Rural House Structure Optimization Design Method and System Based on Building Information Modeling

Abstract

The present application discloses a method and system for optimizing the design of rural house structures based on building information modeling, which relates to the technical field of building structure analysis and design. The method includes: obtaining geographical climate data, building user requirement data, and construction condition data of the rural house to be built; determining a set of house design variables according to the obtained data; randomly generating an initial solution based on the value ranges of the various variable parameters in the house design variables to construct an initial population; using a genetic algorithm to optimize individuals in the initial population, screening individuals according to fitness and performing iterative genetic evolution operations including crossover and mutation, and finally determining the optimal individual; inputting the target value ranges of various variable parameters indicated by the optimal individual into a BIM construction module to determine the target BIM model for the rural house. Thus, the organic integration of genetic algorithm optimization and BIM modeling technology improves the structural safety, economy, construction efficiency, and intelligent level of rural house design.

Description

Technical Field

[0001] This application relates to the technical field of building structure analysis and design, and particularly to a method and system for optimizing the design of rural housing structures based on building information modeling. Background Art

[0002] Due to the complex terrain and landforms in rural areas, many areas are in earthquake-prone regions or areas greatly affected by strong stormy weather. However, during the housing design and construction process, there is a lack of scientific analysis of environmental conditions such as geology and climate, and advanced structural analysis and optimization cannot be carried out, resulting in the safety of the housing structure not being effectively guaranteed when facing natural disasters (such as earthquakes, storms, etc.).

[0003] In addition, the technical levels of rural construction teams vary widely, and there are many difficulties in the management and communication at the construction site. Traditional two-dimensional design drawings (for example, AutoCAD drawings) are difficult to clearly express complex spatial layouts and construction steps, especially the positions and connection relationships of components in three-dimensional space, which are likely to cause misunderstandings and incorrect construction by construction workers, thereby affecting the quality and safety of rural housing.

[0004] In response to the above problems, the industry has not yet proposed a better technical solution. Summary of the Invention

[0005] This application provides a method, system, storage medium, computer program product, and electronic device for optimizing the design of rural housing structures based on building information modeling, so as to at least solve the problem that the quality and safety of rural housing cannot be guaranteed due to insufficient environmental analysis and insufficient design optimization ability in the current related technologies.

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing the design of a rural house structure based on building information modeling, including: obtaining geographical climate data, building user requirement data, and construction condition data of the rural house to be built; the geographical climate data includes topographical and geomorphic information and climate condition information; the building user requirement data includes building area, house layout, and construction budget cost; the construction condition data includes types of available building materials, types of available construction equipment, equipment rental cost, and construction labor unit price; determining a set of house design variables according to the obtained geographical climate data, building user requirement data, and construction condition data; the set of house design variables includes foundation design variables, structural frame design variables, wind and earthquake resistance design variables, and material selection design variables; each house design variable is respectively used to indicate the value range of the corresponding variable parameter; randomly generating a set of initial solutions according to the value ranges of the respective variable parameters indicated by the house design variable ranges to construct an initial population; each individual in the initial population is respectively defined by a corresponding candidate house structure design scheme, and the candidate house structure design scheme includes candidate values of various variable parameters; optimizing the individuals in the initial population by using a genetic algorithm, screening individuals according to the calculation results of the fitness function for iterative genetic evolution operations including crossover and mutation, and finally determining the optimal individual; the fitness function of the genetic algorithm is defined according to the structural safety degree, cost budget matching degree, and construction period corresponding to the candidate house structure design scheme in the individual; inputting the target values of various variable parameters indicated by the optimal individual into a BIM construction module to determine the target BIM model for the rural house.

[0007] Second aspect, an embodiment of the present application provides a rural house structure optimization design system based on building information modeling, including: a data acquisition unit for acquiring geographical climate data, building user requirement data, and construction condition data of a rural house to be built; the geographical climate data includes topographic and geomorphic information and climate condition information; the building user requirement data includes building area, house layout, and construction budget cost; the construction condition data includes types of available building materials, types of available construction equipment, equipment rental cost, and construction labor unit price; a design variable determination unit for determining a set of house design variables according to the acquired geographical climate data, building user requirement data, and construction condition data; the set of house design variables includes foundation design variables, structural frame design variables, wind and earthquake resistance design variables, and material selection design variables; each house design variable is respectively used to indicate the value range of the corresponding variable parameter; a population initialization unit for randomly generating a set of initial solutions according to the value ranges of the respective variable parameters indicated by the house design variable ranges to construct an initial population; each individual in the initial population is respectively defined by a corresponding candidate house structure design solution, and the candidate house structure design solution includes candidate values of various variable parameters; a genetic optimization unit for optimizing the individuals in the initial population by using a genetic algorithm, screening individuals according to the calculation results of a fitness function for iterative genetic evolution operations including crossover and mutation, and finally determining the optimal individual; the fitness function of the genetic algorithm is defined according to the structural safety degree, cost budget matching degree, and construction period corresponding to the candidate house structure design solution in the individual; a BIM modeling unit for inputting the target values of various variable parameters indicated by the optimal individual into a BIM construction module to determine a target BIM model for the rural house.

[0008] Third aspect, there is provided an electronic device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the steps of the method for optimizing the design of the rural house structure based on building information modeling according to any embodiment of the present application.

[0009] Fourth aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, characterized in that when the program is executed by a processor, the steps of the method for optimizing the design of the rural house structure based on building information modeling according to any embodiment of the present application are implemented.

[0010] Fifth aspect, an embodiment of the present application provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of the method for optimizing the design of the rural house structure based on building information modeling according to any embodiment of the present application are implemented.

[0011] A rural house structure optimization design method and system provided by this application can at least produce the following technical effects:

[0012] (1) Based on the complex geographical and climatic conditions in rural areas, fully consider the impacts of natural disasters such as earthquakes and strong winds. In the house design, by obtaining topographical and geomorphic information and climate condition data, a set of design variables suitable for the local environment can be generated, covering foundation design, structural frame design, and wind and earthquake resistance design. Through the iterative optimization of the genetic algorithm, with the structural safety of the house as an important part of the fitness function, the reliability of the house design in terms of structure and the ability to resist natural disasters are ensured.

[0013] (2) Not only consider the structural safety of the building design, but also take building user requirements (such as building area, house layout, and construction budget cost) and construction condition data (such as building materials, equipment, and labor cost) as input variables. Through the iterative optimization of the genetic algorithm, the optimization method combines multiple factors of structural safety, cost budget, and construction period. While meeting the structural safety requirements and construction period requirements, it maximally matches the construction budget, reduces resource waste and unnecessary expenses, and realizes the compatibility of cost control and design optimization. Thus, the system can autonomously adjust design variables according to the changing environment and requirements, making the house design scheme highly flexible and intelligent.

[0014] (3) Generate a detailed three-dimensional BIM model of the optimal design scheme through the BIM (Building Information Model) construction module, replacing the traditional two-dimensional design drawings. The BIM model can more intuitively display the complex spatial layout and component connection relationships of the house, making it easier for the construction team to understand the design scheme and reducing construction misunderstandings and errors caused by unclear drawings. At the same time, the BIM model has the ability of dynamic update and real-time feedback, and can make real-time adjustment and feedback according to the actual situation during the construction process, further ensuring the construction quality of the house.

