill bit diameter, mud discharge, formation drillability index, and drilling cost during the construction of drilled pile foundations in the project.

[0082] A42, Data Segmentation

[0083] Based on data characteristics, drilling rig shutdown data, fault repair data, and drill rod installation and removal data are segmented to obtain pure drilling data of the drilling rig.

[0084] A43. Abnormal Data Removal

[0085] The 3σ rule is used to identify outliers in pure drilling data and replace the mean.

[0086] A44, Parameter Normalization

[0087] Normalization methods are used to normalize various types of raw data so that the output range of various types of data is between [0,1].

[0088] A45. Model Building

[0089] The SVM algorithm was used to establish regression prediction functions for drilling pressure, rotation speed, torque, mud discharge, formation drillability index, hole formation efficiency, and hole verticality, and a drilling cost calculation model was constructed.

[0090] In the A45, the drilling cost calculation model is as follows:

[0091] f3 = C b +C R (t T +t)

[0092] H t

[0093] Among them, C b C represents the cost of the drill bit. R This indicates the cost of the drilling rig, in tons. T H represents the tripping time, t represents the pure drilling time of the drill bit, and H represents the tripping time. t This indicates the depth of the drill bit.

[0094] A46, Parameter Optimization

[0095] Using drilling speed, drilling cost, and hole verticality as fitness functions of the improved NSGA-Ⅲ algorithm, an SVM-NSGA-Ⅲ multi-objective optimization model is constructed. Based on the Pareto optimal solution set obtained from the improved NSGA-Ⅲ algorithm, the control range of drilling construction parameters that satisfy drilling rate, hole verticality, and hole cost is determined.

[0096] According to a preferred embodiment of the present invention, in A45, the regression prediction function is f1 =

[0097] -[SVM-regression(Xi)],i=1,2,…,6;

[0098] f2=[SVM-regression(Xi)],i=1,2,…,6

[0099] Where f1 represents drilling efficiency; f2 represents hole verticality; X1, X2, X3, X4, X5, and X6 represent drilling pressure, rotation speed, torque, drill bit diameter, mud discharge, and formation drillability index, respectively; and SVM-regression(Xi) is the SVM regression function.

[0100] Specifically, in step S4, the automatic identification of the pile hole process status includes the following operations:

[0101] B41. Hole Formation Stage Process Identification: Obtain the center coordinates of the drilling rig through the Beidou positioning system of the drilling rig, and then calculate the distance with the center coordinates of the pile hole. The pile hole with the closest distance is the pile hole being drilled, and the pile number is associated with the drilling rig; after confirming the completion of the hole, the drilling rig is automatically unbound from the pile number.

[0102] B42. Reinforcing Cage Installation Stage Process Identification: After the pile hole is automatically confirmed and the drilling rig has completed drilling, the reinforcing cage lowering stage begins. The crawler crane's Beidou positioning system obtains the coordinates of the crawler crane lowering the reinforcing cage and the coordinates of the hole where the reinforcing cage is to be lowered, and calculates the distance. The pile hole with the closest distance is the pile hole where the reinforcing cage is being lowered, and the pile number is associated with the crawler crane. During the lowering process, the RFID tag on the reinforcing cage is automatically read by the vehicle-mounted RFID reader to record the lowering time and number of sections. After confirming that the lowering of the reinforcing cage is completed, the crawler crane is automatically unbound from the pile number.

[0103] The start time for lowering the steel cage is: the time when the RFID reader reads the tag of the first steel cage after the crawler crane is associated with the pile number.

[0104] The time when the steel cage is lowered is: the time when the RFID reader reads the tag of the last steel cage after the crawler crane is associated with the pile number;

[0105] B43. Concrete pouring stage process identification: After the steel cage is lowered, the concrete pouring stage begins. The Beidou positioning system of the concrete mixer truck obtains the coordinates of the concrete mixer truck and the coordinates of the pile hole to be filled with the steel cage. The nearest pile hole is the pile hole that is being filled with concrete. The pile number is associated with the concrete mixer truck. After the concrete pouring is completed, the mixer truck is automatically unbound from the pile number.

[0106] Among them, the concrete pouring start time is the moment when the first concrete mixer truck is associated with the chainage.

[0107] Concrete pouring completion time: the latest time among all the times when the concrete mixer trucks are associated with the chainage.

