BIM-based bolt-sphere net rack collaborative construction management system and method

Abstract

The invention provides a BIM (Building Information Modeling)-based bolt-sphere net rack collaborative construction management system and method. The BIM-based bolt-sphere net rack collaborative construction management system comprises a BIM model building module used for building and updating a three-dimensional building information model of a bolt-sphere net rack and a real-time monitoring module, and the torque and the screwing depth of a bolt in the construction process are monitored in real time through a sensor, a control system and a data acquisition device. According to the BIM-based bolt-sphere net rack collaborative construction management system and method, the torque, the screwing depth, the axial force and other key parameters of the bolts can be monitored in real time in the construction process, and it is ensured that the screwing quality of each bolt meets the design requirement. Through the cooperation of the multi-dimensional sensor device and the real-time data processing system, the system can accurately detect the false screwing phenomenon and the bolt depth deviation, and carries out data analysis and feedback in time.

Description

Technical Field

[0001] The present invention relates to the technical field of bolt-ball grid collaborative construction management, and in particular to a bolt-ball grid collaborative construction management system and method based on BIM. Background Art

[0002] BIM model building and data integration module: This module integrates data from various stages of the project, including design, construction, and maintenance. Through 3D modeling technology, the system generates a detailed bolt ball grid structure model to ensure that the design is consistent with the actual construction. The model includes information such as bolt location, size, and material, and can be updated in real time and provide accurate construction data.

[0003] Although the BIM-based bolt-ball grid collaborative construction management system has significant advantages in improving construction efficiency and quality management, it still has some defects in practical applications, especially the influence of human factors in the bolt tightening process. During the construction process, some workers will have the phenomenon of "false tightening", that is, the bolts are not tightened to the specified torque requirements, resulting in loose bolt connections. This phenomenon may be caused by improper operation of workers, failure to properly calibrate tools, or lack of sufficient experience of workers. Due to tool accuracy problems or inconsistent operation of workers, the depth of screwed screws may be inconsistent. This difference will cause the bolts to deform to varying degrees when subjected to external forces, thereby changing the force distribution of the structure and affecting the stability and safety of the grid. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a BIM-based bolt-ball grid collaborative construction management system and method, which solves the problem of "false tightening", that is, the bolts are not tightened to the specified torque requirements, resulting in loose bolt connections; due to tool accuracy issues or inconsistent worker operations, the depth of the screws may be inconsistent.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a bolt ball grid collaborative construction management system and method based on BIM, including:

[0006] BIM model building module, used to build and update the three-dimensional building information model of the bolt ball grid;

[0007] Real-time monitoring module, which monitors the torque and screwing depth of bolts in real time during construction through sensors, control systems and data acquisition devices;

[0008] Intelligent adjustment and feedback module, which uses intelligent algorithms to analyze construction quality based on monitoring data;

[0009] The construction collaboration module is used to dynamically schedule construction resources, assign tasks, track the construction progress, and achieve information sharing and real-time collaboration among multiple participants through the cloud platform.

[0010] Preferably, the real-time monitoring module includes:

[0011] The multi-dimensional sensor device is used to measure the tightening torque, screwing-in depth, and axial force of the bolts, and transmit the data to the control system through wireless technology;

[0012] The data processing system is used to receive the sensor data, perform real-time data processing and analysis, generate a quality assessment report, and compare it with the standard requirements in the BIM model to determine whether there are any abnormalities during the construction process.

[0013] Preferably, the intelligent adjustment and feedback module includes:

[0014] The alarm and guidance system, when detecting the phenomenon of "false tightening" or depth deviation, gives an audible and visual prompt and automatically provides specific operation adjustment suggestions to the workers;

[0015] The operation guidance system uses intelligent algorithms to analyze the tightening mode and tool usage, and generates an adjustment plan in real time to help construction personnel correctly use the wrench tool, adjust the torque, and correct the operation method.

