Building structure intelligent forward design system based on BIM and parameterization technology

Abstract

The invention relates to the technical field of building structure design, in particular to a building structure intelligent forward design system based on BIM and parameterization technologies. According to the technical scheme, a parameter-driven BIM initial model generator is included, a visual parameter input panel is created, a designer can conveniently set key parameters such as the number of layers, the layer height, the span and the function partition of a building, an intelligent algorithm is built in, a building mechanics knowledge base and classic case big data are combined, and a BIM initial model is generated; input parameters are quickly converted into a preliminary BIM building structure model, reasonable column grid arrangement, beam system model selection, model parameter association real-time linkage, modification at one position and automatic adaptation of related components are achieved, and an efficient forward design foundation is laid. The method has the advantages that the design efficiency can be remarkably improved, the cost can be remarkably reduced, the building structure performance can be remarkably improved, the cooperative work quality can be effectively improved, all professions can be matched more timely, and it is guaranteed that high-quality design results are produced.

Description

Technical Field

[0001] The present invention relates to the technical field of building structure design, and in particular to an intelligent forward design system for building structures based on BIM and parametric technology. Background Art

[0002] Detailed structural design is required during the design phase of building products. The current building structure design process has obvious drawbacks. The traditional hand-drawing plus two-dimensional drawing to three-dimensional mode has complicated operation links, poor information flow, time-consuming and laborious design adjustments, and a single move affects the entire system. Each profession works independently, and the connection between architecture, structure, and electromechanical information is disconnected, which frequently leads to design conflicts and changes. When encountering complex shapes and special-shaped structures, conventional design is difficult, and it is difficult to balance creativity and mechanical rationality. Although BIM is involved, it has not deeply integrated the advantages of parametricization and has not formed a mature forward design system, making it difficult to match the current diverse architectural demands and strict engineering standards. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent forward design system for building structures based on BIM and parametric technology, which has significantly improved design efficiency through rapid parameter input and intelligent matching; in the detailed design stage, topology optimization provides real-time feedback, and the overall design cycle is shortened by 40% - 50%, which greatly improves project delivery efficiency and significantly reduces costs. Through topology optimization, redundant structures are accurately eliminated, material usage is reduced by 25% - 35%, and raw material procurement costs are reduced. The structural performance is excellent, and simulation verification ensures the safety of the structure throughout its life cycle, reduces the investment in later maintenance, extends the service life of the building, and coordinates quality upgrades. The cross-professional collaborative cloud platform allows instant and accurate information communication, and the tacit cooperation of various professions can produce high-quality design results.

[0004] The technical solution of the present invention is as follows: The intelligent forward design system of building structure based on BIM and parametric technology is characterized by: 1) Parameter-driven BIM initial model generator: Create a visual parameter input panel, designers can easily set key parameters such as the number of building floors, floor height, span, functional zoning, etc., built-in intelligent algorithm, combined with the architectural mechanics knowledge base and classic case big data, quickly convert the input parameters into a preliminary BIM building structure model, with reasonable column grid layout, beam system selection, model parameter association real-time linkage, one modification, automatic adaptation of related components, laying the foundation for efficient forward design; 2) Structural detail design module based on topology optimization: By introducing the topology optimization algorithm, docking with BIM Model, based on the structural force distribution and load conditions, accurately identify redundant material areas; automatically optimize the cross-sectional shape and connection method of the components, generate lightweight and mechanically excellent structural details, and separately collect and organize key test data points; simultaneously consider architectural aesthetics and construction convenience, and output a design plan that takes into account the demands of multiple parties; the process is visualized to facilitate designers to control the optimization process in real time; 3) Cross-disciplinary collaborative cloud platform: Build a real-time collaborative working environment on the cloud, and structural designers share BIM models with architectural and mechanical and electrical professionals; built-in instant messaging and annotation functions are built-in to facilitate communication of design intent; integrated conflict detection engine, real-time troubleshooting of design "collisions" between different professions, automatically generate coordination strategies, and greatly reduce communication costs and the probability of design rework; 4) Full life cycle performance simulation and verification unit: Combined with structural dynamics, thermodynamics, and durability theory, simulations of earthquake response, wind load, temperature stress, and service life estimation are carried out on the basis of the BIM model; output quantitative evaluation indicators to intuitively reflect the performance of the structure under different working conditions; adjust the design parameters retrospectively based on the simulation results to ensure the safety and stability of the structure throughout its life cycle.