[0108] S5. Post-event evaluation analysis

[0109] Collect construction progress, quality, and cost parameters of bored piles from different project departments for comprehensive evaluation, and establish a database of construction parameters for bored pile companies and a database of quotas for bored cast-in-place pile companies to provide data support for subsequent project schedule and cost calculation.

[0110] The comprehensive evaluation includes the calculation methods for five indicators: Class I pile index, hole verticality index, concrete homogenization index, hole formation efficiency index, and concrete loss index, to achieve comprehensive benchmarking of pile foundation products across different projects.

[0111] Category I pile index = (Number of Category I piles / Total number of piles in the project) * 100%

[0112]

[0113] On the other hand, the present invention also provides a digital construction control system for bored piles, comprising:

[0114] Information entry module: used to enter the geological strata, drilling process, equipment specifications, drilling efficiency, drilling cost and corresponding drilling pressure, torque and rotation speed of historical engineering projects, and to establish a database of information on bored pile construction.

[0115] Geological model building module: Based on the geological drilling data and soil physical and mechanical parameters of historical engineering projects, the module fits and generates a three-dimensional stratum of the pile hole area and the soil layer distribution corresponding to each pile hole.

[0116] Process identification module: Automatically identifies the process status of each pile hole;

[0117] Parameter early warning module: Real-time monitoring of collected construction parameters and comparison with threshold values. If the collected construction parameters are not within the threshold value range, the parameter early warning module will issue an alarm to remind relevant technical personnel and display the specific pile number where the abnormality occurred, the reason for the alarm, and the specific construction time.

[0118] The early warning parameters of the parameter early warning module include: drilling pressure, torque, drilling rate, hole verticality, mud properties, waiting time for rebar cage lowering, and guide pipe burial depth. The early warning thresholds for these parameters can be manually set or automatically generated based on the company's average level.

[0119] According to a preferred embodiment of the present invention, it further includes

[0120] Visualization module: Visualizes the overall project status and completion status, as well as the construction progress of the current pile location. It displays corresponding 3D digital twin models for each of the drilling, rebar cage lowering, and concrete pouring conditions, thus making the hidden works visible.

[0121] Log push module: After each pile construction process is completed, information such as the construction duration and energy consumption of the process is automatically pushed to the construction management personnel; the construction log is automatically pushed to the construction management personnel every day, showing the amount of work completed for the pile location that day, including hole drilling, rebar cage placement, and concrete pouring.

[0122] According to a preferred embodiment of the present invention, it further includes

[0123] Decision Support Module: Based on the actual drilling parameters collected by the project, and using the NSGA-III multi-objective optimization algorithm, dynamic drilling parameter optimization decisions are made based on the constraints of drilling efficiency, drilling verticality, and drilling energy consumption.

[0124] Enterprise Management Module: Used by enterprises to collect project efficiency, cost, and quality data, conduct comprehensive assessments of bored pile construction in different project departments, and establish a database of construction parameters for bored pile enterprises and a database of quotas for bored cast-in-place pile enterprises, providing data support for subsequent project schedule and cost calculations.

[0125] Digital twin cockpit interface: This interface provides an intuitive and user-friendly interface to enable operators to effectively utilize the digital twin cockpit. It broadcasts warning information to operators and allows them to view and analyze prediction results, and adjust and optimize parameters.

