Steel structure virtual trial assembly method and system based on BIM platform

Abstract

This invention discloses a method and system for virtual trial assembly of steel structures based on a BIM platform. The invention relates to the field of virtual testing of steel building materials and includes the following steps: analyzing carbon footprint data from multiple historical assembly processes, selecting the assembly sequence with the smallest carbon footprint as the target sequence, clustering components based on their inter-component influences to form a set of sub-components, constructing an assembly association vector graph, assigning risk values ​​to components based on the importance of their function types, analyzing deviation sensitivity coefficients, performing virtual trial assembly through a BIM model, monitoring assembly errors, and identifying influencing components. An error prediction model is constructed to output the assembly errors affecting components and determine risk coefficients. The positions of the erroneous components are adjusted according to the risk coefficients until the assembly conditions are met, completing the virtual trial assembly. This method improves assembly efficiency and accuracy, and reduces the impact of assembly errors on structural safety.

Description

Technical Field

[0001] This invention relates to the field of virtual testing of steel building materials, specifically to a method and system for virtual trial assembly of steel structures based on a BIM platform. Background Technology

[0002] With the rapid development of the construction industry, steel structures, due to their superior strength and lightweight properties, are widely used in high-rise buildings, bridges, and other large-scale infrastructure projects. However, the installation and assembly process of steel structures remains one of the most critical and complex aspects of building construction. Currently, traditional steel structure assembly often relies on manual experience and intuitive judgment, leading to uncertain assembly sequences and inaccurate component assembly, which in turn affects the safety and stability of the overall structure. Furthermore, traditional assembly methods lack effective simulation and verification methods before construction, making it difficult to foresee and control potential risks during the assembly process, thus posing hidden dangers to project quality. In practice, errors, deviations, and improper assembly sequences that may occur during assembly can negatively impact the final assembly quality. Especially in large-scale projects, assembly errors can accumulate due to mutual influence, ultimately leading to overall structural deformation and even safety accidents. Furthermore, traditional methods fail to systematically analyze the impact of different assembly sequence combinations on the carbon footprint, thus failing to provide effective sustainability data for construction decisions. These pain points not only reduce the economic benefits of the project but also increase the construction period and worker safety risks, urgently requiring improvement through new technological means. Therefore, there is an urgent need for a virtual trial assembly method for steel structures to simulate different assembly sequences, analyze and optimize the assembly process, and address issues such as assembly uncertainty, insufficient carbon emission control and risk monitoring in traditional methods.

[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for virtual trial assembly of steel structures based on a BIM platform, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for virtual trial assembly of steel structures based on a BIM platform, comprising the following steps: The total carbon footprint data of multiple historical assembly processes were analyzed and determined. The assembly sequence of steel structure components in the historical assembly process with the smallest total carbon footprint was selected as the target assembly sequence. Based on the target assembly sequence and the influence between each steel structure component, the steel structure components were clustered to form several sub-component sets. Based on the formed set of sub-components, an assembly association vector diagram is constructed, and for any steel structure component, a risk value is assigned based on the importance of the function type. Based on the risk value of each steel structure component and its assembly sequence, the deviation sensitivity coefficient of each steel structure component is analyzed. A BIM model is constructed based on the geometric feature parameters of each steel structure component. Virtual trial assembly is performed using the BIM model according to the target assembly sequence. Assembly errors of each steel structure component are detected by monitoring the data stream. When an assembly error is detected in a steel structure component, the affected component is identified by searching the assembly association vector graph. An error prediction model is constructed, taking the ranking of the component with assembly error in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, and the assembly error as inputs, and using the assembly error affecting the component as a label for model training, and determining the risk coefficient based on the output of the assembly error affecting the component. The assembly conditions are determined based on the risk coefficient. If they are met, the assembly of subsequent steel structure components is carried out. Otherwise, the positions of steel structure components with assembly errors are adjusted until the assembly conditions are met, thereby completing the virtual trial assembly of the steel structure.

[0006] Furthermore, the geometric feature parameters of each steel structure component specifically include the component length, width, height, and outline shape. A BIM model is constructed based on the geometric feature parameters of each steel structure component. Based on the material type and material physical parameters, the constructed BIM model is material-defined to complete the model construction of each steel structure component. The material physical parameters include material mass, density, and hardness. The historical steel structure component assembly sequence refers specifically to the assembly sequence of steel structure components that could previously complete the overall structural assembly, thereby forming several assembly sequence combinations.