[0015] Through this technical solution, the organic integration of genetic algorithm optimization and BIM modeling technology significantly improves the structural safety, economy, construction efficiency, and intelligent level of rural house design, significantly enhances the disaster resistance ability of the house, optimizes the construction management and communication process during the rural house construction process, and has extremely high application value. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 Fig. shows a flowchart of an example of a rural house structure optimization design method based on building information modeling according to an embodiment of the present application;

[0018] Figure 2 Fig. shows a flowchart of another example of a rural house structure optimization design method based on building information modeling according to an embodiment of the present application;

[0019] Figure 3 Fig. shows a structural block diagram of an example of a rural house structure optimization design system based on building information modeling according to an embodiment of the present application;

[0020] Figure 4 Fig. is a schematic structural diagram of an embodiment of an electronic device of the present application. Detailed implementation manners

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0022] In the technical solutions of the present application, the processing of the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved complies with the provisions of relevant laws and regulations and does not violate public order and good customs.

[0023] Figure 1 Fig. shows a flowchart of an example of a rural house structure optimization design method based on building information modeling according to an embodiment of the present application.

[0024] Regarding the execution entity of the method in the embodiments of the present application, it can be any controller or processor with computing or processing capabilities. Specifically, it can be implemented by a building design optimization server or a mid-terminal, for example, by running building design optimization function software to perform various operations. Specifically, design optimization is carried out through a genetic algorithm, enabling the housing design scheme to dynamically generate an optimal solution based on multi-dimensional input data (such as terrain, climate, user requirements, construction conditions, etc.). Through iterative evolution, the system can autonomously adjust design variables according to the changing environment and requirements, making the design scheme highly flexible and intelligent. At the same time, the genetic algorithm can select reasonable materials and construction techniques on the premise of ensuring safety and budget matching. Furthermore, a detailed 3D BIM model is generated through BIM 3D modeling, making it easier for the construction team to understand the design scheme.

[0025] In some examples, it can be integrated and configured in an electronic device or terminal in a software, hardware, or software-hardware combination manner, and the types of terminals or electronic devices can be diverse, such as mobile phones, tablets, or desktop computers, etc.

[0026] As Figure 1 shown, in step S110, geographical and climatic data, building user requirement data, and construction condition data of the rural house to be built are obtained.

[0027] Here, the geographical and climatic data includes topographical and geomorphic information and climatic condition information, the building user requirement data includes building area, house layout, and construction budget cost, and the construction condition data includes types of available building materials, types of available construction equipment, equipment rental cost, and construction labor unit price.

[0028] In some embodiments, by using GIS (Geographic Information System) and a meteorological database, detailed topographical and geomorphic information of the location of the rural house is obtained, such as terrain slope, soil type, altitude, etc. Here, the terrain information will be used to design the foundation type and depth of the house. If the site is on a slope, it may be necessary to design a protective slope or an underground drainage system, and the foundation design may need to be deeper and wider to ensure stability. In addition, the slope and elevation difference of the terrain will also affect the layout and orientation selection of the house.

[0029] The climatic condition data includes historical meteorological data, such as annual wind speed, rainfall, temperature, earthquake frequency, etc. Here, the climatic data will be used to optimize the wind resistance design, heat insulation and heat preservation performance, and drainage system design of the house. For example, in areas with frequent strong winds, the house requires additional wind resistance design, and the roof slope and structural stiffness need to be strengthened; in areas with heavy rainfall, the use of roof drainage systems and waterproof materials needs to be considered.

[0030] By detecting user interactions, such as user input information, building user requirement data is obtained, which includes floor area, house layout (such as the number of rooms, functional zoning, etc.), and the available construction budget. In addition, construction condition data can also be determined through user interactions, or by cooperating with local building material suppliers and construction equipment rental companies to obtain the types of available materials (such as bricks, concrete, wood, etc.) and their costs, and combining with the local labor market to determine the equipment rental price and construction labor costs.

[0031] Here, the types of available building materials will directly affect the material selection of the house. For example, in remote areas, high-strength concrete or steel materials may be lacking, so the design needs to consider using brick-concrete structures or wooden structures. In addition, the types of available construction equipment will affect the selection of construction techniques, and the lack of equipment may limit the implementation of complex construction techniques. When designing, a simpler construction design plan needs to be selected. For example, if large lifting equipment is lacking, precast components may need to be selected instead of in-situ casting. In addition, the equipment rental cost and construction labor unit price will be used to evaluate the economy of different design plans in the design stage.

[0032] In step S120, according to the obtained geographical and climatic data, building user requirement data, and construction condition data, a set of house design variables is determined.

[0033] Here, the set of house design variables includes foundation design variables, structural frame design variables, wind and earthquake resistance design variables, and material selection design variables. Each house design variable is used to indicate the value range of the corresponding variable parameter. Exemplarily, by querying the corresponding variable calibration table with the obtained data, the value range of each variable parameter can be obtained.

[0034] In some embodiments, the foundation design variables determine the type, size of the building foundation and its interaction with the foundation, directly affecting the overall stability and settlement resistance of the building. Specifically, the foundation design variables include: foundation depth, foundation width, and foundation type. Exemplarily, the foundation type (such as strip foundation, isolated foundation or pile foundation) and depth range are determined according to the terrain and soil data. For example, the range of foundation depth (such as, 1.5 meters - 3 meters) is determined according to the soil type and groundwater level. In areas with soft soil or high groundwater level, the foundation depth needs to be increased to ensure the stability of the foundation. The foundation width is generally determined according to the building area, building load and foundation bearing capacity. It usually cooperates with the foundation depth to disperse the building load and can be set in the width range of 1 meter to 2 meters. By increasing the foundation width, the load can be dispersed to avoid excessive local settlement. Especially in areas with poor soil bearing capacity, a wider foundation helps to improve the stability of the building. The foundation type can include strip foundation, raft foundation, pile foundation, etc. Different foundation types are suitable for different geological conditions. For example, raft foundation is suitable for soft soil foundation, while pile foundation is suitable for areas with weak bearing capacity and high groundwater level.

[0035] In addition, according to the building layout and budget, the structural type of the building (such as frame structure, brick-concrete structure or light steel structure) and the size range of the main components are determined. The structural frame design variables include the following variable parameters: beam-column section size, wall thickness, and storey height. The beam-column section size can be calculated according to the storey height of the building. The value range for rural houses can generally be set from 200mm×300mm to 500mm×700mm. Higher storeys or heavier load requirements will require larger beam-column sections to enhance the structural stiffness. The value range of the wall thickness can be set in the usual wall thickness range of 200mm to 500mm and can be adjusted according to seismic and insulation requirements. For example, in cold regions, thicker walls can improve the insulation effect, while in earthquake-prone areas, increasing the wall thickness helps to enhance the structural stability. The storey height generally has a value range of 2.6 meters to 3.5 meters and can be floated according to user needs. However, a higher storey height will lead to an increase in the beam-column section size and a corresponding increase in construction costs.

