A construction method for assembled steel-concrete composite structure

Abstract

The present invention discloses a method for constructing a prefabricated steel-concrete composite structure, relates to the technical field of building construction, and solves the problems of complex construction process, high difficulty in quality control, difficulty in maintenance and repair, and low work efficiency existing in the existing construction method; improves the controllability and stability of construction through a modular design method based on BIM; simulates the installation sequence and construction operations of prefabricated components through a virtual construction simulation model to reduce the complexity of construction; improves the control and management of construction quality through an intelligent monitoring system; realizes real-time monitoring, prediction and optimization of maintenance plan of structural status through an intelligent structural health monitoring system; improves work efficiency and quality control level through an intelligent digital manufacturing system; optimizes the construction process through a construction optimization algorithm based on machine learning; the present invention greatly improves the work efficiency of the method for constructing a prefabricated steel-concrete composite structure.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and more specifically to a method for constructing an assembled steel-concrete composite structure. Background Art

[0002] The construction method of prefabricated steel-concrete composite structure originates from the continuous pursuit of construction efficiency and quality. With the rise of industrialization and modern construction concepts, people have put forward an urgent demand for faster and more efficient construction methods. In the context of the times, engineering construction is increasingly showing the characteristics of large-scale, high efficiency, high quality, and green environmental protection. Traditional construction methods can no longer meet people's new demands for construction. Against this background, the construction method of prefabricated steel-concrete composite structure came into being.

[0003] Under the existing technical background, various new materials and advanced technologies are constantly emerging in the field of engineering construction, making construction more refined, standardized and automated. Steel structures and concrete, as two common building materials, have their own advantages. Through the combination of prefabricated construction methods, the advantages of the two can be complemented to improve the overall performance and use effect of the structure. At the same time, the popularization of modern architectural design software and the application of digital technology provide more scientific and accurate support for the design and construction of prefabricated steel-concrete composite structures.

[0004] However, although the prefabricated steel-concrete composite structure construction method brings many advantages, it also faces some challenges. First, the construction process is relatively complex and requires a high level of professional technology and strict operating procedures. Once a problem occurs, it may have a serious impact on the entire project. Secondly, due to the complexity of the structure, quality control is difficult and requires strict monitoring and management. In addition, once damage occurs or repairs are required, maintenance and repair work will become more difficult and time-consuming due to the integrated nature of the structure. Finally, although the concept of prefabricated construction can improve construction efficiency, in actual operation, it is necessary to overcome problems such as coordination and cooperation between processes to ensure the improvement of overall work efficiency.

[0005] Therefore, the present invention discloses a construction method of an assembled steel-concrete composite structure. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention discloses a method for constructing a prefabricated steel-concrete composite structure, which solves the problems of complex construction process, high difficulty in quality control, difficult maintenance and repair, and low work efficiency existing in the existing method for constructing a prefabricated steel-concrete composite structure.

[0007] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0008] A method for constructing an assembled steel-concrete composite structure, wherein the method comprises:

[0009] As a further technical solution of the present invention, the following steps are included:

[0010] Step 1: Design and planning;

[0011] Build a 3D digital model of the assembled steel-concrete composite structure through sensors and 3D modeling methods to achieve information sharing and collaborative design throughout the entire process;

[0012] Step 2: Modular design;

[0013] The structure is decomposed into standardized modules through BIM-based modular design method to reduce construction difficulty and quality fluctuation;

[0014] Step 3: Process optimization;

[0015] Analyze and predict at least the construction site topography, environment, and construction team staffing factors through machine learning-based construction optimization algorithms to optimize the construction process;

[0016] Step 4: Automated manufacturing;

[0017] Realize automated production of prefabricated components through intelligent digital manufacturing systems, improve work efficiency and quality control level;

[0018] Step 5: Real-time monitoring and data collection;

[0019] Real-time monitoring of the construction progress and quality of prefabricated steel-concrete composite structures through intelligent monitoring systems;

[0020] Step 6: Identification management of prefabricated components;

[0021] Improving construction site material management and assembly efficiency through a component inventory management system; the component inventory management system identifies assembled components through laser coding;

[0022] Step 7: Conduct a plan rehearsal simulation;

[0023] The installation sequence and construction work of prefabricated components are simulated by a virtual construction simulation model. The virtual construction simulation model conducts risk assessment on the construction links and construction sites through a construction risk assessment algorithm based on big data, and gives corresponding warnings and suggestions;

[0024] Step 8: Collaborative management of construction sites;

[0025] Improve construction coordination and efficiency through BIM collaborative management platform;

[0026] Step 9: Digital maintenance and repair;

[0027] Real-time monitoring, prediction and optimization of maintenance plans of structural status can be achieved through intelligent structural health monitoring system.

[0028] As a further technical solution of the present invention, the working principle and steps of the BIM-based modular design method are as follows:

[0029] S1, structural decomposition;

[0030] Create component element associations through the object-relationship model and decompose the entire structure according to functions and standardization requirements;

[0031] S2, module design;

[0032] The parametric modeling function of BIM software defines at least the geometry, size, connection method and material requirement parameters of each module;

[0033] S3, modular manufacturing;

[0034] Customized manufacturing of modules through CNC cutting and CNC punching;

[0035] S4, modular identification;

[0036] Laser coding adds a unique identification code to each module for quick identification and installation at the construction site;

[0037] S5. Construction plan optimization;

[0038] The three-dimensional design model NW is used to simulate and optimize the construction process to determine the best construction sequence and method; the three-dimensional design model NW simulates the assembly process of the modular structure through a 4D simulation method;

[0039] S6, precise installation;

[0040] The module positions are measured by a total station to ensure the connection accuracy between modules and the accuracy of the overall structure.

[0041] As a further technical solution of the present invention, the construction process optimization algorithm based on machine learning predicts the influence of the construction site terrain on the construction time through a construction time prediction function; the construction time prediction function collects and analyzes a large amount of historical construction data through a machine learning algorithm, and predicts the influence of different site terrains on the construction time through a regression analysis method; the formula expression of the construction time prediction function is:

[0042]

[0043] In formula (1), T represents the predicted construction time; β is the site slope, which is used to indicate the degree of ground inclination of the construction site; S is the distance between the site and the construction site, which indicates the distance between the construction site and the construction site; γ is the site roughness, which is used to indicate the unevenness of the ground of the construction site; K is the site altitude, which is used to indicate the relative height of the construction site; d represents the influence weight; the steel structure installation sequence optimization function is used to optimize the steel structure installation sequence; the formula expression of the steel structure installation sequence optimization function is:

[0044]

[0045] In formula (2), R is the optimized steel structure installation sequence; P is the connection strength between the mth component and the jth component, which is used to indicate the connection relationship between the steel structure components; O is the height of the mth component, which is used to indicate the vertical size of the steel structure component; C is the weight of the mth component, which is used to indicate the mass of the steel structure component; L is the length of the jth component, which is used to indicate the horizontal size of the steel structure component; the concrete pouring sequence optimization function is used to optimize the concrete pouring sequence; the formula expression of the concrete pouring sequence optimization function is:

[0046]

[0047] In formula (3), P represents the optimized concrete pouring sequence; h is the volume of the δth pouring area, which is used to indicate the size of the concrete pouring area; v is the concrete strength grade of the δth pouring area, which is used to indicate the quality standard of the concrete; ρ is the temperature of the δth pouring area, which is used to indicate the ambient temperature during concrete pouring; μ is the distance between the δth pouring area and the construction site, which is used to indicate the distance between the pouring area and the construction site.