[0007] Furthermore, the logic behind analyzing and extracting the total carbon footprint of each assembly sequence combination in actual assembly is as follows: obtaining the energy consumption for completing the assembly of each steel structure component, the transportation distance for each steel structure component assembly, and the energy consumption generated by the transportation distance. Among them, the energy consumption for completing the assembly of each steel structure component specifically refers to the total power consumption of the assembly operation, including the total power consumption of hoisting and installation operations, and the energy consumption generated by the transportation distance specifically refers to the total carbon emissions generated by transporting the steel structure components. The logic behind calculating the total carbon footprint of each assembly sequence combination is as follows: The total carbon footprint of each assembly sequence combination is calculated based on the energy consumption of completing the assembly of each steel structure component and the energy consumption generated during the transportation of the steel structure components. The specific formula used for this calculation is as follows: In the formula, The total carbon footprint of the z-th assembly sequence combination is... To determine the total power consumption for assembling the steel structure components in the z-th assembly sequence, As the energy conversion factor, Let be the total carbon emissions generated from transporting steel structural components in the z-th assembly sequence combination, where z is the index of the assembly sequence combination.

[0008] Furthermore, the specific method for forming several sub-component sets is as follows: based on the connection relationship of each steel structure component and combined with past assembly experience, the influence relationship between each steel structure component is determined, and the components with influence relationships are clustered to form sub-component sets; The assembly association vector graph specifically includes nodes and directed edges. The nodes represent each steel structure component in any set of sub-components, and the directed edges represent the connection order relationship between steel structure components that have an influence relationship. The specific types of functions of each steel structure component include primary load-bearing type, secondary load-bearing type, connection type, and other types. The logic for assigning risk values ​​based on the importance of function type is as follows: the risk values ​​of steel structure components are assigned in the order of primary load-bearing type, secondary load-bearing type, connection type, and other types, with the risk value assigned to primary load-bearing type steel structure components being the highest and the risk value assigned to other types of steel structure components being the lowest. The logic behind analyzing the deviation sensitivity coefficients of each steel structure component is as follows: Based on the ranking of different steel structure components in the target assembly sequence, and combined with the total number of steel structure components, a normalization process is performed. The normalized value is used as the installation risk coefficient for that steel structure component. Based on the installation risk coefficient and the risk assignment for that steel structure component, the deviation sensitivity coefficient of each steel structure component is determined. The specific formula for calculating the installation risk coefficient is as follows: In the formula, Let be the installation risk coefficient for the i-th steel structure component. Let represent the ranking of the i-th steel structure component in the target assembly sequence. This represents the total number of steel structure components. The specific formula used to calculate the deviation sensitivity coefficient of each steel structure component is as follows: In the formula, Let be the deviation sensitivity coefficient of the i-th steel structure member. Assign a risk value to the i-th steel structure component. and These are the weighting coefficients, where and and All are greater than 0.

[0009] Furthermore, the logic underlying the analysis of the assembly errors of each steel structure component is as follows: By performing a depth-first search along all paths originating from the steel structural member in the assembly association vector graph, all subsequent steel structural members related to the steel structural member are identified as the influencing members of the steel structural member. The specific method for constructing the training error prediction model is as follows: For each steel structure component, several assembly errors are randomly set, and the assembly errors of subsequent steel structure components are determined through simulated assembly. The ranking of the steel structure component in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, the set assembly errors and the assembly error data affecting the components are mapped one by one to form a training dataset. Based on the training dataset, an error prediction model is constructed with the input of the target assembly order ranking, the deviation sensitivity coefficient of the steel structure component, and the set assembly error. The output is the assembly error data of the steel structure component affecting the component. The error prediction model is trained using the training dataset. Based on the error prediction model, the assembly error of the steel structure component affecting the component under different assembly errors is determined.

[0010] Furthermore, the specific logic underlying the determination of the risk coefficient based on the impact of the component assembly error is as follows: For steel structural components with detected assembly errors, their corresponding deviation sensitivity coefficients are retrieved, and the predicted assembly errors affecting these components are analyzed using an error prediction model. Based on the predicted assembly errors affecting the components, the risk coefficient of the overall assembled structure is simulated. The specific formula used to calculate the risk coefficient is as follows: In the formula, For risk coefficient, Let q be the predicted assembly error of the q-th influencing component, where q is the index of the influencing component.

[0011] Furthermore, the assembly conditions are specifically set as follows: In the formula, This is the cumulative risk threshold. The assembly error threshold is the predicted assembly error for each influencing component.