[0126] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for digital construction control of a cast-in-place pile, characterized by, The method comprises the following steps: S1, historical data collection Statistically collect stratum, hole-forming process, equipment specification, drilling efficiency, drilling cost, and corresponding drilling pressure, torque, and rotating speed of historical engineering projects to establish a bored pile construction information database; S2, establishing a geological model According to the geological exploration drilling data and soil physical and mechanical parameters of the new engineering project, a three-dimensional stratum of the pile hole area and the soil layer distribution corresponding to each pile hole are fitted and generated; S3, pre-construction scheme recommendation Based on the three-dimensional stratum of the pile hole area and the soil layer distribution corresponding to each pile hole, the average construction efficiency and cost data of each hole-forming process in the similar stratum of the pile hole area are found out from the bored pile construction information database, and the construction period and cost of a single pile under different hole-forming processes are calculated to provide a selection of hole-forming process and equipment specification for construction management personnel; S4, monitoring and early warning during construction and auxiliary decision-making The pile hole procedure state of each pile is automatically identified, the construction parameters are monitored and early warning is performed, and a visual display is provided; a drilling rate, hole-forming verticality, and hole-forming cost calculation model is established according to the drilled pile construction parameters, and a control range of drilling rig construction parameters meeting the construction requirements is obtained according to an improved NSGA-III multi-objective optimization algorithm; S5, post-evaluation and analysis Construction progress, quality, and cost parameters of drilled piles of different project departments are collected for comprehensive examination and evaluation, and a drilled pile enterprise construction parameter database and a bored pile enterprise quota library are established to provide data support for subsequent project duration and cost estimation work; In S4, according to the improved NSGA-III multi-objective optimization algorithm, The method comprises the following steps: A41, drilling data collection Drilling pressure, rotating speed, torque, drill bit diameter, mud displacement, stratum drillability index, and drilling cost during the construction process of the project are obtained; A42, data segmentation Drilling rig pure drilling data are obtained by segmenting drilling rig downtime data, fault maintenance data, and drill pipe installation and removal data according to data characteristics; A43, abnormal data elimination Abnormal values in the drilling rig pure drilling data are identified and replaced with the mean value by using the 3σ rule; A44, parameter normalization Normalization methods are used to normalize various types of original data, so that the output range of each type of data is between [0, 1]; A45, model establishment Regression prediction functions of drilling pressure, rotating speed, torque, mud displacement, and stratum drillability index and hole-forming efficiency and hole-forming verticality are established using the SVM algorithm, and a drilling cost calculation model is constructed; A46, parameter optimization Drilling speed, drilling cost, and hole-forming verticality are used as the fitness function of the improved NSGA-III algorithm to construct a SVM-NSGA-III multi-objective optimization model; the Pareto optimal solution set is obtained according to the improved NSGA-III algorithm to determine the control range of drilling construction parameters meeting the drilling speed, hole-forming verticality, and hole-forming cost.

2. The cast-in-place pile digital construction control method according to claim 1, characterized in that, In A45, the regression prediction function is f1=-[SVM-regression(Xi)],i=1,2,…,6; f2=[SVM-regression(Xi)],i=1,2,…,6 Wherein, f1 represents drilling efficiency; f2 represents hole verticality; X1, X2, X3, X4, X5, X6 respectively represent drilling pressure, rotation speed, torque, drill bit diameter, mud displacement, formation drillability index, and SVM-regression(Xi) is an SVM regression function.

3. The cast-in-place pile digital construction control method according to claim 1, characterized in that, In the A45, the drilling cost calculation model is: where C b represents the cost of the drill bit, C R represents the cost of the rig, t T represents the tripping time, t represents the net drilling time of the drill bit, H t represents the footage of the drill bit.

4. The cast-in-place pile digital construction control method according to claim 1, characterized in that, In the S4, the automatic identification of the pile hole process state comprises the following operations: B41, hole forming stage process identification: the coordinates of the drilling rig center are obtained through the Beidou positioning system of the drilling rig, and then distance calculation is performed with the pile hole center coordinates, the nearest pile hole is the pile hole being drilled, and the pile number is associated with the drilling rig; After confirming the final hole, the drilling rig and the pile number are automatically unbound; B42, steel reinforcement cage installation stage process identification: after the pile hole automatically confirms the drilling rig final hole, the steel reinforcement cage placement stage is entered, the coordinates of the crawler crane placing the steel reinforcement cage are obtained through the Beidou positioning system of the crawler crane, distance calculation is performed with the coordinates of the pile hole to be placed, the nearest pile hole is the pile hole being placed with the steel reinforcement cage, and the pile number is associated with the crawler crane; during the steel reinforcement cage placement process, the RFID tag on the steel reinforcement cage is automatically read through the vehicle-mounted RFID reader, the steel reinforcement cage placement time and the number of layers are recorded, and after confirming the completion of the steel reinforcement cage placement, the crawler crane and the pile number are automatically unbound; Wherein, the steel reinforcement cage placement start time is the time when the first steel reinforcement cage tag is read by the RFID reader after the crawler crane is associated with the pile number; The steel reinforcement cage placement completion time is the time when the last steel reinforcement cage tag is read by the RFID reader after the crawler crane is associated with the pile number; B43, concrete pouring stage process identification: after automatically confirming the completion of the steel reinforcement cage placement, the concrete pouring stage is entered, the coordinates of the concrete pouring tank truck are obtained through the Beidou positioning system of the concrete pouring tank truck, distance calculation is performed with the coordinates of the pile hole to be placed, the nearest pile hole is the pile hole being poured with concrete, and the pile number is associated with the concrete pouring tank truck; after confirming the completion of the concrete pouring, the tank truck and the pile number are automatically unbound; Wherein, the concrete pouring start time is the time when the first tank truck is associated with the pile number; The concrete pouring completion time is the latest time among the times when all tank trucks are associated with the pile number.