[0005] Furthermore, the effectiveness analysis of the detection data points is specifically carried out as follows: a detection point cloud composed of detection data points is obtained and an original point cloud is obtained by scanner acquisition; feature data points are extracted from the original point cloud and the detection point cloud respectively using the ISS algorithm, the feature data points in the detection point cloud are matched with the feature data points in the original point cloud, the matching number and matching mean of the feature data points in the detection point cloud are collected and correlated and integrated for effectiveness evaluation and analysis.

[0006] Furthermore, when conducting full life cycle performance simulation, the simulation results and key influencing factors in the simulation process are automatically organized centrally, and different comparative optimization solutions can be extracted by changing the key data point information, and key important data points are automatically marked with different display colors.

[0007] Beneficial effects of the present invention: The following beneficial effects can be achieved through the technical solution of the present invention: 1. Design efficiency is significantly improved through rapid parameter input and intelligent matching; in the detailed design stage, topology optimization provides real-time feedback, the overall design cycle is reduced by 40% - 50%, and project delivery efficiency is greatly improved.

[0008] 2. The cost is significantly reduced. Through topology optimization, redundant structures are accurately eliminated, material usage is reduced by 25% - 35%, and the cost of raw material procurement is reduced. 3. Excellent structural performance, simulation verification ensures the safety of the structure throughout its life cycle, reduces subsequent maintenance investment, and extends the service life of the building.

[0009] 4. Collaborative quality upgrade, the cross-professional collaborative cloud platform allows information communication to be immediate and accurate, and each profession cooperates well to produce high-quality design results. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a panel screenshot related to the model information of the present invention; Figure 2 It is a screenshot of the control page when the present invention performs a design system simulation operation; Figure 3 It is a screenshot of the operation page of the modular setting of building information in the design system of the present invention; DETAILED DESCRIPTION

[0011] At the beginning of the project, the designer first uses the parameter panel to build the initial model, and then uses the parameter-driven BIM initial model generator to establish the model. By creating a visual parameter input panel, the designer can easily set key parameters such as the number of floors, floor height, span, functional zoning, and carry out detailed design of different design structures. Through the built-in intelligent algorithm within the system, combined with the architectural mechanics knowledge base and classic case big data, the input parameters are quickly converted into a preliminary BIM building structure model, which comes with a reasonable column grid layout and beam system selection, and realizes the rapid establishment of building information models of different specifications. The model parameters are linked in real time, and the relevant components are automatically adapted after one modification, laying the foundation for efficient forward design and ensuring that all different data in the design system can be adjusted in a timely and accurate manner. For the scale parameter information annotated based on real engineering drawing data, its attribute category is the Y label in the classification task, and its feature design is as follows: For any scale data, the initial features it contains are the first and last coordinates x1, y1, x2 and y2 of the scale segment, as well as its size value v, from which feature information needs to be extracted. First, for any engineering drawing, the ruler parameters can be converted to horizontal or vertical direction by rotating the image at a certain angle, and the horizontal / vertical direction of any parameter attribute category is unique. Based on this conclusion, the ruler can be divided into two categories according to horizontal or vertical, and classification models can be designed for the dimension parameters in the two directions to improve the stability of the model. The dimension parameter direction can be determined according to the difference between x1 and x2, y1 and y2, and the coordinate value of the other dimension is the length information of the dimension parameter. Since the coordinate value of the other dimension is irrelevant to its attribute category for the horizontal / vertical dimension parameters, feature decoupling is performed in the vertical / horizontal direction, thereby converting the four parameters x1, y1, x2 and y2 into two parameters start and end, representing the two end points of the line segment. Secondly, the dimension information of the component view image is obtained, and the values ​​of start and end are normalized. For the horizontal dimension parameter, it is divided by the width of the image; for the vertical dimension parameter, it is divided by the degree of the image. In addition, the length obtained by end-start, as another feature of the classification model, is also normalized. Finally, the numerical parameters of the ruler are used as another feature to assist the prediction of the model. After extracting the above features based on the labeled information of the ruler parameters, the construction of the data set is completed. The data set is divided into a horizontal attribute data set and a vertical attribute data set, and the XGBoos model is trained separately. Finally, the attribute parameter classification model weights in the horizontal and vertical directions are obtained, which can accurately and effectively predict the attributes of the ruler parameters in their directions.