[0048] As a further technical solution of the present invention, the intelligent digital manufacturing system includes a structural analysis module, a material processing module, an automatic welding module, a concrete manufacturing module, an intelligent detection module and a data analysis and feedback module; the structural analysis module simulates and evaluates the mechanical properties of the prefabricated structure through a finite element analysis method; the material processing module realizes the automatic cutting of the steel structure through a CNC steel structure cutting machine; the automatic welding module realizes the precise positioning and automatic welding of the prefabricated steel components through machine vision, sensors and automatic control methods; the concrete manufacturing module realizes the efficient manufacturing of concrete components through a precast concrete production line; the precast concrete production line realizes the automatic production and processing of concrete through an automatic mixer and an automatic pouring machine; the intelligent detection module performs online monitoring and detection of the size, shape and surface quality of the prefabricated components through non-contact measurement and image processing methods; the data analysis and feedback module collects and analyzes the data in the construction process in real time through the Internet of Things and big data analysis methods, and issues problem warnings.

[0049] As a further technical solution of the present invention, the intelligent monitoring system includes a real-time monitoring and data acquisition module, a BIM technology application module, a construction progress monitoring module, a quality control module and a safety management module; the real-time monitoring and data acquisition module realizes real-time monitoring and data acquisition of various parameters in the construction process through a sensor monitoring system and data acquisition and transmission equipment; the BIM technology application module realizes modeling, coordination and optimization of the construction process of the prefabricated steel-concrete composite structure through a building information model and a BIM data management system; the construction progress monitoring module monitors the construction progress of the prefabricated steel-concrete composite structure in real time through a camera and an image recognition and processing method; the quality control module realizes quality monitoring and evaluation of the prefabricated steel-concrete composite structure through a non-destructive testing method and a data analysis and judgment system; the non-destructive testing method performs non-destructive testing on the prefabricated steel-concrete composite structure through ultrasonic, magnetic powder and X-ray methods; the data analysis and judgment system analyzes and judges the non-destructive testing data through a neural network model; the safety management module identifies potential safety risks and hidden dangers through image processing, video analysis and machine learning methods.

[0050] As a further technical solution of the present invention, the component inventory management system includes a component information management module, an inventory management module, a material management module and a laser coding identification module; the component information management module realizes efficient storage and query of component information through a database management system; the database management system realizes rapid retrieval, modification and update of component information through transaction management and database indexing; the inventory management module is connected to the component information management system through a radio frequency identification RFID tag to monitor and manage component inventory in real time; the material management module interconnects the material warehouse and the material management system of the construction site through a wireless communication network to realize real-time monitoring and replenishment of materials; the laser coding identification module engraves the coding identification on the assembled component through a laser engraving method, and realizes unique identification and tracking of each component through the combination of the database management system and the RFID tag.

[0051] As a further technical solution of the present invention, the construction risk assessment algorithm based on big data calculates the time required for virtual construction through a construction time budget function; the construction time budget function performs data analysis and modeling on the estimated time, importance and difficulty of different construction tasks through historical construction data and related statistical information; the formula expression of the construction time budget function is:

[0052]

[0053] In formula (4), T VS Represents the time required for virtual construction, which is used to evaluate the estimated time of the entire construction process; θ i is the importance weight of the i-th construction task, which is used to consider the priority and impact of the task; v i represents the difficulty coefficient of the i-th construction task, which is used to measure the complexity of the task; t i is the estimated time of the i-th construction task, which is used to calculate the time contribution of each task; n represents the total number of construction tasks; the construction risk index is calculated by the construction risk assessment index function; the formula expression of the construction risk assessment index function is:

[0054]

[0055] In formula (5), M represents the construction risk index, which is used to evaluate the risk level during the construction process; i is the probability risk coefficient of the i-th construction task, which is used to measure the probability of the task occurring; x i is the consequence risk coefficient of the i-th construction task, which is used to measure the impact after the task occurs; z iis the frequency risk coefficient of the i-th construction task, which is used to consider the number of repeated occurrences of the task; the overall construction resource utilization is obtained through the construction resource utilization evaluation function; the formula expression of the construction resource utilization evaluation function is:

[0056]

[0057] In formula (6), Y represents the construction resource utilization rate, which is used to evaluate the resource utilization efficiency during the construction process; c i It represents the amount of resources required for the i-th construction task, which is used to measure the degree of resource demand of the task; s i is the duration of the i-th construction task, which is used to measure the time length of the task; F is the total virtual construction time, which is used to calculate resource utilization.

[0058] As a further technical solution of the present invention, the BIM collaborative management platform includes a collaborative design module, a bill of quantities management module, a schedule management module, a quality and safety management module, an equipment and material management module and a construction site collaborative module; the collaborative design module realizes design collaboration and information sharing among participants through a three-dimensional building information model, and the three-dimensional building information model improves design quality and efficiency through collaborative design review and collision detection and analysis methods; the bill of quantities management module realizes automatic measurement, calculation and change of engineering quantities through an automated recognition method; the automated recognition method automatically recognizes BIM models through automatic measurement, engineering quantity calculation and change management methods The component information in the model is collected and the corresponding bill of quantities is generated; the schedule management module connects the construction schedule with the BIM model through the schedule optimization method to realize the automatic tracking and optimization of the project progress; the schedule optimization method improves the construction efficiency through resource scheduling and task allocation methods; the quality and safety management module realizes real-time monitoring of the construction site environment, materials and equipment through the Internet of Things and sensor equipment; the equipment and material management module realizes the tracking management of equipment and materials through the laser engraving method; the construction site collaboration module realizes real-time collaboration and information exchange between construction site personnel through mobile terminal collaboration, real-time communication and information sharing methods.