[0012] This invention also provides a BIM-based virtual trial assembly system for steel structures, which is used to execute the above-described BIM-based virtual trial assembly method for steel structures, including: The carbon footprint analysis module is used to analyze and determine the total carbon footprint data of multiple historical assembly processes, select the assembly sequence of steel structure components in the historical assembly process with the smallest total carbon footprint as the target assembly sequence, and cluster the steel structure components based on the target assembly sequence and the influence between each steel structure component to form several sub-component sets. The assembly sequence analysis module is used to construct an assembly association vector diagram based on the formed set of sub-components, and assign a risk value to any steel structure component based on the importance of its function type. Based on the risk value of each steel structure component and its assembly sequence, the module analyzes the deviation sensitivity coefficient of each steel structure component. The deviation impact analysis module is used to construct a BIM model based on the geometric characteristics of each steel structure component, perform virtual trial assembly according to the target assembly sequence using the BIM model, detect the assembly error of each steel structure component by monitoring the data stream, and when an assembly error is detected in a steel structure component, the affected component is identified by searching the assembly association vector graph. The assembly risk analysis module is used to build an error prediction model. It takes the ranking of the component with assembly error in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, and the assembly error as input, and uses the assembly error affecting the component as a label for model training. The risk coefficient is determined based on the output of the assembly error affecting the component. The assembly adjustment and optimization module is used to determine whether the assembly conditions are met based on the risk coefficient. If so, the assembly of subsequent steel structure components is carried out; otherwise, the position of the steel structure components with assembly errors is adjusted until the assembly conditions are met, thereby completing the virtual trial assembly of the steel structure.

[0013] Compared with the prior art, the beneficial effects of the present invention are: By quantitatively assessing the carbon emissions of each assembly sequence during the assembly simulation phase, the assembly sequence with the smallest total carbon footprint is selected, reducing material waste and rationally allocating resources, further reducing construction costs, and achieving rational utilization of resources while meeting design requirements; Secondly, this solution implements a dynamic monitoring and adjustment mechanism for virtual trial assembly. By monitoring the data stream in real time, the system can promptly detect assembly errors and analyze the risk coefficient of component deviations through assembly correlation vector graphs. This ensures that each component can be installed in compliance with the target assembly sequence, which further improves the accuracy of assembly, reduces the waste of resources caused by rework and readjustment due to errors, optimizes the construction cycle and cost, and ensures the integrity and stability of the structure. It also greatly reduces the impact of assembly errors on structural safety and lowers the possibility of accidents. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 Fitting a curve for the installation risk coefficient versus the deviation sensitivity coefficient; Figure 3 Fitting curve for assembly sequence position-deviation sensitivity coefficient; Figure 4 This is a schematic diagram of the overall system structure of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0016] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0017] Example: Please see Figures 1-3 The present invention provides a technical solution: A method for virtual trial assembly of steel structures based on a BIM platform, comprising the following steps: Step 1: Analyze and determine the total carbon footprint data of multiple historical assembly processes, select the assembly sequence of steel structure components in the historical assembly process with the smallest total carbon footprint as the target assembly sequence, and cluster the steel structure components according to the target assembly sequence and the influence between each steel structure component to form several sub-component sets.

[0018] The geometric feature parameters of each steel structure component specifically include the component length, width, height, and outline shape. A BIM model is constructed based on the geometric feature parameters of each steel structure component. Based on the material type and material physical parameters, the constructed BIM model is material-defined to complete the model construction of each steel structure component. The material physical parameters include material mass, density, and hardness. The specific method for obtaining the geometric features of each steel structure component is as follows: using high-precision measuring tools such as laser rangefinders and total stations, the length, width, and height of the components are directly measured on-site. This method can ensure that the obtained dimensional data has high accuracy.

[0019] For components with complex shapes, a 3D scanner can be used to create a 3D model and obtain detailed geometric data, which is especially suitable for irregularly shaped components; or, based on drawings from the design stage, such as construction drawings and structural drawings, which usually contain detailed component dimensions and shape information, the geometric features of each component can be extracted through careful analysis of these drawings.

[0020] The specific method for obtaining the material's physical parameters is as follows: Material tests are conducted on actual steel samples to obtain their physical parameters. Common testing methods include tensile testing, hardness testing, and density testing. These experiments accurately obtain parameters such as the material's mass, density, and hardness. Laboratories typically use standardized testing methods, such as international standards like ISO and ASTM, to ensure the reliability of the results. Alternatively, the factory or manufacturer usually provides the material's Technical Data Sheet (TDS), which contains the material's physical and chemical properties. These data sheets provide necessary information for model construction, such as density, hardness, and elastic modulus.

[0021] The historical steel structure component assembly sequence refers specifically to the assembly sequence of steel structure components that could previously complete the overall structural assembly, thereby forming several assembly sequence combinations.

[0022] One method for obtaining historical steel structure component assembly sequence is through project reports. Engineering projects typically have phased reports at different stages, which record in detail the construction methods, assembly sequence, and materials used. These documents can provide systematic data on historical assembly sequences.