[0016] Preferably, the construction collaboration module includes:

[0017] The construction resource scheduling system intelligently allocates construction resources according to the construction progress, task priorities, and on-site conditions to ensure the optimal utilization of resources;

[0018] The progress tracking and visualization management system intuitively presents the construction progress and quality situation through real-time data transmission and BIM model display, and supports remote monitoring and project management.

[0019] Preferably, it is characterized in that: the intelligent adjustment and feedback module uses artificial intelligence analysis and machine learning algorithms to predict potential quality problems based on historical data and construction trends, issue early warnings, and take preventive measures.

[0020] The BIM-based collaborative construction management method for bolted spherical grid structures includes the following steps:

[0021] S1. Before construction, use BIM technology to construct and integrate the three-dimensional model of the bolted spherical grid structure. The three-dimensional model includes design data, construction requirements, material properties, and bolt tightening standards, and ensures the consistency between the construction plan and the design by real-time docking with the design software and construction equipment;

[0022] The bolt connection stress formula of the three-dimensional model formula:

[0023]

[0024] Among them, σ is the stress borne by the bolt, F is the external force, and A is the cross-sectional area of the connection area.

[0025] S2. During the construction process, use multi-dimensional sensors to monitor the torque, depth, and axial force of the bolts, collect data in real time, and compare it with the design standard values in the BIM model to determine whether there is a "false tightening" phenomenon or inconsistent bolt depths.

[0026] For the judgment of torque and depth consistency, define:

[0027] T = K·d

[0028] Among them, T is the torque of the bolt, K is the friction coefficient, and d is the screwing depth of the bolt. Compare with the real-time data through this formula to determine whether it meets the preset standard.

[0029] S3. When a "false tightening" phenomenon or depth deviation is detected, establish a dynamic adaptive tightening adjustment model.

[0030] S4. Optimize the construction resource scheduling and quality control through a multi-objective optimization model to ensure the best quality control at the lowest cost.

[0031] Multi-objective optimization resource scheduling model:

[0032] Z = λ1·f cost (R) + λ2·f quality (Q)

[0033] Among them, Z is the comprehensive optimization goal, λ1 and λ2 are weight factors, and f cost (R) is the cost function of resource scheduling, and f quality (Q) is the function of quality control.

[0034] S5. Use deep learning algorithms to predict and analyze construction data, monitor potential quality problems in real time and issue warnings, and establish a deep learning quality prediction and warning model. This model automatically identifies potential problems during the construction process by learning historical data and construction trends, and takes measures in advance to reduce rework and construction defects.

[0035] Based on this formula, the system can predict and handle the quality risks during construction in advance.

[0036] S6. Through the construction collaboration module, the system dynamically schedules construction resources and tracks the construction progress.

[0037] Progress tracking formula:

[0038]

[0039] Where P is the percentage of task progress, C is the amount of work completed, and T total is the total amount of work. This formula is used to monitor the construction progress in real time and provide a basis for adjusting resource allocation.

[0040] Preferably, the deep learning quality prediction and early warning model:

[0041] Q = β0 + β1X1 + β2X2 + … + β n X n

[0042] Where Q is the predicted probability of quality problems occurring, β0, β1, …, β n are the regression coefficients of the deep learning model, and X1, X2, …, X n are the construction variables affecting quality.

[0043] Preferably, the dynamic adaptive tightening adjustment model:

[0044] ΔT = γ(T actual -T target )·f(T env ,H env ,E tool )

[0045] Where ΔT is the adjusted torque, γ is the adjustment coefficient, T actual is the actual torque, T target is the target torque, and f(T env ,H env ,E tool ) is a comprehensive function of environmental factors affecting bolt tightening and tool aging. This formula realizes adaptive adjustment to adapt to environmental changes and tool accuracy deviations.