[0059] As a further technical solution of the present invention, the intelligent structure health monitoring system includes a sensor module, a data management module, a data analysis module and a visualization display module; the sensor module monitors the assembled steel-concrete composite structure in real time through strain sensors, acceleration sensors and temperature sensors; the strain sensor monitors the strain of the structure in real time through resistors; the acceleration sensor evaluates the stability and dynamic response of the structure through vibration sensors; the data management module includes a data storage unit and a data transmission unit; the data storage unit collects and stores the data obtained by the sensor through a cloud storage device; the data transmission unit transmits the collected data through a wireless communication method to a data center or cloud platform; the data analysis module includes a structure identification and monitoring unit, a structure health assessment unit and a maintenance plan optimization unit; based on the collected data, the structure identification and monitoring unit identifies and locates the damage of the prefabricated steel-concrete composite structure through a support vector machine model; the structure health assessment unit performs health assessment and structural reliability analysis on the prefabricated steel-concrete composite structure through a finite element analysis method; according to the structural health assessment result, the maintenance plan optimization unit optimizes the maintenance strategy of the prefabricated steel-concrete composite structure through a simulated annealing algorithm; the visualization display module displays the structure in a chart and describes the warning information through a chart library and a natural language processing method.

[0060] Positive beneficial effects:

[0061] The present invention decomposes the structure into standardized modules through a modular design method based on BIM, reduces the difficulty and volatility of construction, and improves the controllability and stability of construction; simulates the installation sequence and construction operations of prefabricated components through a virtual construction simulation model, predicts construction risks and gives corresponding warnings and suggestions, and reduces the complexity of construction; constructs a three-dimensional digital model of a prefabricated steel-concrete composite structure through sensors and three-dimensional modeling methods, realizes information sharing and collaborative design throughout the process, and an intelligent monitoring system monitors the construction progress and quality of the prefabricated steel-concrete composite structure in real time, and improves the control and management of construction quality; realizes real-time monitoring, prediction and optimization of maintenance plans of structural status through an intelligent structural health monitoring system, can detect structural problems in advance and take corresponding maintenance measures, and extend the service life of the structure; realizes the automated production of prefabricated components through an intelligent digital manufacturing system, and improves work efficiency and quality control level. At the same time, the construction process is optimized through a construction optimization algorithm based on machine learning to improve construction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0063] Figure 1 It is a schematic diagram of the process steps of the present invention;

[0064] Figure 2 It is a working principle diagram of the intelligent digital manufacturing system of the present invention;

[0065] Figure 3 It is a working principle diagram of the component inventory management system of the present invention;

[0066] Figure 4 A step diagram of the operation process of the construction optimization algorithm based on machine learning of the present invention;

[0067] Figure 5 It is a principle step diagram of the BIM-based modular design method of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] like Figure 1-Figure 5 As shown, a method for constructing an assembled steel-concrete composite structure comprises the following steps:

[0070] Step 1: Design and planning;

[0071] Build a 3D digital model of the assembled steel-concrete composite structure through sensors and 3D modeling methods to achieve information sharing and collaborative design throughout the entire process;

[0072] Step 2: Modular design;

[0073] The structure is decomposed into standardized modules through BIM-based modular design method to reduce construction difficulty and quality fluctuation;

[0074] Step 3: Process optimization;

[0075] Analyze and predict at least the construction site topography, environment, and construction team staffing factors through machine learning-based construction optimization algorithms to optimize the construction process;

[0076] Step 4: Automated manufacturing;

[0077] Realize automated production of prefabricated components through intelligent digital manufacturing systems, improve work efficiency and quality control level;

[0078] Step 5: Real-time monitoring and data collection;

[0079] Real-time monitoring of the construction progress and quality of prefabricated steel-concrete composite structures through intelligent monitoring systems;

[0080] Step 6: Identification management of prefabricated components;

[0081] Improving construction site material management and assembly efficiency through a component inventory management system; the component inventory management system identifies assembled components through laser coding;

[0082] Step 7: Conduct a plan rehearsal simulation;

[0083] The installation sequence and construction work of prefabricated components are simulated by a virtual construction simulation model. The virtual construction simulation model conducts risk assessment on the construction links and construction sites through a construction risk assessment algorithm based on big data, and gives corresponding warnings and suggestions;

[0084] Step 8: Collaborative management of construction sites;

[0085] Improve construction coordination and efficiency through BIM collaborative management platform;

[0086] Step 9: Digital maintenance and repair;

[0087] Real-time monitoring, prediction and optimization of maintenance plans of structural status can be achieved through intelligent structural health monitoring system.

[0088] In the above embodiment, the working principle and steps of the BIM-based modular design method are as follows:

[0089] S1, structural decomposition;

[0090] Create component element associations through the object-relationship model and decompose the entire structure according to functions and standardization requirements;

[0091] S2, module design;

[0092] The parametric modeling function of BIM software defines at least the geometry, size, connection method and material requirement parameters of each module;

[0093] S3, modular manufacturing;

[0094] Customized manufacturing of modules through CNC cutting and CNC punching;

[0095] S4, modular identification;

[0096] Laser coding adds a unique identification code to each module for quick identification and installation at the construction site;

[0097] S5. Construction plan optimization;

[0098] The three-dimensional design model NW is used to simulate and optimize the construction process to determine the best construction sequence and method; the three-dimensional design model NW simulates the assembly process of the modular structure through a 4D simulation method;

[0099] S6, precise installation;

[0100] The module positions are measured by a total station to ensure the connection accuracy between modules and the accuracy of the overall structure.

[0101] In a specific embodiment, the BIM-based modular design method uses three-dimensional modeling software to digitally model the prefabricated steel-concrete composite structure, including the main structure, connectors, support systems, etc.

[0102] According to the engineering requirements and design specifications, the entire structure is broken down into standardized modules, each with the same size and features. These modules can be part of a whole floor, part of a room, or part of a component.

[0103] During the modular design process, BIM software is used to conduct collaborative design and collision detection between modules to ensure that the connection and coordination between modules meet the requirements and to predict and solve problems that may be encountered during construction.

[0104] In a method for constructing a prefabricated steel-concrete composite structure, modular design decomposes the structure into standardized modules, making the production, transportation and installation process of each module simpler and more controllable, reducing the difficulty of construction. In addition, standardized module design can ensure the consistency and uniformity between modules, reduce errors and quality fluctuations in construction, and improve the stability and reliability of the structure. Secondly, modular design makes the production and assembly of prefabricated components more efficient, reduces the time and labor costs of on-site construction, and improves the efficiency of construction. At the same time, modular design can accurately calculate and plan the use of materials and resources, reduce waste, improve resource utilization, and reduce construction costs.