[0012] Structural detail design module based on topological optimization: By introducing the topological optimization algorithm and connecting to the BIM model, the redundant material area can be accurately identified according to the structural force distribution and load conditions; the cross-sectional shape and connection method of the components can be automatically optimized to generate lightweight structural details with excellent mechanical properties, and key detection data points can be separately collected and sorted; architectural aesthetics and construction convenience can be considered simultaneously, and a design scheme that takes into account the demands of multiple parties can be output; the process is visualized to facilitate designers to control the optimization process in real time. Extract the construction geological conditions according to the construction design data of the building project to obtain the construction geological conditions data; perform differential calculation of the building foundation level according to the construction design data of the building project and the three-dimensional model of the building project to obtain the differential data of the building foundation level; perform foundation stability calculation according to the differential data of the building foundation level and the construction geological conditions data to obtain the building foundation stability data. Calculate the building strength of the three-dimensional model of the building project according to the building foundation stability data and the construction design data of the building project to obtain the building strength data. Obtain historical natural disaster data around the building; perform building risk assessment according to historical natural disaster data around the building and the building strength data to obtain building risk assessment data. The building structure design optimization strategy is analyzed on the building structure design data according to the building risk estimation data to obtain the building structure design optimization strategy data.

[0013] Cross-disciplinary collaborative cloud platform: Build a real-time collaborative working environment on the cloud, where structural designers share BIM models with architectural and electromechanical professionals; built-in instant messaging and annotation functions facilitate communication of design intent; integrated conflict detection engine to check for design "collisions" between different disciplines in real time, automatically generate coordination strategies, and significantly reduce communication costs and the probability of design rework. A first engineering design model generation module is used to receive a first engineering design plan and a preliminary quotation plan, and input the first engineering design plan into a building information simulation program to generate a first engineering design model; a first engineering sub-model division module is used to divide the first engineering design model according to a preset business allocation rule, generate a plurality of first engineering sub-models and send them to corresponding design terminals; a construction cost evaluation module is used to receive a plurality of second engineering editing instructions and input them into a building information simulation program to generate a corresponding second engineering sub-model, evaluate the building material cost information, labor cost information and equipment cost information corresponding to the target second engineering sub-model and mark them to the target second engineering sub-model; a quotation data determination module is used to calculate the construction cost data of the target second engineering sub-module based on the building material cost information, labor cost information and equipment cost information, and determine the quotation data of the corresponding construction sub-contract based on the construction cost data of different second engineering sub-modules.

[0014] Full life cycle performance simulation and verification unit: Combined with structural dynamics, thermodynamics, and durability theory, the simulation of earthquake response, wind load, temperature stress, and service life estimation is carried out on the basis of the BIM model; quantitative evaluation indicators are output to intuitively reflect the performance of the structure under different working conditions; the design parameters are adjusted retrospectively according to the simulation results to ensure the safety and stability of the structure throughout its life cycle. The soil mechanics analysis system is used to calculate the foundation settlement. During the calculation process, the load conditions and soil characteristics of each base point are input to analyze the settlement trend in different time periods. The building foundation settlement data is obtained by considering factors such as soil compressibility and groundwater level changes. The foundation stability analysis is carried out based on the horizontal differentiation data of the building foundation and the building foundation settlement data to obtain the building foundation stability data. The foundation stability analysis is carried out using the structural analysis system for comprehensive evaluation, and all relevant data, including soil strength, settlement, and foundation load, are input to analyze the stability of the foundation under different working conditions. The analysis results not only provide the safety factor of the foundation, but also identify potential instability risk areas. Use the geological modeling system to conduct a detailed analysis of the soil layers on site, and import the construction geological conditions data, including the depth, type and physical properties of different soil layers. Then, generate a stratigraphic distribution map to indicate the thickness and distribution of each soil layer. Finally, the generated building foundation stratigraphic distribution data should list the properties of each layer of soil in detail, input the physical parameters of each soil layer, including friction angle, cohesion and elastic modulus, etc., analyze the strength differences between different strata through advanced calculation methods (such as finite element method), and obtain stratigraphic soil strength heterogeneity data. The results should be presented in the form of graphics and tables, clearly indicating the strength characteristics and change trends of each layer of soil, input soil strength heterogeneity data and horizontal differentiation data, and identify potential defective areas. Then, by building a model, analyze the overlap of soil layers and the impact of defects on the foundation. Finally, the generated foundation defect overlap data should describe the characteristics and potential risks of each defective area in detail, and input the design load of the building, including self-weight load, live load and wind load. Then, based on the defect overlap data, analyze the impact of each defective area on the load distribution of the building, and calculate the load data of the building foundation. The results should be presented in a tabular form, clearly listing the load values ​​and action locations of each point. Based on the load data and combined with the soil strength and settlement characteristics of the foundation, an appropriate calculation model (such as the step-by-step loading method) should be used to simulate the settlement. Ultimately, the generated building foundation settlement data should describe in detail the settlement amount and settlement trend at each location. Based on the construction design data of the building project, use the Building Information Modeling (BIM) software to extract the building design structure and building material information. In the specific operation, it is necessary to identify all load-bearing structures (such as walls, columns, and floor slabs) and non-load-bearing structures from the design blueprint, extract the corresponding geometric parameters (such as size and shape) and the building materials used (such as concrete grade, steel type, etc.).According to the building structure data and building material data, the bearing capacity of the building load-bearing wall of the construction project 3D model is calculated to obtain the bearing capacity data of the building load-bearing wall. According to the bearing capacity data of the building load-bearing wall, the building structure data and the building material data, the stability of the connection point is evaluated to obtain the stability data of the building connection point. According to the bearing capacity data of the building load-bearing wall, the stability data of the building foundation and the stability data of the building connection point, the strength of the building is calculated to obtain the strength data of the building. Based on the bearing capacity data of the load-bearing wall, the stability data of the building foundation and the stability data of the connection point, the building strength is calculated using the comprehensive structural analysis system. All relevant data are input, including the bearing capacity of the load-bearing wall, the bearing condition of the foundation and the stability evaluation results of the connection point. By simulating the stress conditions of the building under different working conditions, the strength and deformation of the entire building are calculated.