[0046] The present invention provides a BIM-based collaborative construction management system and method for bolted spherical grids. It has the following beneficial effects:

[0047] The BIM-based collaborative construction management system and method for bolted spherical grids can monitor key parameters such as bolt torque, screw-in depth, and axial force in real time during construction to ensure that the tightening quality of each bolt meets the design requirements. Through the cooperation of multi-dimensional sensor devices and real-time data processing systems, the system can accurately detect the "false tightening" phenomenon and bolt depth deviation, and conduct data analysis and feedback in a timely manner. In case of abnormalities, the intelligent adjustment and feedback module will automatically generate an adjustment plan based on artificial intelligence algorithms to guide construction workers to take correct operations and ensure the firmness of bolt connections. By introducing a dynamic adaptive tightening adjustment model, the tightening standard is corrected in real time according to different construction environments, further ensuring the accuracy and adaptability of the construction process. This automated and intelligent feedback mechanism not only reduces human operation errors but also improves construction quality and ensures the long-term stability of the grid structure.

[0048] This technical solution uses a multi-objective optimization model to intelligently schedule and optimize construction resources, enabling the maximum reduction of costs and improvement of construction efficiency while ensuring construction quality. This optimization model combines the cost function of resource scheduling and the objective function of quality control, and precisely balances the relationship between cost and quality through the adjustment of weight factors. In addition, the quality prediction and early warning model based on deep learning can analyze the data during the construction process in real time, predict potential quality problems, and issue early warnings. This innovative method effectively avoids rework and resource waste caused by quality problems, while enhancing the controllability of the construction process. Through systematic resource scheduling and progress management, the construction progress can be accurately tracked, and real-time feedback is provided through visualization tools to assist project managers in making decisions, thus significantly improving the overall construction efficiency. This collaborative management system provides comprehensive and real-time construction control for project managers, making the construction process more efficient, transparent, and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] As Figure 1As shown in the figure, the embodiment of the present invention provides a BIM-based collaborative construction management system and method for bolted spherical grids, including a BIM model construction module for establishing and updating a three-dimensional building information model of the bolted spherical grid. This model integrates design data, construction requirements, material properties, and bolt tightening standards, and supports real-time docking with design software and construction equipment.

[0052] A real-time monitoring module, through sensors, control systems, and data acquisition devices, monitors the torque and insertion depth of bolts during construction in real time, and compares the data with the design values in the BIM model to determine whether there is a "false tightening" phenomenon or inconsistent depth. The real-time monitoring module includes:

[0053] A multi-dimensional sensor device for measuring the tightening torque, insertion depth, and axial force of bolts, and transmitting the data to the control system through wireless technology;

[0054] A data processing system for receiving sensor data, performing real-time data processing and analysis, generating a quality assessment report, and comparing it with the standard requirements in the BIM model to determine whether there are any abnormalities during construction;

[0055] An intelligent adjustment and feedback module analyzes the construction quality using intelligent algorithms based on the monitoring data, automatically sends feedback to construction personnel and guides them to adjust operations or tools to avoid quality problems caused by improper operations. The intelligent adjustment and feedback module uses artificial intelligence analysis and machine learning algorithms to predict potential quality problems based on historical data and construction trends, issue early warnings, and take preventive measures. The intelligent adjustment and feedback module includes:

[0056] An alarm and guidance system that, when detecting a "false tightening" phenomenon or depth deviation, provides audible and visual prompts and automatically gives specific operation adjustment suggestions to workers;

[0057] An operation guidance system that uses intelligent algorithms to analyze the tightening pattern and tool usage, and generates an adjustment plan in real time to help construction personnel correctly use wrench tools, adjust the torque, and correct the operation method;

[0058] A construction collaboration module for dynamically scheduling construction resources, allocating tasks, tracking the construction progress, and realizing information sharing and real-time collaboration among multiple participants through a cloud platform. The construction collaboration module includes:

[0059] A construction resource scheduling system that intelligently allocates construction resources according to the construction progress, task priorities, and on-site conditions to ensure the optimal utilization of resources;

[0060] A progress tracking and visualization management system that intuitively presents the construction progress and quality situation through real-time data transmission and BIM model display, and supports remote monitoring and project management.