[0105] In the above embodiment, the construction process optimization algorithm based on machine learning predicts the influence of the construction site terrain on the construction time through the construction time prediction function; the construction time prediction function collects and analyzes a large amount of historical construction data through the machine learning algorithm, and predicts the influence of different site terrains on the construction time through the regression analysis method; the formula expression of the construction time prediction function is:

[0106]

[0107] In formula (1), T represents the predicted construction time; β is the site slope, which is used to indicate the degree of ground inclination of the construction site; S is the distance between the site and the construction site, which indicates the distance between the construction site and the construction site; γ is the site roughness, which is used to indicate the unevenness of the ground of the construction site; K is the site altitude, which is used to indicate the relative height of the construction site; d represents the influence weight; the steel structure installation sequence optimization function is used to optimize the steel structure installation sequence; the formula expression of the steel structure installation sequence optimization function is:

[0108]

[0109] In formula (2), R is the optimized steel structure installation sequence; P is the connection strength between the mth component and the jth component, which is used to indicate the connection relationship between the steel structure components; O is the height of the mth component, which is used to indicate the vertical size of the steel structure component; C is the weight of the mth component, which is used to indicate the mass of the steel structure component; L is the length of the jth component, which is used to indicate the horizontal size of the steel structure component; the concrete pouring sequence optimization function is used to optimize the concrete pouring sequence; the formula expression of the concrete pouring sequence optimization function is:

[0110]

[0111] In formula (3), P represents the optimized concrete pouring sequence; h is the volume of the δth pouring area, which is used to indicate the size of the concrete pouring area; v is the concrete strength grade of the δth pouring area, which is used to indicate the quality standard of the concrete; ρ is the temperature of the δth pouring area, which is used to indicate the ambient temperature during concrete pouring; μ is the distance between the δth pouring area and the construction site, which is used to indicate the distance between the pouring area and the construction site.

[0112] In a specific embodiment, the construction optimization algorithm based on machine learning can optimize the construction process by analyzing and predicting factors such as the construction site terrain, environment, and personnel configuration of the construction team. Its working principle is as follows:

[0113] P1. Data collection: Obtain relevant data such as construction site topography, environment, and personnel configuration through sensors or other data collection equipment, and store it in a database or data warehouse.

[0114] P2. Data preprocessing: Perform preprocessing operations such as cleaning, normalization, and feature extraction on the collected data so that it can be better applied to machine learning algorithms.

[0115] P3. Feature Engineering: Based on the specific problems and data characteristics, feature engineering is performed on the preprocessed data to extract effective features that can reflect the optimization of the construction process.

[0116] P4. Model training: Use machine learning algorithms (such as regression, classification, clustering, etc.) to train the preprocessed data and establish a construction process optimization model. During the model training process, cross-validation and other techniques can be used to evaluate the performance and generalization ability of the model.

[0117] P5. Model evaluation and tuning: Evaluate the trained model, including indicators such as accuracy, recall, F1 value, etc. Based on the evaluation results, tune the model to improve its predictive performance and adaptability.

[0118] P6. Prediction and optimization: Use trained and tuned models to predict and optimize new construction site terrain, environment, and staffing data. Through optimization algorithms, the best construction process can be obtained to improve efficiency and reduce costs.

[0119] In a method for constructing a prefabricated steel-concrete composite structure, a construction optimization algorithm based on machine learning improves construction efficiency by optimizing the construction process and staffing, reducing repetitive work, time waste, and resource waste. In addition, the construction optimization algorithm based on machine learning reduces construction costs and reduces waste of manpower and materials by optimizing the construction process and resource utilization. Secondly, the construction optimization algorithm based on machine learning optimizes the construction process, reduces accident risks, and improves construction safety by considering factors such as the topography and environment of the construction site. At the same time, by analyzing and predicting a large amount of data based on machine learning algorithms, problems and difficulties in the construction process can be more accurately predicted, and countermeasures can be made in advance. In the specific implementation, the data test comparison table of the construction optimization algorithm based on machine learning and the traditional algorithm is shown in Table 1:

[0120] Table 1 Data test comparison table

[0121]

[0122]

[0123] As can be seen from the table above, compared with traditional algorithms, the construction optimization algorithm based on machine learning has achieved obvious advantages in construction time, number of staffing, resource utilization, number of safety accidents and cost. This shows that the construction optimization algorithm based on machine learning can bring positive and beneficial effects, improve construction efficiency, reduce costs and improve safety.

[0124] In a specific embodiment of a method for constructing a prefabricated steel-concrete composite structure, the operating hardware environment of the construction optimization algorithm based on machine learning is a computer or server: used to carry the operating environment of the algorithm, including a processor, memory, and hard disk, etc. Data acquisition equipment: used to collect relevant data such as the topography, environment, and personnel configuration of the construction site.

[0125] In the specific implementation, the test data table of the construction optimization algorithm based on machine learning is shown in Table 2:

[0126] Table 2 Test data of construction optimization algorithm based on machine learning

[0127] Site topography (X1) Environmental factors (X2) Staffing (X3) Construction time (Y) 1 0.2 10 8 2 0.5 15 12 3 0.8 20 16 4 0.3 12 9 5 0.6 18 14

[0128] In Data Table 2, we collected three features, including site topography, environmental factors, and staffing, as inputs (X1, X2, X3), and used a machine learning algorithm to predict the construction time (Y). By training and tuning the algorithm, a reliable model can be obtained to predict the optimal construction time based on different input features.

[0129] In the above embodiment, the intelligent digital manufacturing system includes a structural analysis module, a material processing module, an automatic welding module, a concrete manufacturing module, an intelligent detection module and a data analysis and feedback module; the structural analysis module simulates and evaluates the mechanical properties of the prefabricated structure through a finite element analysis method; the material processing module realizes the automatic cutting of the steel structure through a CNC steel structure cutting machine; the automatic welding module realizes the precise positioning and automatic welding of the prefabricated steel components through machine vision, sensors and automatic control methods; the concrete manufacturing module realizes the efficient manufacturing of concrete components through a precast concrete production line; the precast concrete production line realizes the automatic production and processing of concrete through an automatic mixer and an automatic pouring machine; the intelligent detection module performs online monitoring and detection of the size, shape and surface quality of the prefabricated components through non-contact measurement and image processing methods; the data analysis and feedback module collects and analyzes the data in the construction process in real time through the Internet of Things and big data analysis methods, and issues problem warnings.

[0130] In a specific embodiment, the structural analysis module determines the size, shape and material requirements of the structure by analyzing the design drawings, and generates a corresponding construction plan. The material processing module cuts, punches and processes the steel according to the information provided by the structural analysis module to meet the requirements of the structure. The automatic welding module uses automated welding equipment to accurately weld the processed steel to ensure the strength and stability of the structure. According to the structural design requirements, the concrete manufacturing module prepares the concrete according to a predetermined proportion, and uses automated equipment to mix and pour the concrete to ensure the quality and accuracy of the concrete. The intelligent detection module uses sensors and monitoring equipment to monitor and detect the structure and materials in the construction process in real time to ensure construction quality and safety. The data analysis feedback module collects and analyzes the data obtained by the intelligent detection module, processes it through algorithms and models, and promptly feeds back to the construction personnel to adjust and optimize the construction process.