[0036] In addition, by combining meteorological and seismic data, parameters such as wind loads and seismic actions are determined, and corresponding wind and seismic resistance design schemes are generated (such as adding seismic walls, adjusting the spacing of construction columns, etc.). The wind and seismic resistance design variables include: the stiffness of the lateral force resisting system, the number of shear walls, and the steel bar anchorage length. Specifically, according to the seismic zoning and wind speed data, the value range of the structural stiffness can be adjusted (such as by the cross-sectional dimensions of the beams and columns, the stiffness settings of the bracing structures, etc.). The number and layout of shear walls directly affect the lateral stiffness of the building, and can be determined according to the building height, seismic requirements, and wind speed conditions. The reasonable layout of shear walls is crucial under seismic and wind loads. The value range of the variable can be set to 1 - 4 walls or more, and the specific number needs to be combined with parameters such as the number of floors and floor area of the building. The range of the steel bar anchorage length is determined according to the force conditions at the beam-column joints and seismic requirements, and can be set with reference to building codes, such as 150mm to 600mm.

[0037] In addition, the types of candidate building materials are screened from the available building material types according to the material strength grade and material durability coefficient. Exemplarily, the material strength grade is determined according to the design load of the building, seismic requirements, and wind resistance performance requirements. For example, the strength grade of concrete can be set from C25 to C40, and the steel bar strength grade is from HRB335 to HRB500. Higher material strength can improve the overall seismic performance of the building, but at the same time increase the cost. The material durability coefficient is determined according to the local climate conditions (such as humidity, temperature difference, weathering degree, etc.). The material durability is evaluated by indicators such as its corrosion resistance and anti-aging performance, and the value range can be set from 0.5 to 1.0. The higher the value, the stronger the material durability. In humid areas, materials with better corrosion resistance need to be selected.

[0038] By determining the multi-dimensional design variable sets of the foundation, structure, wind and seismic resistance, and materials, multiple constraint conditions and parameter ranges are provided for building design, enabling the design process to flexibly respond to different construction conditions and user requirements while meeting safety and practicality.

[0039] In step S130, a set of initial solutions is randomly generated according to the value ranges of the various variable parameters indicated by the building design variable set to construct an initial population.

[0040] Here, each individual in the initial population is respectively defined by the corresponding candidate building structure design scheme, and the candidate building structure design scheme includes the candidate values of various variable parameters.

[0041] In some embodiments, the Monte Carlo method or other random number generation algorithms can be employed to randomly generate a set of initial solutions within the value ranges of each design variable. Each solution represents a candidate housing structure design scheme, including random values of various variable parameters such as the foundation, structural frame, wind and earthquake resistance measures, and material selection. For example, the number of individuals in the initial population can be set according to computing resources and optimization requirements, and dozens to hundreds of solutions can be selected as the initial population to ensure that the genetic algorithm has sufficient search space.

[0042] For example, in step S120, the approximate value ranges of each design variable parameter have been determined, such as:

[0043] - The foundation depth is from 1.5 meters to 3 meters.

[0044] - The cross-sectional dimensions of the beam and column are from 200mm x 300mm to 500mm x 700mm.

[0045] - The wall thickness is from 200mm to 500mm.

[0046] - The material strength grade is from C25 to C40 (concrete strength).

[0047] According to these value ranges above, several parameter combinations are randomly selected within the parameter space. These combinations represent different housing structure design schemes, which form the individuals in the initial population. For example, an individual may be a design scheme with a foundation depth of 2.0 meters, a wall thickness of 300mm, and a material strength of C30.

[0048] Here, each individual is a combination of design variable parameters. When generating the initial population, the parameter values of each design variable are randomly selected, so as to ensure that the variable combinations of each individual are different, that is, the random combinations of variable parameters should have diversity. In addition, advanced sampling methods such as Latin hypercube sampling can also be used to generate the initial population to ensure that the values of each design variable are evenly distributed in the population, avoid individuals concentrating in certain regions of the variable space, and enhance the diversity of the population.

[0049] In step S140, the genetic algorithm is used to optimize the individuals in the initial population, and the individuals are screened according to the calculation results of the fitness function for iterative genetic evolution operations including crossover and mutation, and finally the optimal individual is determined.

[0050] It should be noted that the genetic algorithm is an optimization algorithm that simulates natural evolution, and its core idea is to continuously optimize the individuals in the population through the evolutionary strategy of "survival of the fittest and elimination of the unfit". Thus, through iterative crossover, mutation, and selection, the optimal solution can gradually be screened out.

[0051] Specifically, starting from the generated initial population, the fitness function of each individual is calculated. Individuals with high fitness are crossed and mutated to generate new individuals. According to fitness, individuals with higher adaptability are selected to enter the next generation. The above steps are iteratively repeated until the convergence condition is met (such as reaching the maximum number of iterations or the fitness reaching a certain threshold), and the optimal individual is determined.

[0052] The crossover operation is one of the core steps of the genetic algorithm. It simulates gene exchange in biological inheritance. By combining the design variables of two "parent" individuals, new individuals are generated, which helps to explore the design space and generate new solutions. The mutation operation simulates gene mutation in biological evolution. By making small random changes to the design variables of an individual, new individuals are generated, which can increase the diversity of the population and prevent the population from falling into a local optimal solution.

[0053] In the embodiment of the present application, the fitness function of the genetic algorithm is defined according to the structural safety, cost budget matching degree, and construction period corresponding to the candidate house structure design scheme in the individual. Furthermore, in each generation of the genetic algorithm, it is first necessary to calculate the fitness value of each individual in the population.

[0054] Specifically, the structural safety can be quickly evaluated through a simplified mechanical model or a preset specification standard. By evaluating the total cost of the design scheme and comparing it with the budget cost, the cost budget matching degree can be obtained. By evaluating the construction complexity of the design scheme, such as construction steps, equipment utilization rate, and labor requirements, the construction period time can be predicted. More details about the fitness function will be elaborated in combination with other examples below.

[0055] In step S150, the target values of various variable parameters indicated by the optimal individual are input into the BIM construction module to determine the target BIM model for rural houses.

[0056] Through the optimization process of the genetic algorithm in step S140, an optimal individual or multiple optimal solutions will ultimately be selected. The various design variable parameters of these optimal solutions gradually approach the optimal individual through genetic operations such as crossover and mutation. The design variable parameters of this optimal individual (such as beam-column sizes, wall thicknesses, material types, etc.) need to be extracted and adapted to the information model in the BIM construction module to ensure that these parameters can be successfully integrated into the BIM model.

[0057] It should be noted that the types of BIM construction modules can be diverse. For example, Autodesk Revit or Tekla Structures can be selected, which can support multi-disciplinary design collaboration and is suitable for the overall design and management of rural houses in complex environments.