[0023] The logic behind analyzing and extracting the total carbon footprint of each assembly sequence combination in actual assembly is as follows: obtain the energy consumption for completing the assembly of each steel structure component, the transportation distance for each steel structure component assembly, and the energy consumption generated by the transportation distance. Among them, the energy consumption for completing the assembly of each steel structure component specifically refers to the total power consumption of the assembly operation, including the total power consumption of hoisting and installation operations, and the energy consumption generated by the transportation distance specifically refers to the total carbon emissions generated by transporting the steel structure components. Understand the fuel or electricity consumption of transportation vehicles, such as the amount of fuel or electricity consumed per kilometer by a truck. This data can be obtained from vehicle manuals or information provided by the manufacturer, and is calculated as the total carbon emissions generated from transporting steel structural components by multiplying the unit energy consumption by the transport distance.

[0024] The logic behind calculating the total carbon footprint of each assembly sequence combination is as follows: The total carbon footprint of each assembly sequence combination is calculated based on the energy consumption of completing the assembly of each steel structure component and the energy consumption generated during the transportation of the steel structure components. The specific formula used for this calculation is as follows: In the formula, The total carbon footprint of the z-th assembly sequence combination is... To determine the total power consumption for assembling the steel structure components in the z-th assembly sequence, As the energy conversion factor, Let be the total carbon emissions generated from transporting steel structural components in the z-th assembly sequence combination, where z is the index of the assembly sequence combination.

[0025] It should be noted that the total carbon footprint of the z-th assembly sequence combination Used to quantify CO2 equivalent emissions, where the total carbon footprint of the z-th assembly sequence combination is... The higher the value, the greater the equivalent carbon dioxide emissions and the more energy consumed.

[0026] Among them, the power conversion factor The specific settings are determined by combining actual energy consumption with expert experience.

[0027] Step 2: Based on the formed set of sub-components, construct an assembly association vector diagram, and assign a risk value to any steel structure component based on the importance of its function type. Based on the risk value of each steel structure component and its assembly sequence, analyze the deviation sensitivity coefficient of each steel structure component.

[0028] By selecting the assembly sequence combination with the minimum total carbon footprint, the environmental impact of the project can be directly reduced, making energy and material use more efficient, avoiding unnecessary resource waste, reducing the environmental burden during construction, and decreasing energy consumption and transportation distance. This not only reduces carbon emissions but also lowers construction costs and improves overall construction efficiency. Quantitative analysis of the total carbon footprint allows for a more scientific and objective decision-making process by selecting the optimal assembly sequence combination, reducing the uncertainty caused by subjective judgment.

[0029] The specific method for forming several sub-component sets is as follows: based on the connection relationship of each steel structure component and combined with past assembly experience, the influence relationship between each steel structure component is determined, and the components with influence relationships are clustered to form sub-component sets; The specific method for determining the influence relationship between each steel structure component is as follows: By reviewing the design drawings and structural calculation sheets, we can analyze the connection methods of each steel structure component in detail, including welding, bolting, etc. We can record the connection nodes of each component and the information of the interconnected components. In combination with past construction experience, we can collect and organize the problems encountered in actual construction, solutions, and records of mutual influence between components, including common construction sequences, connection sequences, and special case handling. Based on connectivity and assembly experience, a component influence matrix is ​​constructed. Components are treated as rows and columns of the matrix. If component A and component B have a connection or influence relationship, the corresponding position in the matrix is ​​marked as 1; otherwise, it is marked as 0. Components are treated as nodes in a graph, and connectivity relationships are treated as edges. Graph theory algorithms, such as depth-first search and breadth-first search, are used to analyze the connectivity between nodes and identify mutually influential component sets, forming several sub-component sets. The assembly association vector graph specifically includes nodes and directed edges. A node represents each steel structure component in any set of sub-components, and a directed edge represents the connection order between steel structure components that have an influence relationship. Each steel structure component creates one node. The node definition should include the component's unique identifier, such as its number or name, and its related attributes, such as dimensions and weight. The assembly relationship between components is determined according to the assembly sequence. For example, if component A must be assembled before component B, a directed edge is established from node A to node B. A graph structure is constructed using graphical tools or programming languages, such as Python's NetworkX library, and all nodes and their directed edges are added to the graph to form a complete assembly association vector graph.

[0030] By drawing assembly relationship vector diagrams, the assembly relationships and sequence between various components can be displayed intuitively, helping the project team understand the assembly process and facilitating subsequent planning and execution.