[0131] In a specific embodiment of a method for constructing a prefabricated steel-concrete composite structure, an intelligent digital manufacturing system can realize automated production and construction, reduce manual operation and time consumption, and improve construction efficiency. Through the intelligent detection module and the data analysis feedback module, the system can monitor and detect quality problems in the construction process in real time, and take timely measures to adjust and correct them, thereby improving the level of construction quality control. In addition, the intelligent digital manufacturing system can realize precise control of material processing and construction, avoid human errors and waste, and thus reduce construction costs. At the same time, automated equipment and intelligent detection modules can reduce the risk of manual operation, reduce the possibility of personal injury and structural instability, and improve construction safety.

[0132] In the above embodiment, the intelligent monitoring system includes a real-time monitoring and data acquisition module, a BIM technology application module, a construction progress monitoring module, a quality control module and a safety management module; the real-time monitoring and data acquisition module realizes real-time monitoring and data acquisition of various parameters in the construction process through a sensor monitoring system and data acquisition and transmission equipment; the BIM technology application module realizes modeling, coordination and optimization of the construction process of the prefabricated steel-concrete composite structure through a building information model and a BIM data management system; the construction progress monitoring module monitors the construction progress of the prefabricated steel-concrete composite structure in real time through a camera and an image recognition and processing method; the quality control module realizes quality monitoring and evaluation of the prefabricated steel-concrete composite structure through a non-destructive testing method and a data analysis and determination system; the non-destructive testing method performs non-destructive testing on the prefabricated steel-concrete composite structure through ultrasonic, magnetic powder and X-ray methods; the data analysis and determination system analyzes and determines the non-destructive testing data through a neural network model; the safety management module identifies potential safety risks and hidden dangers through image processing, video analysis and machine learning methods.

[0133] In a specific embodiment, the working principle of the intelligent monitoring system is to conduct comprehensive analysis and real-time monitoring of various factors involved in the construction process through the synergy of multiple modules such as real-time monitoring and data acquisition module, BIM technology application module, construction progress monitoring module, quality control module and safety management module, so as to ensure the stability of construction quality and the smooth progress of construction.

[0134] Specifically, the real-time monitoring and data acquisition module uses sensors and data acquisition equipment to monitor and collect data on factors such as the environment, temperature, humidity, pressure, and vibration at the construction site, and transmits the data to the monitoring center; the BIM technology application module uses digital modeling and 3D visualization technology to conduct a full range of digital simulation and prediction of the construction process; the construction progress monitoring module promptly discovers and resolves the problem of delayed construction progress by comparing the differences between the actual construction progress and the planned progress; the quality control module promptly discovers and eliminates quality problems by testing and analyzing the quality of construction materials and components; the safety management module promptly discovers and prevents the occurrence of safety accidents by identifying and analyzing safety hazards at the construction site.

[0135] In a prefabricated steel-concrete composite structure construction method, the implementation of an intelligent monitoring system can improve the stability and consistency of construction quality, reduce the incidence of quality problems, and improve construction efficiency and building quality. At the same time, it can optimize construction progress management, ensure the smooth progress of construction, and avoid construction lags and delays. It can strengthen safety management and risk control, prevent the occurrence of safety accidents on the construction site, and improve the safety level of construction workers. At the same time, it can improve the digitalization and informatization of construction management, strengthen the visualization and transparency of the construction process, and provide basic data support for subsequent maintenance and management.

[0136] In the above embodiment, the component inventory management system includes a component information management module, an inventory management module, a material management module and a laser coding identification module; the component information management module realizes efficient storage and query of component information through a database management system; the database management system realizes rapid retrieval, modification and update of component information through transaction management and database indexing; the inventory management module is connected to the component information management system through a radio frequency identification RFID tag to monitor and manage component inventory in real time; the material management module interconnects the material warehouse and the material management system at the construction site through a wireless communication network to realize real-time monitoring and replenishment of materials; the laser coding identification module engraves the coding identification on the assembled component through a laser engraving method, and realizes unique identification and tracking of each component through the combination of the database management system and the RFID tag.

[0137] In a specific embodiment, the component inventory management system records and manages the relevant information of all components, including the type, specification, quantity, quality, etc. of the components, through the component information management module. Through this module, each component can be identified and tracked. The inventory status of the components is managed through the inventory management module, including the monitoring of warehousing, outbound and inventory quantity. Through this module, the inventory status of each component can be understood in real time, and effective scheduling and management can be carried out. The materials and materials required for the components are managed through the material management module, including the procurement of materials, supplier management and monitoring of material consumption. Through this module, the timely supply and reasonable use of the required materials can be ensured. The laser coding identification module uses laser coding technology to uniquely identify each component for easy identification and tracking of the component. Through laser coding identification, the information of the component can be accurately recorded and managed, and the automatic management and identification of the component can be realized.

[0138] In a specific embodiment of a method for constructing a prefabricated steel-concrete composite structure, through a component information management module and an inventory management module, the system can monitor and manage the inventory of components in real time, avoid the problem of excess or insufficient inventory, and improve the efficiency of inventory management. Through the laser coding identification module, each component has a unique identification code, which can be easily identified and tracked. In this way, the use of each component can be accurately recorded, which is convenient for quality tracing and problem troubleshooting. Through the material management module, the system can effectively manage and monitor the procurement and consumption of required materials, avoid the problem of waste and shortage of materials, and optimize the effect of material management. Through the effective operation of the component inventory management system, the timely supply of required components and materials can be ensured, delays and quality problems caused by shortage of components or substandard quality during the construction process can be avoided, and the construction quality and efficiency can be improved.

[0139] In the above embodiment, the construction risk assessment algorithm based on big data calculates the time required for virtual construction through a construction time budget function; the construction time budget function performs data analysis and modeling on the estimated time, importance and difficulty of different construction tasks through historical construction data and related statistical information; the formula expression of the construction time budget function is:

[0140]

[0141] In formula (4), T VS Represents the time required for virtual construction, which is used to evaluate the estimated time of the entire construction process; θ i is the importance weight of the i-th construction task, which is used to consider the priority and impact of the task; v i represents the difficulty coefficient of the i-th construction task, which is used to measure the complexity of the task; t iis the estimated time of the i-th construction task, which is used to calculate the time contribution of each task; n represents the total number of construction tasks; the construction risk index is calculated by the construction risk assessment index function; the formula expression of the construction risk assessment index function is:

[0142]

[0143] In formula (5), M represents the construction risk index, which is used to evaluate the risk level during the construction process; i is the probability risk coefficient of the i-th construction task, which is used to measure the probability of the task occurring; x i is the consequence risk coefficient of the i-th construction task, which is used to measure the impact after the task occurs; z i is the frequency risk coefficient of the i-th construction task, which is used to consider the number of repeated occurrences of the task; the overall construction resource utilization is obtained through the construction resource utilization evaluation function; the formula expression of the construction resource utilization evaluation function is:

[0144]

[0145] In formula (6), Y represents the construction resource utilization rate, which is used to evaluate the resource utilization efficiency during the construction process; c i It represents the amount of resources required for the i-th construction task, which is used to measure the degree of resource demand of the task; s i is the duration of the i-th construction task, which is used to measure the time length of the task; F is the total virtual construction time, which is used to calculate resource utilization.