[0058] Here, the BIM system generates a three-dimensional building model based on the input geometric parameters (such as beam and column dimensions, storey height, wall thickness, etc.). For rural houses, this model will include the design of all the main load-bearing structures, enclosing structures and building functional areas. In addition to geometric information, the BIM model can also integrate non-geometric attributes such as material information and construction period information obtained from the optimal individuals. For example, each part of the BIM model (such as beams, columns, shear walls, etc.) contains information such as the type, specification and construction time of the materials used, so as to be used in the subsequent construction and maintenance phases. At this time, the BIM system is not only a design tool, but can also run through the entire life cycle of house construction, including the construction, operation and maintenance phases. By introducing the optimized design parameters into the BIM model, it provides data support for subsequent construction management, cost control, material procurement, etc.

[0059] In some examples of the embodiments of the present application, the fitness function is expressed by the following formula:

[0060] Q = w1·S 结构 + w2·B 成本 + w3·S 周期 , Equation (1)

[0061] In the formula, Q represents the fitness of the individual, S 结构 represents the structural safety factor corresponding to the candidate house structure design scheme in the individual, B 成本 represents the cost budget matching degree corresponding to the candidate house structure design scheme in the individual, S 周期 represents the construction period matching degree corresponding to the candidate house structure design scheme in the individual; w1, w2 and w3 are fitness weights.

[0062] Here, the fitness weights can be dynamically adjusted according to the specific requirements of the project. For example, if the project area is located in an earthquake-prone area, the weight of seismic safety can be increased to preferentially select a scheme with stronger seismic resistance. In addition, if the project emphasizes economy, the weight of cost budget matching degree can be increased, so as to promote the algorithm to preferentially select a design scheme with lower cost. Thus, flexible weight settings can adapt to the special needs of different projects and ensure that the optimized design results meet the actual priorities of the project. Through dynamic adjustment, the fitness function can flexibly adjust the optimization direction, making the house design more in line with the specific project business requirements.

[0063] The structural safety factor S 结构 mainly measures the stability and load-bearing capacity of the house under common loads (such as wind load, seismic load, etc.). Exemplarily, a simplified physical model can be used in combination with structural design codes to calculate the lateral force resistance, seismic resistance and structural stiffness of the house structure.

[0064] Specifically, for S结构 The calculation of

[0065] S 结构 = w 风 ·S 风 + w 震 ·S 震 , Equation (2)

[0066]

[0067] F 地 = C 震 ·M 房屋 ·g, Equation (6)

[0068] In the formula, S 风 represents the wind resistance safety degree of the house, S 震 represents the seismic safety degree of the house, w 风 and w 震 are the structure safety degree weights; K 侧 is the lateral stiffness of the house, F 风 is the wind load; C d is the wind pressure coefficient, determined according to the shape of the building and the roughness of the windward surface; A 风 is the windward area of the house; ρ 风 is the air density, V 风 is the designed wind speed of the house; F 地 is the seismic load; C 震 is the seismic coefficient, set according to the design seismic intensity of the area where the house is located; M 房屋 is the weight of the house, and g is the acceleration due to gravity.

[0069] Here, the wind pressure coefficient C d is a parameter reflecting the shape, roughness of the windward surface of the building and the influence of the building surrounding environment on the wind load. It is usually determined according to the geometric shape of the building and the flow of air, and there are relevant reference values in some common building design codes. The example calculation method is as follows:

[0070] C d = C h ·C s , Equation (7)

[0071] In the formula, C h is the building height related coefficient, reflecting the influence of wind pressure change with the building height. C s is the building shape coefficient, reflecting the influence of the shape of the windward surface of the building on the wind load.

[0072] For low-rise buildings, the change of wind pressure along the height is small. For example, for rural houses with 2 or 3 floors, take C h= 1. When the height exceeds 10 meters, C can be taken. h = 1.1 to 1.3. For a general rectangular building, C s = 1.0 to 1.2; for a house with a sloping roof, the shape factor varies with the different inclination angles. For a house with an inclination angle below 30 degrees, C s = 0.8 to 1.0; for a circular or other complex geometric shape building, C s = 1.1 to 1.3.

[0073] Thus, by running the above function module, the structural safety of each candidate design solution can be quickly estimated.

[0074] Cost budget matching degree B 成本 Measures whether the construction cost of the design solution matches the budget. The construction cost mainly includes material cost, equipment rental cost, and labor cost. The material cost is calculated based on the type, strength grade, usage amount, and unit price of the selected materials. The labor cost is calculated based on the types of work and labor required during the construction process. The equipment rental cost is the rental fee for the mechanical equipment used during the construction process.

[0075] Specifically, for the calculation of B 成本 is as follows:

[0076]

[0077] C 总 = C 材料 + C 设备 + C 人工 , Equation (9)

[0078]

[0079] In the formula, C 总 represents the total construction cost, C 预算 represents the construction budget cost, C 材料 , C 设备 and C 人工 respectively represent the construction material cost, construction equipment rental cost, and construction labor cost; V a is the usage amount of the a-th building material, P a is the unit price of the a-th building material, A is the number of types of building materials involved in the candidate house structure design solution in the individual; T j is the usage duration of the j-th equipment, R j is the rental unit price of the j-th equipment, J is the number of types of construction equipment involved in the candidate house structure design solution in the individual; N α is the number of the α-th type of worker, W α is the hourly wage of the α-th type of worker, T αis the construction time of the α-th type of worker, and I is the number of types of work involved in the candidate housing structure design scheme in the individual.

[0080] Thus, by estimating the costs of labor, materials, and machinery during the construction process and comparing them with the budget, the fitness function can quickly evaluate the cost matching degree of the design and achieve efficient cost estimation with low computational resource occupancy.

[0081] The construction period is determined by the complexity of the construction process, the supply time of materials, the usage duration of equipment, and the efficiency of manual operations. The total construction period can be estimated by summing up the time required for each construction task.

[0082] Specifically, for S 周期 The calculation of is as follows:

[0083]

[0084] In the formula, T 基准 represents the set expected construction period of the project, T 施工 represents the construction period calculated according to the candidate housing structure design scheme in the individual, W l is the workload of the l-th construction task, E l is the construction efficiency of the l-th construction task, and q is the total number of construction tasks involved in the candidate housing structure design scheme in the individual.

[0085] Thus, by calculating the workload and efficiency of each construction task, the fitness function can identify the bottleneck processes in the construction process, prompting the genetic algorithm to preferentially select the design with a shorter construction period and strong operability, and avoiding selecting those designs that are excellent in terms of safety or cost but are complex in construction.

[0086] The fitness function provided by the embodiments of the present application covers three key dimensions: structural safety, cost budget matching degree, and construction period. Calculating the structural safety degree based on the wind resistance safety degree and seismic safety degree, the fitness function can ensure the structural stability and disaster resistance performance of the design scheme under natural disasters (such as earthquakes and strong winds). Estimating the total construction cost based on the accurately calculated construction costs of materials, equipment, and labor and comparing it with the budget, the fitness function can evaluate the economy of the scheme and ensure that the design scheme achieves an optimized effect while meeting the budget. By estimating the workload and efficiency of construction tasks, the fitness function can evaluate the construction feasibility and efficiency of the design scheme and ensure that the housing construction is completed on time or in advance.