[0061] A collaborative construction management method for bolted spherical grid structures based on BIM, characterized by including the following steps:

[0062] S1. Before construction, use BIM technology to construct and integrate a three-dimensional model of the bolted spherical grid structure. The three-dimensional model includes design data, construction requirements, material properties, and bolt tightening standards, and ensures the consistency between the construction plan and the design by connecting the design software and construction equipment in real time;

[0063] The bolt connection stress formula in the three-dimensional model formula:

[0064]

[0065] Where σ is the stress borne by the bolt, F is the external force, and A is the cross-sectional area of the connection area. This formula is used in the BIM model to calculate the stress of each bolt to ensure that the tightening standard is reasonable.

[0066] S2. During construction, use multi-dimensional sensors to monitor the torque, depth, and axial force of the bolts, collect data in real time, and compare it with the design standard values in the BIM model to determine whether there is a "false tightening" phenomenon or inconsistent bolt depth;

[0067] For the judgment of torque and depth consistency, define:

[0068] T = K·d

[0069] Where T is the torque of the bolt, K is the friction coefficient, and d is the screwing depth of the bolt. Compare with the real-time data through this formula to determine whether it meets the preset standard;

[0070] S3. When a "false tightening" phenomenon or depth deviation is detected, establish a dynamic adaptive tightening adjustment model. The system automatically adjusts the tightening torque through an intelligent algorithm to help construction workers adjust the operation in real time to ensure the bolt connection quality. The dynamic adaptive tightening adjustment model:

[0071] ΔT = γ(T actual - T target )·f(T env , H env , E tool )

[0072] Where ΔT is the adjusted torque, γ is the adjustment coefficient, T actual is the actual torque, T target is the target torque, and f(T env , H env , E tool ) is a comprehensive function of environmental factors and tool aging that affect bolt tightening. This formula realizes adaptive adjustment to adapt to environmental changes and tool accuracy deviations.

[0073] S4. Optimize construction resource scheduling and quality control through a multi-objective optimization model to ensure achieving the best quality control at the lowest cost.

[0074] Multi-objective optimization resource scheduling model:

[0075] Z = λ1·f cost (R) + λ2·f quality (Q)

[0076] Where Z is the comprehensive optimization objective, λ1 and λ2 are weight factors, f cost (R) is the cost function of resource scheduling, f quality (Q) is the function of quality control. This model can reduce construction costs while ensuring quality and achieve the optimal allocation of construction resources.

[0077] S5. Use deep learning algorithms to predict and analyze construction data, monitor potential quality problems in real time and issue early warnings. Establish a deep learning quality prediction and early warning model. This model can automatically identify potential problems in the construction process by learning historical data and construction trends, and take measures in advance to reduce rework and construction defects.

[0078] Based on this formula, the system can predict and handle quality risks in construction in advance. The deep learning quality prediction and early warning model:

[0079] Q = β0 + β1X1 + β2X2 + … + β n X n

[0080] Where Q is the predicted probability of quality problems occurring, β0, β1, …, β n are the regression coefficients of the deep learning model, and X1, X2, …, X n are the construction variables affecting quality.

[0081] S6. Through the construction collaboration module, the system dynamically schedules construction resources and tracks the construction progress to ensure that construction tasks are completed on time and can be adjusted in real time.

[0082] Progress tracking formula:

[0083]

[0084] Where P is the percentage of task progress, C is the completed workload, and T total is the total workload. This formula is used to monitor the construction progress in real time and provide a basis for adjusting resource allocation.