[0031] The specific types of functions of each steel structure component include primary load-bearing type, secondary load-bearing type, connection type, and other types. The logic for assigning risk values ​​based on the importance of function type is as follows: the risk values ​​of steel structure components are assigned in the order of primary load-bearing type, secondary load-bearing type, connection type, and other types, with the risk value assigned to primary load-bearing type steel structure components being the highest and the risk value assigned to other types of steel structure components being the lowest. It should be noted that the main load-bearing components bear the main loads of the building or structure, including self-weight, service load, wind load, earthquake, etc. Therefore, their failure may lead to serious safety accidents or structural collapse, causing significant loss of life and property. Therefore, the risk of setting the main load-bearing type components is the highest, to emphasize their key role in the overall structure. Although secondary load-bearing components are not as important as primary load-bearing components, they still bear loads to a certain extent and work together with primary load-bearing components to affect the structure. Their failure may affect the performance of primary load-bearing components and lead to a decrease in the safety of the entire structure. Therefore, the risk assignment of secondary load-bearing components is relatively high, reflecting their importance to structural safety. Connecting members are responsible for linking different steel structural components to ensure effective load transfer. Although their primary function is to provide connections, failure in these connections can compromise the stability and safety of the entire structure. Therefore, connecting members are assigned a lower risk value than load-bearing members. Other types of steel structural components (such as decorative or non-load-bearing components) typically do not directly bear loads, and their failure has a relatively small impact on the overall structure. The main functions of these components are aesthetics, sound insulation, and heat insulation, rather than structural stability. Therefore, these components have the lowest risk assignment, reflecting their low impact on the overall structural safety.

[0032] Prioritizing components with high risk values ​​during risk assessment and management helps optimize resource allocation. This ensures that more attention and resources are invested in components that have the greatest impact on structural safety during design, construction, and maintenance, thereby effectively reducing potential risks.

[0033] The logic behind analyzing the deviation sensitivity coefficients of each steel structure component is as follows: Based on the ranking of different steel structure components in the target assembly sequence, and combined with the total number of steel structure components, a normalization process is performed. The normalized value is used as the installation risk coefficient for that steel structure component. Based on the installation risk coefficient and the risk assignment for that steel structure component, the deviation sensitivity coefficient of each steel structure component is determined. The specific formula for calculating the installation risk coefficient is as follows: In the formula, Let be the installation risk coefficient for the i-th steel structure component. Let represent the ranking of the i-th steel structure component in the target assembly sequence. This represents the total number of steel structure components. It should be noted that in the assembly process of steel structures, the components assembled in the early stages usually provide basic support and positional reference for the subsequent components. If there are deviations or errors in these early assembled components, it will directly affect the assembly accuracy and position of the subsequent components, which may threaten the stability and safety of the entire structure. The components assembled later often depend on the accuracy of the earlier components. Especially in complex structures, errors in the earlier components may gradually amplify, making the assembly of the later components more difficult and inaccurate. Therefore, the earlier components need to be given a higher installation risk factor to reflect their significant impact on the overall assembly quality.

[0034] The specific formula used to calculate the deviation sensitivity coefficient of each steel structure component is as follows: In the formula, Let be the deviation sensitivity coefficient of the i-th steel structure member. Assign a risk value to the i-th steel structure component. and These are the weighting coefficients, where and and All are greater than 0.

[0035] It should be noted that the deviation sensitivity coefficient of the i-th steel structure component... It reflects the impact of deviation on the overall structure; the larger the value, the greater the impact on the overall structure.

[0036] in, This reflects the risk assignment of a component, indicating its importance to structural safety. Components with higher risk assignments may lead to more severe consequences in the event of an error. and Proportional.

[0037] This reflects the importance of components in the assembly sequence. Components assembled earlier usually have a greater impact on the stability of the overall structure. The natural logarithm function is used. This method, used to represent the installation risk coefficient, effectively captures the nonlinear characteristics of the risk coefficient. With... The increase, The rate of increase gradually slows down, and this setting avoids the excessive influence of extreme risk values ​​on the deviation sensitivity coefficient.

[0038] The risk assignment of components has a more significant effect on the deviation sensitivity coefficient, emphasizing the priority of high-risk components, therefore setting... and and All are greater than 0.

[0039] Step 3: Construct a BIM model based on the geometric feature parameters of each steel structure component. Perform virtual trial assembly according to the target assembly sequence using the BIM model. Detect assembly errors of each steel structure component by monitoring the data stream. When an assembly error is detected in a steel structure component, search the assembly association vector graph to identify the affected component.

[0040] Virtual trial assembly was conducted using BIM software, and the assembly status of each component was recorded to simulate the assembly behavior during actual construction. When an assembly error was detected in a component, a depth-first search was performed along all paths starting from that steel structure component in the assembly association vector diagram to identify all subsequent steel structure components related to that steel structure component, which were then considered as the affected components of that steel structure component. Virtual trial assembly using BIM models allows for intuitive observation of the assembly status of each component, enabling early detection of potential problems and reducing risks during actual construction. The construction of assembly association vector diagrams and the depth-first search method systematize the identification process of influencing components, comprehensively considering the interrelationships between components and improving the accuracy of the analysis. By identifying influencing components, the assembly sequence and methods can be optimized in a targeted manner, reducing chain reactions caused by errors and thus improving the efficiency of the entire construction process.