[0087] Thus, by comprehensively evaluating these key dimensions, the fitness function can comprehensively and accurately screen out design solutions that have excellent performance in terms of safety, cost, and construction time, enabling the house design to achieve the best balance while meeting various requirements and ensuring the high quality and high efficiency of the project.

[0088] In some examples of the embodiments of the present application, the crossover in the iterative genetic evolution operation can adopt dynamic crossover operation and local optimization, and the mutation in the iterative genetic evolution operation can adopt adaptive mutation operation.

[0089] In some embodiments, in the crossover, for the candidate values of various variable parameters in the candidate house structure design solution corresponding to an individual, a dynamic variable weight is introduced, and the crossover operation is performed according to the sensitivity of each variable parameter to the fitness to generate a new individual.

[0090] In the crossover process, the crossover intensity of each variable is determined according to its weight. For variables with larger weights, conservative crossover is adopted, that is, excellent genes are retained as much as possible to reduce the possibility of mutation and large-scale changes; for variables with smaller weights, free crossover is adopted to allow more exploration to increase the diversity of the population.

[0091]

[0092] In the formula, represents the value of the i-th variable parameter in the new individual generated by the crossover operation, G i represents the contribution degree of the i-th variable parameter to the fitness, w i represents the i-th variable parameter, and are respectively the values of the i-th variable parameter corresponding to the first parent individual and the second parent individual selected from the t-th generation, and ΔQ is the change amount of the fitness value; is the given change range of the i-th variable parameter, representing the small adjustment amount used for sensitivity analysis; Q(x1, x2,..., x i ,..., x n ) represents the individual fitness value without sensitive fine-tuning, represents the individual fitness value after a small change i occurs in the i-th variable parameter x .

[0093] By adopting the above-mentioned crossover operation, for different design variables (such as beam-column sections, shear wall layouts, material selections, etc.), weights are dynamically assigned according to their sensitivities to fitness. Variables with high sensitivities will be conservatively processed during the crossover process, while variables with low sensitivities are allowed more freedom to vary. This ensures that while the crossover operation retains excellent genes, it enhances the global exploration ability. Especially in the design of rural house structures, key design parameters (such as seismic resistance and wind resistance) can be stably inherited, while non-critical parameters (such as material selection and construction procedures) can be explored diversely through crossover. Additionally, by dynamically adjusting the weights, variables with high sensitivities are less affected by large-scale changes, which effectively avoids the destruction of excellent features during the crossover process, reduces the workload of subsequent corrections, and significantly accelerates the convergence speed.

[0094] For the newly generated individuals, within a local range according to the search radius, search for a better combination of design variables to generate crossover individuals through local area optimization.

[0095] Specifically, for the generated new individuals, make fine-tuning in a local area (such as material cost or construction procedures), similar to the "local hill climbing method". By setting a search radius, search for a better combination of design variables within a local range to improve the fitness of the individuals.

[0096]

[0097] In the formula, It represents the adjustment amount for local optimization; r 搜索 is the search radius during local optimization, used to control the amplitude of the optimization adjustment.

[0098] By adopting the above-mentioned local adjustment of the better solution, after the crossover operation generates new individuals, use local optimization to further fine-tune the design variables in the new individuals, especially those secondary parameters that have little impact on fitness. Through local search (limited by the search radius), find a better solution in a local area of the design space. For the design of rural houses, local optimization can make adjustments in aspects such as material selection and construction details to further optimize costs and construction time.

[0099] Especially, local optimization keeps the changes of individuals within a reasonable range, and will not cause excessive changes due to crossover or mutation, thus maintaining the stability of the optimization and avoiding unnecessary large jumps in design. Through local optimization of specific design areas, the crossover individuals can be further enhanced on the basis of the original good fitness, improving the local fitness.

[0100] During mutation, the mutation rate of the mutation operation is dynamically adjusted according to the change speed of the population fitness.

[0101] The adaptive mutation rate can be dynamically adjusted by monitoring the iterative process of the algorithm according to the change in fitness. The mutation rate changes with the change in population fitness. When the population fitness converges slowly, the mutation rate is increased to increase population diversity; when the fitness improves rapidly, the mutation rate is decreased to maintain good genes.

[0102]

[0103] Where μ t+1 represents the mutation rate of the (t + 1)-th generation, μ t represents the mutation rate of the t-th generation, ΔG 平均 represents the difference in average fitness between the t-th generation and the (t - 1)-th generation, and G 平均 represents the average fitness of the t-th generation.

[0104]

[0105] Where represents the value of the i-th variable parameter in the individual of the (t + 1)-th generation generated by the mutation operation; rand(-r 变异 , r 变异 ) represents a random perturbation within the range from -r 变异 to r 变异 , and r 变异 represents the maximum mutation amplitude.

[0106] Through the embodiments of the present application, by monitoring the change in population fitness, the mutation rate is adaptively adjusted to ensure that the mutation rate is relatively large in the early exploration stage to guarantee population diversity; when converging in the later stage, the mutation rate gradually decreases to reduce the damage to excellent individuals. For the design of rural housing structures, based on the dynamic mutation mechanism, different design schemes can be explored through diversity in the initial stage, and finally converge stably to the optimal scheme in the later stage.

[0107] In addition, for complex combinations of design variables (such as material selection, seismic design, wind load consideration, etc.), through the mutation operation, some design variables can be randomly changed to further expand the search space and prevent the optimization from falling into a local optimum. Adaptive mutation can flexibly respond to changes in design, especially when design requirements are dynamically adjusted, such as changing the budget, adjusting the seismic standard, etc. The dynamic adjustment of the mutation rate can appropriately increase the mutation range to quickly find a design solution that meets the new requirements.

[0108] Figure 2 FIG. shows a flowchart of another example of the rural housing structure optimization design method based on building information modeling according to the embodiments of the present application.

[0109] As Figure 2As shown, in step S210, the target values of various variable parameters indicated by the optimal individual are input into the BIM construction module to determine the target BIM model for rural houses.

[0110] Regarding the details of step S210, reference can be made to the description of the operations in Figure 1 and will not be elaborated here.

[0111] In step S220, finite element analysis is performed on the target BIM model to obtain the stress distribution information, maximum displacement, and inter-story displacement angle of the house load-bearing components in the target BIM model under wind load and seismic load.

[0112] In some embodiments, in order to perform finite element analysis, the target BIM model can be imported into finite element analysis software, which can be based on ANSYS software, and then detailed mechanical analysis of the load-bearing components of the house can be carried out.

[0113] For mechanical calculations, the target BIM model needs to be divided into finite element meshes. Mesh generation is the basis of finite element analysis, and the quality and density of the meshes will directly affect the calculation accuracy. Regarding the selection of mesh density, in the key areas of the load-bearing components (such as beams, columns, shear walls, etc.), the meshes should be denser to capture local stress concentrations and displacement changes; while in non-critical areas (such as large-area plates, the middle part of the walls), relatively coarser meshes can be used to reduce the amount of calculation.