[0146] In a specific embodiment, the hardware environment of the construction risk assessment algorithm based on big data is as follows: Server: a server with large memory and processing power, used to store large-scale construction-related data and perform algorithm calculations. Storage device: used to store large-scale construction-related data, such as construction link data, site data, etc. High-performance computing equipment: used to analyze and calculate large-scale data.

[0147] The operation process of the construction risk assessment algorithm based on big data is as follows:

[0148] Data collection: Through sensors, monitoring equipment and other data collection methods, data related to the construction process and construction site, such as temperature, humidity, pressure, vibration, material strength, etc., are obtained.

[0149] Data preprocessing: Perform preprocessing operations such as cleaning, denoising, and correction on the collected raw data to ensure the accuracy and reliability of the data.

[0150] Feature extraction: Extract meaningful features from preprocessed data, such as temperature change trends, humidity fluctuations, and material strength variation.

[0151] Big data analysis: Compare and analyze the extracted feature data with historical data, industry standards, etc., and use machine learning and data mining technologies to build a construction risk model.

[0152] Risk assessment: Based on the established construction risk model, conduct risk assessment on the current construction process and construction site. According to the changes in various indicators, assess the possible risks and potential problems.

[0153] Early warning and suggestions: Based on the risk assessment results, the system generates corresponding early warning information and suggested measures. Early warning information can timely remind relevant personnel to take countermeasures, while suggested measures can guide adjustments and optimizations during the construction process.

[0154] In a method for constructing a prefabricated steel-concrete composite structure, a sample table of test data for a construction risk assessment algorithm based on big data is shown in Table 3:

[0155] Table 3 Test data example of construction risk assessment algorithm based on big data

[0156]

[0157] From the data table 3, it can be seen that the construction risk assessment algorithm based on big data conducts risk assessment on different construction links and site characteristics, and gives corresponding risk assessment results and early warning suggestions to help construction personnel effectively prevent and solve construction risk problems.

[0158] The construction risk assessment algorithm based on big data can effectively prevent accidents and safety problems and ensure the safety of construction workers by comprehensively and accurately assessing the risks of construction links and sites. In addition, by analyzing and optimizing the risk factors in the construction process, unnecessary shutdowns and duplication of work can be reduced, and construction efficiency and quality can be improved. Secondly, by predicting and solving potential risk problems, economic losses caused by losses and delays can be avoided and construction costs can be reduced. In specific implementation, the data test comparison table of the construction risk assessment algorithm based on big data and the traditional algorithm is shown in Table 4:

[0159] Table 4 Data test comparison table

[0160] Construction stage Traditional algorithm to evaluate risk index Assessment risk index based on big data Site leveling 3.5 2.1 Concrete pouring 4.2 3.8 Steel beam installation 3.8 2.9 Rebar tying 4.5 3.2

[0161] By comparison, it can be seen that the construction risk assessment algorithm based on big data can more accurately assess the risks of the construction link than the traditional algorithm. In this example, the risk index given by the algorithm based on big data is relatively low, indicating that the risk level of the construction link is small, so the construction process is safer and more reliable. This further proves the positive and beneficial effects of the construction risk assessment algorithm based on big data in improving construction safety and efficiency.

[0162] In the above embodiment, the BIM collaborative management platform includes a collaborative design module, a bill of quantities management module, a schedule management module, a quality and safety management module, an equipment and material management module, and a construction site collaborative module; the collaborative design module realizes design collaboration and information sharing among participants through a three-dimensional building information model, and the three-dimensional building information model improves design quality and efficiency through collaborative design review and collision detection and analysis methods; the bill of quantities management module realizes automatic measurement, calculation and change of engineering quantities through an automated identification method; the automated identification method automatically identifies the BIM model through automatic measurement, engineering quantity calculation and change management methods component information and generate the corresponding bill of quantities; the schedule management module connects the construction schedule with the BIM model through the schedule optimization method to realize the automatic tracking and optimization of the project progress; the schedule optimization method improves the construction efficiency through resource scheduling and task allocation methods; the quality and safety management module realizes real-time monitoring of the construction site environment, materials and equipment through the Internet of Things and sensor equipment; the equipment and material management module realizes the tracking management of equipment and materials through laser engraving method; the construction site collaboration module realizes real-time collaboration and information exchange between construction site personnel through mobile terminal collaboration, real-time communication and information sharing methods.

[0163] In a specific embodiment, the collaborative design module uses BIM technology to achieve information sharing and collaborative design of multiple design disciplines, ensuring coordination and consistency among disciplines; the bill of quantities management module manages and monitors various engineering quantities to achieve accurate statistics and predictions of engineering quantities; the schedule management module ensures the smooth progress of construction by formulating a detailed construction schedule and comparing and adjusting it with the actual construction progress; the quality and safety management module establishes quality and safety management standards, conducts quality and safety inspections and monitoring, and improves construction quality and safety levels; the equipment and material management module manages and tracks the equipment and materials required for construction to ensure the supply and use of materials during the construction process; the construction site collaboration module achieves information exchange and collaborative operation among all parties on the construction site through real-time data sharing and collaborative work.

[0164] In a prefabricated steel-concrete composite structure construction method, the BIM collaborative management platform improves the coordination and consistency between design and construction, reduces design changes and construction conflicts, and avoids construction delays and additional costs. In addition, it realizes the visual management and optimization of the construction progress, reduces the construction cycle, and improves the accuracy of the construction period control. At the same time, it improves the quality and safety management level, prevents and reduces the occurrence of construction quality problems and safety accidents, and protects the life safety of construction personnel. It realizes the precise management and tracking of equipment and materials, reduces material waste and loss, and improves the efficiency of resource utilization. It enhances the collaborative work ability between all parties on the construction site, improves construction efficiency and teamwork effect.