[0015] The effectiveness analysis of the detection data points is specifically as follows: obtaining a detection point cloud composed of detection data points and obtaining an original point cloud by scanner acquisition; using the ISS algorithm to extract feature data points from the original point cloud and the detection point cloud respectively, matching the feature data points in the detection point cloud with the feature data points in the original point cloud, collecting the matching number and matching mean of the feature data points in the detection point cloud, and performing correlation integration for effectiveness evaluation and analysis. It is necessary to collect historical natural disaster data in the surrounding area of ​​the building. The implementation process includes accessing the database of the national or local natural disaster management agency to obtain disaster records such as earthquakes, floods, and typhoons in the past few decades. The specific operation can use a geographic information system to spatially associate disaster data with the location of the building, analyze the frequency of natural disasters and their potential impact on specific buildings. Combine the strength data of the building body with the historical natural disaster data, analyze the deterioration trend of the building body, establish a physical model of the building body, and collect information such as its material strength, structural design parameters, and usage. Then, statistical analysis methods (such as regression analysis or time series analysis) are used to combine historical disaster data to evaluate the impact of natural disasters on the strength of the building body. For example, the strength change of a building after experiencing multiple earthquakes can be analyzed, its deterioration rate can be calculated, and a degradation trend data graph can be generated to show the strength change of the building in a specific time period to predict future degradation. Based on the degradation trend data and strength data of the building, a comprehensive building risk assessment is conducted. A risk assessment model (such as the analytic hierarchy process or the fuzzy comprehensive evaluation method) is used to comprehensively assess the degradation trend of the building and the risk of natural disasters encountered. By analyzing the degradation trend data, key structural components with a high degree of deterioration are identified, and then combined with the probability of natural disasters, the damage risk under specific circumstances is assessed. For example, a building has shown a high degradation trend in past earthquakes. Combined with the probability of future earthquakes, risk assessment data for the building to suffer serious damage in the next five years can be obtained.

[0016] This design system significantly improves design efficiency when used. The traditional design process often takes several months, with repeated revisions of drawings and coordination of professionals. With the system of the present invention, the initial model building time is shortened by 80%, thanks to the rapid input and intelligent matching of parameters; in the detailed design link, topology optimization provides real-time feedback, the overall design cycle is shortened by 40% - 50%, and the project delivery efficiency is greatly improved. The cost is significantly reduced. After topology optimization, redundant structures are accurately eliminated, the material usage is reduced by 25% - 35%, and the raw material procurement cost is reduced; the frequency of design changes is reduced by more than 70%, saving a lot of rework costs and time costs; the collaborative platform reduces communication friction, and the manpower input cost is reduced by about 20%, with extremely considerable comprehensive economic benefits. The structural performance is excellent, and simulation verification ensures the safety of the structure throughout its life cycle. Earthquake simulation shows that the seismic performance of the structure is improved by 30%, which can better withstand high-intensity earthquakes; under wind load simulation, the stress distribution of components is more uniform, and the deformation is controlled in a very small range; the durability estimate time is extended by 20%, reducing the later maintenance investment and extending the service life of the building. Collaborative quality upgrade, the cross-professional collaborative cloud platform allows information communication to be instant and accurate, the design "collision" detection rate is over 95%, and coordination plans can be generated on the spot to avoid design errors caused by poor communication. All disciplines cooperate well to produce high-quality design results.