[0114] Here, the boundary conditions of the finite element analysis are defined according to the foundation fixing conditions, soil properties, and building structure support relationships.

[0115] Regarding the description of the foundation fixing conditions, if the foundation is a solid concrete foundation, the foundation part can be set as fixed boundary conditions, that is, it is assumed that the foundation of the house cannot undergo translation or rotation. For soft soil foundations, the settlement or slip of the foundation needs to be considered, and elastic supports or spring supports are defined to simulate the flexible characteristics of the foundation.

[0116] For soil properties, the interaction between the soil and the foundation will affect the force situation of the house. The interaction between the soil and the house foundation can be simulated by adding spring elements or contact elements, and the soil stiffness depends on the actual geological exploration data.

[0117] For the building structure support relationships, it can express the support relationships of components such as beams, columns, and shear walls inside the house. For example, the connection method between the beam and the column can be set as hinged or rigid, and the connection method between the wall and the foundation can be fixed or sliding, etc. These connection conditions are crucial for the force analysis of the structure.

[0118] To perform seismic and wind resistance FEA (Finite Element Analysis) on the building structure, it is necessary to define the specific action modes of wind loads and seismic loads. More specifically, the corresponding wind loads and seismic loads can be calculated through user input information or physical models. For details on calculating wind loads and seismic loads through physical models, reference can be made to the description in combination with Equations (4) and (6) in the above text, which will not be elaborated here.

[0119] By applying wind loads and seismic loads and combining with boundary conditions, finite element analysis is carried out to obtain the response behavior of the building structure under different external forces, including stress distribution information, maximum displacement, and inter-story drift ratio. Stress distribution information refers to analyzing the stress distribution of the load-bearing components (such as beams, columns, shear walls, etc.) of the building under wind loads and seismic loads, finding stress concentration areas, and evaluating whether structural failure will occur. The maximum displacement represents the displacement situation of the building under wind loads and seismic loads, especially the maximum displacement at the top of the building. The inter-story drift ratio is an important indicator to measure the seismic performance of the building structure, indicating the relative horizontal displacement between each floor of the building under earthquake action.

[0120] In step S230, according to the stress distribution information, maximum displacement, and inter-story drift ratio, verify whether the building structure indicated by the target BIM model meets the seismic and wind resistance safety requirements.

[0121] In some embodiments, by checking the stress distribution of the load-bearing components, ensure that the stress levels of each component are within the bearing capacity range of the material. By comparing the maximum displacement obtained from the simulation with the displacement limit specified in the code, evaluate the stability of the building. The magnitude of the inter-story drift ratio directly affects the seismic performance of the building. By comparing it with the inter-story drift ratio specified in the code, evaluate the stability of the building.

[0122] Through the embodiments of the present application, finite element analysis is adopted to simulate the actual deformation and stress response of the building under real wind loads and seismic loads, providing dynamic simulation verification, enabling direct observation of the stress conditions of the building under various load conditions, and ensuring the safety of the building design in actual use. By analyzing the stress distribution information, maximum displacement, and inter-story drift ratio of the building structure, it can be accurately determined whether the building can meet the seismic and wind resistance safety requirements. Based on the verification method of finite element analysis, potential design defects and weak links can be discovered.

[0123] Through the stress distribution information analyzed by FEA, it is easy to identify potential stress concentration areas in the building structure (such as beam-column joints, the bottom of shear walls, etc.), visualize the weak points in the building design, and perform design optimization or strengthening treatment in advance to avoid structural failure. Through FEA analysis, the true deformation of the building under natural disasters such as storms or earthquakes can be simulated, the maximum displacement and inter-story displacement angle can be calculated, and compared with the allowable values in the seismic code, so as to optimize the design in a timely manner.

[0124] In some examples of the embodiments of the present application, regarding the details of the above step S220, calculate the shear stress of each building load-bearing member, and obtain the stress distribution information based on the position of each building load-bearing member and the corresponding shear stress.

[0125] Specifically, the formula for calculating the shear stress of a building load-bearing member is:

[0126]

[0127] In the formula, σ u is the shear stress of the u-th building load-bearing member, which is used to measure the internal mechanical response of the member under external loads; F 外 is the external load determined according to the resultant force of wind load and seismic load, f u is the shear force of the u-th building load-bearing member, b u is the cross-sectional area of the u-th building load-bearing member; e u (t) is the relationship function of the elastic modulus of the u-th building load-bearing member with respect to time t, which is pre-calibrated according to the degradation test data of various building material types.

[0128] Here, combining the external load F 外 and the member cross-sectional characteristics b u together, directly calculate the stress distribution of each load-bearing member under different loads, effectively integrating different types of loads. Based on accurate stress calculations, it helps engineers better understand the mechanical behavior of the building under various load combinations (such as the simultaneous action of wind and earthquake), and can avoid blindly designing oversized member cross-sections and unnecessary material waste. For example, if the stress distribution of a certain load-bearing member is reasonable and within the safety range, there is no need to use materials with too high strength. Thus, on the premise of ensuring that the overall safety and local bearing capacity of the building meet the design requirements, materials with good economy can be reasonably selected, avoiding the selection of materials with too high strength and high cost, thereby reducing the total construction cost of the project.

[0129] In addition, in engineering practice, building materials (such as concrete, steel, etc.) will degrade over time, resulting in a decrease in their mechanical properties, especially the elastic modulus e uA decrease in (t) will affect the load-bearing capacity of the structure. By introducing a time-varying elastic modulus e u (t) in the function, it allows designers to fully consider the impact of material degradation on the structural safety during stress calculations. Thus, by regularly updating the values of the material elastic modulus, the stress distribution of the house at different service life can be dynamically evaluated. For example, as the elastic modulus decreases due to material aging, the stress of the load-bearing members will gradually increase, which can help to evaluate the safety of the future house at different time periods in advance and ensure the long-term structural safety of rural houses.

[0130] On the other hand, a finite element analysis is performed on the target BIM model to obtain the overall stiffness matrix of the house structure. According to the overall stiffness matrix and the external loads, the displacements of each house load-bearing member are calculated. Based on the maximum value among the displacements of each house load-bearing member, the maximum displacement is determined. According to the displacements of each house load-bearing member, the displacement distribution information of each floor is fitted to determine the inter-story drift angle of each floor.

[0131] Specifically, the calculation of the overall stiffness matrix of the house structure is as follows:

[0132] K y =∫ V L T ·D·LdV, Equation (22)

[0133]

[0134] In the formula, K y represents the local stiffness matrix of the yth finite element; L is the derivative matrix of the shape function, which is used to describe the relationship between the displacement field of the element and the nodal displacements; D is the elastic matrix of the material corresponding to the finite element, and V is the element volume of the finite element; Z y is the assembly matrix, which is used to map the stiffness matrix K y in the local coordinate system of the element to the global coordinate system; K 总 represents the overall stiffness matrix of the house structure, and Y represents the total number of finite elements in the target BIM model.