[0165] In the above embodiment, the intelligent structural health monitoring system includes a sensor module, a data management module, a data analysis module and a visualization display module; the sensor module monitors the assembled steel-concrete composite structure in real time through a strain sensor, an acceleration sensor and a temperature sensor; the strain sensor monitors the strain of the structure in real time through a resistor; the acceleration sensor evaluates the stability and dynamic response of the structure through a vibration sensor; the data management module includes a data storage unit and a data transmission unit; the data storage unit collects and stores the data obtained by the sensor through a cloud storage device; the data transmission unit transmits the collected data to a data center or a cloud platform through a wireless communication method; the data analysis module includes a structure identification and monitoring unit, a structure health assessment unit and a maintenance plan optimization unit; based on the collected data, the structure identification and monitoring unit identifies and locates the damage of the assembled steel-concrete composite structure through a support vector machine model; the structure health assessment unit performs health assessment and structural reliability analysis on the assembled steel-concrete composite structure through a finite element analysis method; according to the structural health assessment result, the maintenance plan optimization unit optimizes the maintenance strategy of the assembled steel-concrete composite structure through a simulated annealing algorithm; the visualization display module displays the structure in a chart and describes the warning information through a chart library and a natural language processing method.

[0166] In a specific embodiment, the intelligent structural health monitoring system monitors the stress, deformation, temperature, humidity and other parameters of the structure through the sensor module. The sensor converts the health status of the structure into a digital signal by collecting and transmitting data in real time. The data transmitted by the sensor module is received, stored and managed by the data management module. The data management module can classify, organize and archive the structural health data for subsequent analysis and processing. The data analysis module uses data processing algorithms and models to analyze and calculate the data collected by the sensor module. Through the data analysis module, the evaluation, abnormality detection and early warning of the structural health status can be realized. The visualization display module presents the structural health status obtained by the data analysis module to the user in a visual way. The user can intuitively understand the health status of the structure through charts, curves, etc., and take corresponding measures in time.

[0167] In a specific embodiment of a method for constructing a prefabricated steel-concrete composite structure, the sensor module can monitor the stress, deformation, temperature, humidity and other parameters of the structure in real time, detect abnormal conditions of the structure in a timely manner, and improve the safety and stability of the structure. The data analysis module can identify potential structural problems by analyzing and calculating the data collected by the sensor, and give early warning prompts to help users take timely measures to avoid accidents. Through the intelligent structural health monitoring system, users can intuitively understand the health status of the structure, and can formulate effective maintenance management strategies based on the monitoring results to extend the service life of the structure and reduce maintenance costs. In addition, the intelligent structural health monitoring system can monitor the deformation and stress of the structure in real time during the construction process, helping construction personnel to adjust the construction plan in a timely manner and improve construction quality and safety.

[0168] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A method for constructing an assembled steel-concrete composite structure, characterized in that: The following steps are involved: Step 1: Design and planning; Build a 3D digital model of the assembled steel-concrete composite structure through sensors and 3D modeling methods to achieve information sharing and collaborative design throughout the entire process; Step 2: Modular design; The structure is decomposed into standardized modules through BIM-based modular design method to reduce construction difficulty and quality fluctuation; Step 3: Process optimization; Analyze and predict at least the construction site topography, environment, and construction team staffing factors through machine learning-based construction optimization algorithms to optimize the construction process; Step 4: Automated manufacturing; Realize automated production of prefabricated components through intelligent digital manufacturing systems, improve work efficiency and quality control level; Step 5: Real-time monitoring and data collection; Real-time monitoring of the construction progress and quality of prefabricated steel-concrete composite structures through intelligent monitoring systems; Step 6: Identification management of prefabricated components; Improving construction site material management and assembly efficiency through a component inventory management system; the component inventory management system identifies assembled components through laser coding; Step 7: Conduct a plan rehearsal simulation; The installation sequence and construction work of prefabricated components are simulated by a virtual construction simulation model. The virtual construction simulation model conducts risk assessment on the construction links and construction sites through a construction risk assessment algorithm based on big data, and gives corresponding warnings and suggestions; Step 8: Collaborative management of construction sites; Improve construction coordination and efficiency through BIM collaborative management platform; Step 9: Digital maintenance and repair; Realize real-time monitoring, prediction and optimization of maintenance plans of structural status through intelligent structural health monitoring system; The construction process optimization algorithm based on machine learning predicts the impact of the construction site terrain on the construction time through the construction time prediction function; the construction time prediction function collects and analyzes a large amount of historical construction data through the machine learning algorithm, and predicts the impact of different site terrains on the construction time through the regression analysis method; the formula expression of the construction time prediction function is: In formula (1), T represents the predicted construction time; β is the site slope, which is used to indicate the degree of ground inclination of the construction site; S is the distance between the site and the construction site, which indicates the distance between the construction site and the construction site; γ is the site roughness, which is used to indicate the unevenness of the ground of the construction site; K is the site altitude, which is used to indicate the relative height of the construction site; d represents the influence weight; the steel structure installation sequence optimization function is used to optimize the steel structure installation sequence; the formula expression of the steel structure installation sequence optimization function is: In formula (2), R is the optimized steel structure installation sequence; P is the connection strength between the mth component and the jth component, which is used to indicate the connection relationship between the steel structure components; O is the height of the mth component, which is used to indicate the vertical size of the steel structure component; C is the weight of the mth component, which is used to indicate the mass of the steel structure component; L is the length of the jth component, which is used to indicate the horizontal size of the steel structure component; the concrete pouring sequence optimization function is used to optimize the concrete pouring sequence; the formula expression of the concrete pouring sequence optimization function is: In formula (3), P represents the optimized concrete pouring sequence; h is the volume of the δth pouring area, which is used to indicate the size of the concrete pouring area; v is the concrete strength grade of the δth pouring area, which is used to indicate the quality standard of the concrete; ρ is the temperature of the δth pouring area, which is used to indicate the ambient temperature during concrete pouring; μ is the distance between the δth pouring area and the construction site, which is used to indicate the distance between the pouring area and the construction site.

2. The method for constructing a prefabricated steel-concrete composite structure according to claim 1, characterized in that: The working principle steps of the BIM-based modular design method are as follows: S1, structural decomposition; Create component element associations through the object-relationship model and decompose the entire structure according to functions and standardization requirements; S2, module design; The parametric modeling function of BIM software defines at least the geometry, size, connection method and material requirement parameters of each module; S3, modular manufacturing; Customized manufacturing of modules through CNC cutting and CNC punching; S4, modular identification; Laser coding adds a unique identification code to each module for quick identification and installation at the construction site; S5. Construction plan optimization; The three-dimensional design model NW is used to simulate and optimize the construction process to determine the best construction sequence and method; the three-dimensional design model NW simulates the assembly process of the modular structure through a 4D simulation method; S6, precise installation; The module positions are measured by a total station to ensure the connection accuracy between modules and the accuracy of the overall structure.