[0017] Preferably, construction design data is extracted from the architectural design documents using building information modeling (BIM) software. This includes floor plans, elevations, sections, and material lists. All data should include the functional requirements, dimensions, material properties, and construction details of the building. Ensure that the extracted data complies with local building regulations and standards to provide an accurate basis for subsequent spatial analysis and modeling. Use an algorithm to calculate the total construction area of ​​the building, taking into account the area of ​​all floors and ancillary structures. Then, measure the maximum height of the building to determine the vertical limitations of the building. Finally, the construction area data and building height data obtained from the building project need to be presented in the form of visual charts, and the building space characteristics analysis is performed using 3D modeling software. This analysis includes calculating the volume, surface area, and space utilization of the building, and evaluating the functional layout and flow of people in the building. By recording these spatial characteristics data in a database, a construction space coordinate system is constructed based on the building space characteristics data. Use 3D modeling software (such as CAD or Revit) to set the coordinate origin, X, Y, and Z axes to ensure that they are consistent with the geometric center of the building design. The coordinate system should be able to cover the entire construction area and indicate the main reference points (such as the location of wall corners, columns, and doors). Use 3D modeling software (such as BIM 360 or Navisworks) to construct a 3D model of a construction project. According to the building space feature data, accurately draw the structure and appearance of the building, including elements such as walls, roofs, windows and doors. During the model construction process, ensure that the connection and relative position between the various parts meet the design requirements, and perform real-time collision detection to identify design problems or conflicts. Ultimately, the generated 3D model data of the construction project should include all relevant component information. When performing full life cycle performance simulation, the simulation results and key influencing factors in the simulation process are automatically organized, and different comparative optimization schemes can be extracted by changing the key data point information, and key important data points are automatically marked with different display colors.

[0018] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements or substitutions can be made without departing from the principles of the present invention. These improvements or substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. Intelligent forward design system for building structures based on BIM and parametric technology, characterized by: Including 1) Parameter-driven BIM initial model generator: Create a visual parameter input panel, designers can easily set key parameters such as the number of building floors, floor height, span, functional zoning, etc., with built-in intelligent algorithms, combined with the architectural mechanics knowledge base and classic case big data, quickly convert the input parameters into a preliminary BIM building structure model, with reasonable column grid layout and beam system selection, model parameter association real-time linkage, one modification, automatic adaptation of related components, laying the foundation for efficient forward design; 2) Structural detail design module based on topology optimization: By introducing the topology optimization algorithm, docking with the BIM model, according to the structural force distribution and load conditions, accurately identify the redundant material area; automatically optimize the cross-sectional shape and connection method of the component, generate lightweight and excellent mechanical performance structural details, and collect and organize key detection data points separately; Consider architectural aesthetics and construction convenience simultaneously, and output design solutions that take into account the demands of multiple parties; visualize the process to facilitate designers to control the optimization process in real time; 3) Cross-disciplinary collaborative cloud platform: Build a real-time collaborative work environment on the cloud, and structural designers share BIM models with architectural and mechanical and electrical professionals; built-in instant messaging and annotation functions to facilitate communication of design intent; integrated conflict detection engine to check the design "collision" between different professions in real time, automatically generate coordination strategies, and greatly reduce communication costs and the probability of design rework; 4) Full life cycle performance simulation and verification unit: Combine structural dynamics, thermodynamics, and durability theory to carry out simulations such as earthquake response, wind load, temperature stress, and service life estimation based on the BIM model; Output quantitative evaluation indicators to intuitively reflect the performance of the structure under different working conditions; adjust the design parameters retrospectively based on the simulation results to ensure the safety and stability of the structure throughout its life cycle.

2. The intelligent forward design system for building structures based on BIM and parametric technology according to claim 1, characterized in that: The effectiveness analysis of the detection data points is specifically carried out as follows: a detection point cloud composed of detection data points is obtained and an original point cloud is obtained by scanner acquisition; feature data points are extracted from the original point cloud and the detection point cloud respectively using the ISS algorithm, the feature data points in the detection point cloud are matched with the feature data points in the original point cloud, the matching number and matching mean of the feature data points in the detection point cloud are collected and correlated and integrated for effectiveness evaluation and analysis.

3. The intelligent forward design system for building structures based on BIM and parametric technology according to claim 1, characterized in that: When conducting full life cycle performance simulation, the simulation results and key influencing factors in the simulation process are automatically organized centrally, and different comparative optimization solutions can be extracted by changing the key data point information. Key and important data points are automatically marked with different display colors.

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