[0135] It should be noted that the assembly matrix is responsible for relating the stiffness matrix in the local coordinate system of the element to the degrees of freedom corresponding to the global coordinate system of the structure. The nodal degrees of freedom involved in the stiffness matrix of each element will occupy specific positions in the degrees of freedom matrix of the entire structure. The assembly matrix assembles the stiffness matrices of all elements into an overall stiffness matrix by mapping the degrees of freedom of the nodes.

[0136] The calculation of the displacement of the house load-bearing member is as follows:

[0137] [K 总 节点 ·δ​节点 = F 节点 , Equation (24)

[0138] where [K 总 节点 represents the nodal stiffness matrix of the building's load-bearing components, δ 节点 represents the nodal displacement of the building's load-bearing components under the action of loads, and F 节点 represents the nodal force vector generated by the external load acting on the nodes of the building's load-bearing components.

[0139] By assembling and performing refined calculations on the stiffness matrix, the deformation behavior of the building under wind load or seismic load can be simulated more accurately. Especially when dealing with complex building structures, it can accurately evaluate the displacement of each node and further calculate the maximum displacement of the entire structure, thereby improving the accuracy of building structure analysis. Precise displacement calculation can effectively avoid excessive deformation of the structure when it exceeds the load-bearing capacity, enhance the safety and reliability of the design, and also avoid over-design and save materials.

[0140] The calculation of the inter-story drift ratio is as follows:

[0141]

[0142] where θ s is the inter-story drift ratio of the s-th floor, H s is the floor height of the s-th floor; Δh s is the relative horizontal displacement of the s-th floor, representing the displacement difference between the s-th floor and the (s - 1)-th floor.

[0143] Here, by fitting the relative horizontal displacement Δh s of each floor with the floor height H s , a direct and effective way is provided to evaluate the seismic and wind resistance performance of the building, and it can accurately evaluate the inter-story drift ratio θ s of each floor of the building under seismic load to ensure that it is within the allowable range of the specification. If the inter-story drift ratio exceeds the design specification, the designer can adjust the member size, material selection, or structural system in a timely manner based on these data to improve the seismic performance.

[0144] Through the embodiments of the present application, a local stiffness matrix K y is constructed using finite element analysis and combined into an overall stiffness matrix K 总 ​, Calculate the displacements of each load-bearing component of the house and fit the inter-story displacement angle, which can quickly simulate the deformation response of the house under different loads during the design stage and perform rapid iterative optimization on the design. By introducing finite element analysis, when it is found that the displacement or inter-story displacement angle of a certain part of the structure exceeds the specification, it can be immediately adjusted in the model, and the new design can be verified by simulation to see if it meets the specification requirements, thus supporting the rapid iteration in the design process. This enables designers to obtain calculation results in a timely manner by dynamically adjusting parameters such as materials, cross-sections, and loads, thereby optimizing the structural design, greatly shortening the time of traditional manual analysis, and improving the design efficiency.

[0145] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of actions combined. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be in other sequences or carried out simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application. In the above embodiments, each embodiment is described with emphasis. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0146] Figure 3 The structural block diagram of an example of a rural house structure optimization design system based on building information modeling according to an embodiment of the present application is shown.

[0147] As Figure 3 shown, the rural house structure optimization design system 300 based on building information modeling includes a data acquisition unit 310, a design variable determination unit 320, a population initialization unit 330, a genetic optimization unit 340, and a BIM modeling unit 350.

[0148] The data acquisition unit 310 is used to acquire the geographical and climatic data, building user requirement data, and construction condition data of the rural house to be built; the geographical and climatic data includes topographical and geomorphic information and climatic condition information; the building user requirement data includes building area, house layout, and construction budget cost; the construction condition data includes the types of available building materials, the types of available construction equipment, equipment rental costs, and construction labor unit prices.

[0149] The design variable determination unit 320 is used to determine a set of house design variables according to the acquired geographical and climatic data, building user requirement data, and construction condition data; the set of house design variables includes foundation design variables, structural frame design variables, wind and earthquake resistance design variables, and material selection design variables; each house design variable is used to indicate the value range of the corresponding variable parameter.

[0150] The population initialization unit 330 is used to randomly generate a set of initial solutions according to the value ranges of various variable parameters indicated by the housing design variable range, so as to construct an initial population; each individual in the initial population is respectively defined by a corresponding candidate housing structure design scheme, and the candidate housing structure design scheme includes the candidate values of various variable parameters.

[0151] The genetic optimization unit 340 is used to optimize the individuals in the initial population by using the genetic algorithm, and screen individuals according to the calculation results of the fitness function for iterative genetic evolution operations including crossover and mutation, and finally determine the optimal individual; the fitness function of the genetic algorithm is defined according to the structural safety, cost budget matching degree and construction period corresponding to the candidate housing structure design scheme in the individual.

[0152] The BIM modeling unit 350 is used to input the target values of various variable parameters indicated by the optimal individual into the BIM construction module to determine the target BIM model for the rural house.

[0153] In some embodiments, the present application provides a non-volatile computer-readable storage medium, in which one or more programs including execution instructions are stored, and the execution instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) to be used to execute the steps of any one of the above-mentioned rural house structure optimization design methods based on building information modeling of the present application.

[0154] In some embodiments, the present application also provides a computer program product, the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is enabled to execute the steps of any one of the above-mentioned rural house structure optimization design methods based on building information modeling.

[0155] In some embodiments, the present application also provides an electronic device, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the rural house structure optimization design method based on building information modeling.

[0156] Figure 4 It is a schematic hardware structure diagram of an electronic device for executing the rural house structure optimization design method based on building information modeling provided by another embodiment of the present application. As Figure 4 shown, the device includes:

[0157] One or more processors 410 and a memory 420, Figure 4 Taking one processor 410 as an example.

[0158] The device for executing the optimized design method of rural house structure based on building information modeling may further include: an input device 430 and an output device 440.

[0159] The processor 410, the memory 420, the input device 430 and the output device 440 may be connected through a bus or other means, Figure 4 Taking connection through a bus as an example.

[0160] The memory 420, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs and modules, such as the program instructions / modules corresponding to the optimized design method of rural house structure based on building information modeling in the embodiments of the present application. The processor 410 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 420, that is, implements the optimized design method of rural house structure based on building information modeling in the above method embodiments.

[0161] The memory 420 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 420 may include a high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 420 may optionally include a memory remotely set relative to the processor 410, and these remote memories can be connected to the electronic device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0162] The input device 430 can receive input digital or character information, and generate signals related to the user settings and function control of the electronic device. The output device 440 may include a display device such as a display screen.

[0163] The one or more modules are stored in the memory 420, and when executed by the one or more processors 410, execute the optimized design method of rural house structure based on building information modeling in any of the above method embodiments.

[0164] The above product can execute the method provided by the embodiments of the present application, and has the corresponding functional modules and beneficial effects of the executed method. For technical details not described in detail in this embodiment, reference can be made to the method provided by the embodiments of the present application.