[0041] Step 4: Construct an error prediction model, using the ranking of the component with assembly error in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, and the assembly error as inputs, and use the assembly error affecting the component as a label for model training, and determine the risk coefficient based on the output of the assembly error affecting the component.

[0042] The specific method for constructing the training error prediction model is as follows: For each steel structure component, several assembly errors are randomly set, and the assembly errors of subsequent steel structure components are determined through simulated assembly. The ranking of the steel structure component in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, the set assembly errors and the assembly error data affecting the components are mapped one by one to form a training dataset. Based on the training dataset, an error prediction model is constructed with the input of the target assembly order ranking, the deviation sensitivity coefficient of the steel structure component, and the set assembly error. The output is the assembly error data of the steel structure component affecting the component. The error prediction model is trained using the training dataset. Based on the error prediction model, the assembly error of the steel structure component affecting the component under different assembly errors is determined.

[0043] Deviation sensitivity coefficients quantify the importance of each component in the overall structure. A high deviation sensitivity coefficient for a component indicates a significant impact of its assembly error on the overall structure. Therefore, the model can prioritize these critical components to more effectively assess and predict overall assembly errors. Incorporating deviation sensitivity coefficients into the model input improves the accuracy of error prediction. By considering the sensitivity of each component, the model can more accurately map the impact of assembly errors on subsequent components, thereby improving the reliability of predictions. Simultaneously, the introduction of deviation sensitivity coefficients allows the model to predict and adjust for potential errors during the design phase, thereby enhancing the stability and safety of the entire structure during use and improving overall performance.

[0044] The error prediction model mentioned above is built based on a specific XGBoost machine learning model; XGBoost is a gradient boosting-based machine learning algorithm widely used in classification, regression, and ranking problems. When initializing a specific XGBoost model, the following important parameters need to be set: learning rate (0.01 to 0.1), maximum depth (3 to 10), subsample ratio (0.5 to 1), regularization parameter (lambda, alpha) (to control model complexity and reduce overfitting), and number of iterations (n_estimators, 100 to 500).

[0045] The model is trained based on the data in the training set. The mean squared error of the test set is calculated using the mean squared error loss function. The mean squared error is used to determine whether the trained model meets the accuracy requirements. If it does not meet the accuracy requirements, the model is retrained.

[0046] The logic behind determining the risk coefficient based on the impact of output on component assembly errors is as follows: For steel structural components with detected assembly errors, their corresponding deviation sensitivity coefficients are retrieved, and the predicted assembly errors affecting these components are analyzed using an error prediction model. Based on the predicted assembly errors affecting the components, the risk coefficient of the overall assembled structure is simulated. The specific formula used to calculate the risk coefficient is as follows: In the formula, For risk coefficient, Let q be the predicted assembly error of the q-th influencing component, where q is the index of the influencing component.

[0047] In steel structure assembly, the assembly error of a certain component often affects the assembly accuracy of subsequent components. By detecting the error of a certain component, the potential assembly errors of subsequent components can be predicted, thus forming an error propagation chain.

[0048] Step 5: Determine whether the assembly conditions are met based on the cumulative risk coefficient. If so, proceed with the assembly of subsequent steel structure components. Otherwise, adjust the position of the steel structure components with assembly errors until the assembly conditions are met, thereby completing the virtual trial assembly of the steel structure.

[0049] The assembly conditions are specifically set as follows: In the formula, This is the cumulative risk threshold. This is the assembly error threshold for the predicted assembly error of each subsequent steel structure component.

[0050] Among them, the assembly error threshold for the predicted assembly error of each subsequent steel structure component. Specifically, it is set as the maximum assembly error of each subsequent steel structure component, and the maximum assembly error of each subsequent steel structure component is determined by the maximum error set in the historical structure. Cumulative risk threshold Specifically, the design is based on the overall design requirements of the assembly structure, combined with expert experience.