5. A digital construction control system for cast-in-place piles, characterized in that, It comprises: An information input module: used for inputting the stratum, hole forming process, equipment specifications, drilling efficiency, drilling cost, and corresponding drilling pressure, torque, and rotation speed of historical engineering projects, and establishing a bored pile construction information database; A geological model establishment module: according to the geological exploration drilling data and soil physical and mechanical parameters of historical engineering projects, a three-dimensional stratum of the pile hole area and the soil layer distribution corresponding to each pile hole are fitted and generated; A process identification module: automatically identifies the pile hole process state of each pile, monitors the construction parameters and gives early warning, and performs visual display; according to the drilling parameters of the constructed pile hole, a drilling rate, hole verticality, and hole cost calculation model is established, and the control range of the drilling rig construction parameters meeting the construction requirements is obtained according to the improved NSGA-III multi-objective optimization algorithm; Parameter early warning module: real-time monitoring of collected construction parameters, and comparing them with thresholds. If the collected construction parameters are not within the threshold range, the parameter early warning module will alarm and remind relevant technical personnel, and display the specific pile position number, early warning reason and specific construction time of the anomaly; According to the improved NSGA-III multi-objective optimization algorithm, the following steps are included. A41, drilling data collection Obtain the drilling pressure, rotation speed, torque, drill bit diameter, mud displacement, formation drillability index, and drilling cost during the construction process of the completed pile foundation of the project. A42, data segmentation Segment the drilling rig downtime data, fault maintenance data, and drill pipe installation and removal data according to data characteristics to obtain pure drilling data of the drilling rig. A43, abnormal data elimination Use the 3σ rule to identify outliers in the pure drilling data of the drilling rig and replace them with the mean value. A44, parameter normalization Normalize the original data of various types to make the output range of each type of data between [0, 1]. A45, model establishment Use the SVM algorithm to establish a regression prediction function of drilling pressure, rotation speed, torque, mud displacement, and formation drillability index with hole completion efficiency and hole completion verticality, and construct a drilling cost calculation model. A46, parameter optimization Use drilling speed, drilling cost, and hole completion verticality as the fitness function of the improved NSGA-III algorithm to construct a SVM-NSGA-III multi-objective optimization model. According to the improved NSGA-III algorithm, the Pareto optimal solution set is obtained to determine the control range of the drilling construction parameters that meet the drilling speed, hole completion verticality, and hole completion cost.

6. The cast-in-place pile digital construction control system according to claim 5, characterized in that, Further comprising Visualization module: visually display the overall and completion status of the project and the construction progress of the current construction pile position. Display the corresponding three-dimensional digital twin model for hole completion, reinforcement cage lowering, and concrete pouring conditions to visualize the concealed engineering. Log pushing module: automatically push the process construction duration, energy consumption, and other information to the construction management personnel after each pile process is completed. Automatically push the construction log to the construction management personnel every day to display the completed hole completion, reinforcement cage lowering, and concrete pouring quantities of the construction pile position on that day.

7. The cast-in-place pile digital construction control system according to claim 5, characterized in that, Further comprising Auxiliary decision-making module: based on the actual drilling parameters collected from the project, the NSGA-III multi-objective optimization algorithm, and the constraints of hole completion efficiency, hole completion verticality, and hole completion energy consumption, provide dynamic drilling parameter optimization decisions. Enterprise management module: used by enterprises to collect project efficiency, cost, and quality data, conduct comprehensive assessment and evaluation of drilling pile construction of different project departments, and establish a drilling pile enterprise construction parameter database and a drilling pile enterprise quota library to provide data support for subsequent project duration and cost estimation work.

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