[0085] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A BIM-based collaborative construction management system for bolted spherical grid structures, characterized in that Including: A BIM model construction module, which is used to establish and update the three-dimensional building information model of the bolted spherical grid. A real-time monitoring module, which uses sensors, control systems and data acquisition devices to monitor the torque and screwing depth of bolts during construction in real time. An intelligent adjustment and feedback module, which analyzes the construction quality using intelligent algorithms based on the monitoring data. A construction collaboration module, which is used to dynamically schedule construction resources, allocate tasks, and track the construction progress.

2. The BIM-based collaborative construction management system for bolt ball grid structures according to claim 1, characterized in that: The real-time monitoring module includes: A multi-dimensional sensor device; A data processing system.

3. The BIM-based collaborative construction management system for bolted spherical grid structures according to claim 1, wherein: The intelligent adjustment and feedback module includes: An alarm and guidance system; An operation guidance system.

4. The BIM-based collaborative construction management system for bolt ball grid structures according to claim 1, characterized in that: The construction collaboration module includes: A construction resource scheduling system; A progress tracking and visualization management system.

5. The BIM-based collaborative construction management system for bolted spherical grid structures according to claim 1, wherein: The intelligent adjustment and feedback module uses artificial intelligence analysis and machine learning algorithms to predict potential quality problems based on historical data and construction trends, issue early warnings and take preventive measures.

6. The collaborative construction management method for bolted spherical grid structures based on BIM, characterized in that, Including the following steps: S1. Before construction, use BIM technology to construct and integrate the three-dimensional model of the bolted spherical grid. The three-dimensional model includes design data, construction requirements, material properties, and bolt tightening standards. By connecting the design software and construction equipment in real time, ensure that the construction plan is consistent with the design. The bolt connection stress formula of the three-dimensional model formula: Where σ is the stress borne by the bolt, F is the external force, and A is the cross-sectional area of the connection area. S2. During construction, use multi-dimensional sensors to monitor the torque, depth and axial force of bolts, collect data in real time, and compare it with the design standard values in the BIM model to judge whether there is a "false tightening" phenomenon or inconsistent bolt depth. For the judgment of torque and depth consistency, define: T = K·d Where T is the torque of the bolt, K is the friction coefficient, and d is the screwing depth of the bolt. Compare with the real-time data through this formula to judge whether it meets the preset standard. S3. When a "false tightening" phenomenon or depth deviation is detected, establish a dynamic adaptive tightening adjustment model. S4. Optimize the construction resource scheduling and quality control through a multi-objective optimization model to ensure the best quality control at the lowest cost. Multi-objective optimization resource scheduling model: Z = λ1·f cost (R) + λ2·f quality (Q) Among them, Z is the comprehensive optimization goal, λ1 and λ2 are weight factors, and f cost (R) is the cost function of resource scheduling, and f quality (Q) is the function of quality control; S5. Use deep learning algorithms to predict and analyze construction data, monitor potential quality problems in real time and issue early warnings, and establish a deep learning quality prediction and early warning model. S6. Through the construction collaboration module, the system dynamically schedules construction resources and tracks the construction progress. Progress tracking formula: Among them, P is the task progress percentage, C is the amount of work completed, and T total is the total amount of work.

7. The collaborative construction management method for bolted spherical grid structures based on BIM according to claim 6, characterized in that: The deep learning quality prediction and early warning model: Q = β0 + β1X1 + β2X2 + … + β n X n Among them, Q is the predicted probability of the occurrence of quality problems, β0, β1, …, β n are the regression coefficients of the deep learning model, and X1, X2, …, X n are the construction variables affecting quality.

8. The collaborative construction management method of bolted spherical grid based on BIM according to claim 6, characterized in that: The dynamic adaptive tightening adjustment model: ΔT = γ(T actual - T target )·f(T env , H env , E tool ) Among them, ΔT is the adjusted torque, γ is the adjustment coefficient, and T actual is the actual torque, and T target is the target torque. The function f(T env , H env , E tool ) is a comprehensive function of environmental factors and tool aging that affect the bolt tightening.

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