[0051] Please see Figure 4 The present invention also provides a BIM-based virtual trial assembly system for steel structures, which is used to execute the above-mentioned BIM-based virtual trial assembly method for steel structures, including: The carbon footprint analysis module is used to analyze and determine the total carbon footprint data of multiple historical assembly processes, select the assembly sequence of steel structure components in the historical assembly process with the smallest total carbon footprint as the target assembly sequence, and cluster the steel structure components based on the target assembly sequence and the influence between each steel structure component to form several sub-component sets. The assembly sequence analysis module is used to construct an assembly association vector diagram based on the formed set of sub-components, and assign a risk value to any steel structure component based on the importance of its function type. Based on the risk value of each steel structure component and its assembly sequence, the module analyzes the deviation sensitivity coefficient of each steel structure component. The deviation impact analysis module is used to construct a BIM model based on the geometric characteristics of each steel structure component, perform virtual trial assembly according to the target assembly sequence using the BIM model, detect the assembly error of each steel structure component by monitoring the data stream, and when an assembly error is detected in a steel structure component, the affected component is identified by searching the assembly association vector graph. The assembly risk analysis module is used to build an error prediction model. It takes the ranking of the component with assembly error in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, and the assembly error as input, and uses the assembly error affecting the component as a label for model training. The risk coefficient is determined based on the output of the assembly error affecting the component. The assembly adjustment and optimization module is used to determine whether the assembly conditions are met based on the risk coefficient. If so, the assembly of subsequent steel structure components is carried out; otherwise, the position of the steel structure components with assembly errors is adjusted until the assembly conditions are met, thereby completing the virtual trial assembly of the steel structure.

[0052] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0053] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0054] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0055] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A steel structure virtual trial assembly method based on a BIM platform, characterized in that, The specific steps include: The total carbon footprint data of multiple historical assembly processes were analyzed and determined. The assembly sequence of steel structure components in the historical assembly process with the smallest total carbon footprint was selected as the target assembly sequence. Based on the target assembly sequence and the influence between each steel structure component, the steel structure components were clustered to form several sub-component sets. Based on the formed set of sub-components, an assembly association vector diagram is constructed, and for any steel structure component, a risk value is assigned based on the importance of the function type. Based on the risk value of each steel structure component and its assembly sequence, the deviation sensitivity coefficient of each steel structure component is analyzed. A BIM model is constructed based on the geometric feature parameters of each steel structure component. Virtual trial assembly is performed using the BIM model according to the target assembly sequence. Assembly errors of each steel structure component are detected by monitoring the data stream. When an assembly error is detected in a steel structure component, the affected component is identified by searching the assembly association vector graph. An error prediction model is constructed, taking the ranking of the component with assembly error in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, and the assembly error as inputs, and using the assembly error affecting the component as a label for model training, and determining the risk coefficient based on the output of the assembly error affecting the component. The assembly conditions are determined based on the risk coefficient. If they are met, the assembly of subsequent steel structure components is carried out. Otherwise, the positions of steel structure components with assembly errors are adjusted until the assembly conditions are met, thereby completing the virtual trial assembly of the steel structure.

2. The virtual trial assembly method of steel structure based on BIM platform according to claim 1, characterized in that: The geometric feature parameters of each steel structure component specifically include the component length, width, height, and outline shape. A BIM model is constructed based on the geometric feature parameters of each steel structure component. Based on the material type and material physical parameters, the constructed BIM model is material-defined to complete the model construction of each steel structure component. The material physical parameters include material mass, density, and hardness. The historical steel structure component assembly sequence refers specifically to the assembly sequence of steel structure components that could previously complete the overall structural assembly, thereby forming several assembly sequence combinations.

3. The method for virtual trial assembly of steel structures based on a BIM platform according to claim 2, characterized in that: The logic behind analyzing and extracting the total carbon footprint of each assembly sequence combination in actual assembly is as follows: obtain the energy consumption for completing the assembly of each steel structure component, the transportation distance for each steel structure component assembly, and the energy consumption generated by the transportation distance. Among them, the energy consumption for completing the assembly of each steel structure component specifically refers to the total power consumption of the assembly operation, including the total power consumption of hoisting and installation operations, and the energy consumption generated by the transportation distance specifically refers to the total carbon emissions generated by transporting the steel structure components. The logic behind calculating the total carbon footprint of each assembly sequence combination is as follows: The total carbon footprint of each assembly sequence combination is calculated based on the energy consumption of completing the assembly of each steel structure component and the energy consumption generated during the transportation of the steel structure components. The specific formula used for this calculation is as follows: In the formula, The total carbon footprint of the z-th assembly sequence combination is... To determine the total power consumption for assembling the steel structure components in the z-th assembly sequence, As the energy conversion factor, Let be the total carbon emissions generated from transporting steel structural components in the z-th assembly sequence combination, where z is the index of the assembly sequence combination.