3. The method for constructing a prefabricated steel-concrete composite structure according to claim 1, characterized in that: The intelligent digital manufacturing system includes a structural analysis module, a material processing module, an automatic welding module, a concrete manufacturing module, an intelligent detection module and a data analysis and feedback module; the structural analysis module simulates and evaluates the mechanical properties of the prefabricated structure through the finite element analysis method; the material processing module realizes the automatic cutting of the steel structure through a CNC steel structure cutting machine; the automatic welding module realizes the precise positioning and automatic welding of the prefabricated steel components through machine vision, sensors and automatic control methods; the concrete manufacturing module realizes the efficient manufacturing of concrete components through a precast concrete production line; the precast concrete production line realizes the automatic production and processing of concrete through an automatic mixer and an automatic pouring machine; the intelligent detection module performs online monitoring and detection of the size, shape and surface quality of the prefabricated components through non-contact measurement and image processing methods; the data analysis and feedback module collects and analyzes the data in the construction process in real time through the Internet of Things and big data analysis methods, and issues problem warnings.

4. The method for constructing a prefabricated steel-concrete composite structure according to claim 1, characterized in that: The intelligent monitoring system includes a real-time monitoring and data acquisition module, a BIM technology application module, a construction progress monitoring module, a quality control module and a safety management module; the real-time monitoring and data acquisition module realizes real-time monitoring and data acquisition of various parameters in the construction process through a sensor monitoring system and data acquisition and transmission equipment; the BIM technology application module realizes modeling, coordination and optimization of the construction process of the prefabricated steel-concrete composite structure through a building information model and a BIM data management system; the construction progress monitoring module monitors the construction progress of the prefabricated steel-concrete composite structure in real time through a camera and an image recognition and processing method; the quality control module realizes quality monitoring and evaluation of the prefabricated steel-concrete composite structure through a non-destructive testing method and a data analysis and determination system; the non-destructive testing method performs non-destructive testing on the prefabricated steel-concrete composite structure through ultrasonic, magnetic powder and X-ray methods; the data analysis and determination system analyzes and determines the non-destructive testing data through a neural network model; the safety management module identifies potential safety risks and hidden dangers through image processing, video analysis and machine learning methods.

5. The method for constructing a prefabricated steel-concrete composite structure according to claim 1, characterized in that: The component inventory management system includes a component information management module, an inventory management module, a material management module and a laser coding identification module; The component information management module realizes efficient storage and query of component information through a database management system; the database management system realizes rapid retrieval, modification and update of component information through transaction management and database indexing; The inventory management module is connected to the component information management system through the radio frequency identification RFID tag to monitor and manage the component inventory in real time; the material management module interconnects the material warehouse and the material management system of the construction site through a wireless communication network to realize real-time monitoring and replenishment of materials; the laser coding identification module engraves the coding identification on the assembled component through a laser engraving method, and realizes the unique identification and tracking of each component through the combination of the database management system and the RFID tag.

6. The method for constructing a prefabricated steel-concrete composite structure according to claim 1, characterized in that: The construction risk assessment algorithm based on big data calculates the time required for virtual construction through a construction time budget function; the construction time budget function performs data analysis and modeling on the estimated time, importance and difficulty of different construction tasks through historical construction data and related statistical information; the formula expression of the construction time budget function is: In formula (4), T VS Represents the time required for virtual construction, which is used to evaluate the estimated time of the entire construction process; θ i is the importance weight of the i-th construction task, which is used to consider the priority and impact of the task; v i represents the difficulty coefficient of the i-th construction task, which is used to measure the complexity of the task; t i is the estimated time of the i-th construction task, which is used to calculate the time contribution of each task; n represents the total number of construction tasks; the construction risk index is calculated by the construction risk assessment index function; the formula expression of the construction risk assessment index function is: In formula (5), M represents the construction risk index, which is used to evaluate the risk level during the construction process; y i is the probability risk coefficient of the i-th construction task, which is used to measure the probability of the task occurring; x i is the consequence risk coefficient of the i-th construction task, which is used to measure the impact after the task occurs; z i is the frequency risk coefficient of the i-th construction task, which is used to consider the number of repeated occurrences of the task; the overall construction resource utilization is obtained through the construction resource utilization evaluation function; the formula expression of the construction resource utilization evaluation function is: In formula (6), Y represents the construction resource utilization rate, which is used to evaluate the resource utilization efficiency during the construction process; c i It represents the amount of resources required for the i-th construction task, which is used to measure the degree of resource demand of the task; s i is the duration of the i-th construction task, which is used to measure the time length of the task; F is the total virtual construction time, which is used to calculate resource utilization.

7. The method for constructing a prefabricated steel-concrete composite structure according to claim 1, characterized in that: The BIM collaborative management platform includes a collaborative design module, a bill of quantities management module, a schedule management module, a quality and safety management module, an equipment and material management module, and a construction site collaborative module; The collaborative design module realizes design collaboration and information sharing among participants through a three-dimensional building information model, and the three-dimensional building information model improves design quality and efficiency through collaborative design review and collision detection and analysis methods; the bill of quantities management module realizes automatic measurement, calculation and change of engineering quantities through an automated identification method; the automated identification method automatically identifies component information in a BIM model through automated measurement, engineering quantity calculation and change management methods, and generates a corresponding bill of quantities; The schedule management module connects the construction schedule with the BIM model through a schedule optimization method to achieve automatic tracking and optimization of the project progress; the schedule optimization method improves construction efficiency through resource scheduling and task allocation methods; the quality and safety management module achieves real-time monitoring of the construction site environment, materials and equipment through the Internet of Things and sensor equipment; the equipment and material management module achieves tracking management of equipment and materials through laser engraving methods; the construction site collaboration module achieves real-time collaboration and information exchange between construction site personnel through mobile collaboration, real-time communication and information sharing methods.

8. The method for constructing a prefabricated steel-concrete composite structure according to claim 1, characterized in that: The intelligent structural health monitoring system includes a sensor module, a data management module, a data analysis module and a visualization display module; the sensor module monitors the assembled steel-concrete composite structure in real time through a strain sensor, an acceleration sensor and a temperature sensor; the strain sensor monitors the strain of the structure in real time through a resistor; the acceleration sensor evaluates the stability and dynamic response of the structure through a vibration sensor; the data management module includes a data storage unit and a data transmission unit; the data storage unit collects and stores the data obtained by the sensor through a cloud storage device; the data transmission unit transmits the collected data to a data center or a cloud platform through a wireless communication method; the data analysis module includes a structure identification and monitoring unit, a structure health assessment unit and a maintenance plan optimization unit; based on the collected data, the structure identification and monitoring unit identifies and locates the damage of the assembled steel-concrete composite structure through a support vector machine model; the structure health assessment unit performs health assessment and structural reliability analysis on the assembled steel-concrete composite structure through a finite element analysis method; according to the structural health assessment results, the maintenance plan optimization unit optimizes the maintenance strategy of the assembled steel-concrete composite structure through a simulated annealing algorithm; The visualization display module displays the structure in charts and describes the warning information through a chart library and a natural language processing method.

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