4. The method for virtual trial assembly of steel structures based on a BIM platform according to claim 3, characterized in that: The specific method for forming several sub-component sets is as follows: based on the connection relationship of each steel structure component and combined with past assembly experience, the influence relationship between each steel structure component is determined, and the components with influence relationships are clustered to form sub-component sets; The assembly association vector graph specifically includes nodes and directed edges. The nodes represent each steel structure component in any set of sub-components, and the directed edges represent the connection order relationship between steel structure components that have an influence relationship. The specific types of functions of each steel structure component include primary load-bearing type, secondary load-bearing type, connection type, and other types. The logic for assigning risk values ​​based on the importance of function type is as follows: the risk values ​​of steel structure components are assigned in the order of primary load-bearing type, secondary load-bearing type, connection type, and other types, with the risk value assigned to primary load-bearing type steel structure components being the highest and the risk value assigned to other types of steel structure components being the lowest. The logic behind analyzing the deviation sensitivity coefficients of each steel structure component is as follows: Based on the ranking of different steel structure components in the target assembly sequence, and combined with the total number of steel structure components, a normalization process is performed. The normalized value is used as the installation risk coefficient for that steel structure component. Based on the installation risk coefficient and the risk assignment for that steel structure component, the deviation sensitivity coefficient of each steel structure component is determined. The specific formula for calculating the installation risk coefficient is as follows: In the formula, Let be the installation risk coefficient for the i-th steel structure component. Let represent the ranking of the i-th steel structure component in the target assembly sequence. This represents the total number of steel structure components. The specific formula used to calculate the deviation sensitivity coefficient of each steel structure component is as follows: In the formula, Let be the deviation sensitivity coefficient of the i-th steel structure member. Assign a risk value to the i-th steel structure component. and These are the weighting coefficients, where and and All are greater than 0.

5. The method for virtual trial assembly of steel structures based on a BIM platform according to claim 4, characterized in that: The logic underlying the analysis of assembly errors of each steel structure component is as follows: By performing a depth-first search along all paths originating from the steel structural member in the assembly association vector graph, all subsequent steel structural members related to the steel structural member are identified as the influencing members of the steel structural member. The specific method for constructing the training error prediction model is as follows: For each steel structure component, several assembly errors are randomly set, and the assembly errors of subsequent steel structure components are determined through simulated assembly. The ranking of the steel structure component in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, the set assembly errors and the assembly error data affecting the components are mapped one by one to form a training dataset. Based on the training dataset, an error prediction model is constructed with the input of the target assembly order ranking, the deviation sensitivity coefficient of the steel structure component, and the set assembly error. The output is the assembly error data of the steel structure component affecting the component. The error prediction model is trained using the training dataset. Based on the error prediction model, the assembly error of the steel structure component affecting the component under different assembly errors is determined.

6. The method for virtual trial assembly of steel structures based on a BIM platform according to claim 5, characterized in that: The logic behind determining the risk coefficient based on the impact of output on component assembly errors is as follows: For steel structural components with detected assembly errors, their corresponding deviation sensitivity coefficients are retrieved, and the predicted assembly errors affecting these components are analyzed using an error prediction model. Based on the predicted assembly errors affecting the components, the risk coefficient of the overall assembled structure is simulated. The specific formula used to calculate the risk coefficient is as follows: In the formula, For risk coefficient, Let q be the predicted assembly error of the q-th influencing component, where q is the index of the influencing component.

7. A method for virtual trial assembly of steel structures based on a BIM platform according to claim 6, characterized in that: The assembly conditions are specifically set as follows: In the formula, This is the cumulative risk threshold. The assembly error threshold is the predicted assembly error for each influencing component.

8. A virtual trial assembly system for steel structures based on a BIM platform, characterized in that: The BIM-based virtual trial assembly system for steel structures is used to execute the BIM-based virtual trial assembly method for steel structures according to any one of claims 1-7, including: The carbon footprint analysis module is used to analyze and determine the total carbon footprint data of multiple historical assembly processes, select the assembly sequence of steel structure components in the historical assembly process with the smallest total carbon footprint as the target assembly sequence, and cluster the steel structure components based on the target assembly sequence and the influence between each steel structure component to form several sub-component sets. The assembly sequence analysis module is used to construct an assembly association vector diagram based on the formed set of sub-components, and assign a risk value to any steel structure component based on the importance of its function type. Based on the risk value of each steel structure component and its assembly sequence, the module analyzes the deviation sensitivity coefficient of each steel structure component. The deviation impact analysis module is used to construct a BIM model based on the geometric characteristics of each steel structure component, perform virtual trial assembly according to the target assembly sequence using the BIM model, detect the assembly error of each steel structure component by monitoring the data stream, and when an assembly error is detected in a steel structure component, the affected component is identified by searching the assembly association vector graph. The assembly risk analysis module is used to build an error prediction model. It takes the ranking of the component with assembly error in the target assembly sequence, the deviation sensitivity coefficient of the steel structure component, and the assembly error as input, and uses the assembly error affecting the component as a label for model training. The risk coefficient is determined based on the output of the assembly error affecting the component. The assembly adjustment and optimization module is used to determine whether the assembly conditions are met based on the risk coefficient. If so, the assembly of subsequent steel structure components is carried out; otherwise, the position of the steel structure components with assembly errors is adjusted until the assembly conditions are met, thereby completing the virtual trial assembly of the steel structure.

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