[0165] The electronic devices in the embodiments of this application exist in various forms, including but not limited to:

[0166] (1) Mobile communication devices: These devices are characterized by having mobile communication functions and mainly aim to provide voice and data communication. Such terminals include: smart phones, multimedia phones, functional phones, and low-end phones, etc.

[0167] (2) Ultra-mobile personal computer devices: These devices belong to the category of personal computers, have computing and processing functions, and generally also have the characteristic of mobile Internet access. Such terminals include: PDA, MID, and UMPC devices, etc.

[0168] (3) Portable entertainment devices: These devices can display and play multimedia content. Such devices include: audio and video players, handheld game consoles, e-books, and intelligent toys and portable vehicle navigation devices.

[0169] (4) Other airborne electronic devices with data interaction functions, such as in-vehicle device installed on vehicles.

[0170] The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0171] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0172] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of this application.

Claims

1. A rural house structure optimization design method based on building information modeling, comprising: Obtaining geographical climate data, building user requirement data, and construction condition data of the rural house to be built; The geographical climate data includes topographical and geomorphic information and climate condition information; the building user requirement data includes building area, house layout, and construction budget cost; the construction condition data includes types of available building materials, types of available construction equipment, equipment rental cost, and construction labor unit price; Determining a set of house design variables according to the obtained geographical climate data, building user requirement data, and construction condition data; the set of house design variables includes foundation design variables, structural frame design variables, wind and earthquake resistance design variables, and material selection design variables; each house design variable is respectively used to indicate the value range of the corresponding variable parameter; Randomly generating a set of initial solutions according to the value ranges of the various variable parameters indicated by the set of house design variables to construct an initial population; each individual in the initial population is respectively defined by a corresponding candidate house structure design solution, and the candidate house structure design solution includes candidate values of various variable parameters; Optimizing the individuals in the initial population by using a genetic algorithm, screening individuals according to the calculation results of the fitness function for iterative genetic evolution operations including crossover and mutation, and finally determining the optimal individual; the fitness function of the genetic algorithm is defined according to the structural safety factor, cost budget matching degree, and construction period corresponding to the candidate house structure design solution in the individual; Inputting the target values of the various variable parameters indicated by the optimal individual into a BIM construction module to determine the target BIM model for the rural house; Performing finite element analysis on the target BIM model to obtain the stress distribution information, maximum displacement, and inter-story displacement angle of the house load-bearing members in the target BIM model under the action of wind load and seismic load; The boundary conditions of the finite element analysis are defined according to the foundation fixing conditions, soil properties, and building structure support relationships; Verifying whether the house structure indicated by the target BIM model meets the seismic and wind resistance safety requirements according to the stress distribution information, maximum displacement, and inter-story displacement angle; Wherein, the fitness function is expressed by the following formula: Q = w1·S 结构 + w2·B 成本 + w3·S 周期 , Wherein, Q represents the fitness of an individual, S 结构 represents the structural safety degree corresponding to the candidate housing structure design scheme in the individual, B 成本 represents the cost budget matching degree corresponding to the candidate housing structure design scheme in the individual, S 周期 represents the construction period matching degree corresponding to the candidate housing structure design scheme in the individual; w1, w2, and w3 are fitness weights; Among them, for S 结构 the calculation is as follows: S 结构 = w 风 · S 风 + w 震 · S 震 , F 地 = C 震 · M 房屋 · g, Wherein, S 风 represents the wind resistance safety factor of the building, and S 震 represents the seismic safety factor of the building. w 风 and w 震 are the structure safety factor weights; K 侧 is the lateral stiffness of the building, F 风 is the wind load; C d is the wind pressure coefficient, which is determined based on the shape of the building and the roughness of the windward surface; A 风 is the windward area of the building; ρ 风 is the air density, V 风 is the designed wind speed of the building; F 地 is the seismic load; C 震 is the seismic coefficient, which is set according to the design seismic intensity of the area where the building is located; M 房屋 is the weight of the building, and g is the acceleration due to gravity; Among them, for B 成本 The calculation is as follows: C 总 = C 材料 + C 设备 + C 人工 , Wherein, C 总 represents the total construction cost, C 预算 represents the construction budget cost, C 材料 , C 设备 and C 人工 respectively represent the construction material cost, the construction equipment rental cost, and the construction labor cost; V a is the usage amount of the a-th building material, P a is the unit price of the a-th building material, and A is the number of types of building materials involved in the candidate housing structure design scheme of the individual; T j is the usage duration of the j-th equipment, R j is the rental unit price of the j-th equipment, and J is the number of types of construction equipment involved in the candidate housing structure design scheme of the individual; N α is the number of the α-th type of workers, W α is the hourly wage of the α-th type of workers, T α is the construction time of the α-th type of workers, and I is the number of types of work involved in the candidate housing structure design scheme of the individual; Among them, for S 周期 the calculation is as follows: Where, T 基准 represents the expected construction period of the set project, and T 施工 represents the construction period calculated according to the candidate housing structure design plan in the individual. W l is the workload of the l-th construction task, and E l is the construction efficiency of the l-th construction task, and q is the total number of construction tasks involved in the candidate housing structure design plan in the individual; Wherein, performing finite element analysis on the target BIM model to obtain the stress distribution information, maximum displacement, and inter-story displacement angle of the house load-bearing members in the target BIM model under the action of wind load and seismic load includes: Calculating the shear stress of each house load-bearing member, and obtaining the stress distribution information according to the positions of the various house load-bearing members and the corresponding shear stresses; Wherein, the formula for calculating the shear stress of the house load-bearing member is: Where, σ u is the shear stress of the u-th housing load-bearing member, which is used to measure the internal mechanical response of the member under external loads; F 外 is the external load determined according to the resultant force of wind load and seismic load, f u is the shear force of the u-th housing load-bearing member, b u is the cross-sectional area of the u-th housing load-bearing member; e u (t) is the relationship function of the elastic modulus of the u-th housing load-bearing member with respect to time t, which is pre-calibrated based on the degradation test data of various building material types.

2. The method according to claim 1, wherein The foundation design variables include: foundation depth, foundation width, and foundation type; the structural frame design variables include the following variable parameters: beam-column section size, wall thickness, and storey height; the wind and earthquake resistance design variables include: lateral force resistance system stiffness, number of shear walls, and steel bar anchorage length; the material selection design variables include multiple candidate building material types, which are screened from the available building material types according to the material strength grade and material durability coefficient.

3. The method according to claim 1, wherein, The topographic and geomorphic information includes terrain slope, soil type, and groundwater level; the climate condition information includes wind speed, rainfall, and temperature.

4. The method according to claim 1, wherein Screening individuals according to the calculation result of the fitness function and performing iterative genetic evolution operations including crossover and mutation, including: In the crossover, for the candidate values of various variable parameters in the candidate house structure design scheme corresponding to an individual, introduce dynamic variable weights, and perform crossover operations based on the sensitivity of each variable parameter to the fitness to generate a new individual: Wherein, represents the value of the i-th variable parameter in the new individual generated by the crossover operation, G i represents the contribution degree of the i-th type of variable parameter to the fitness, w i represents the i-th type of variable parameter, and are respectively the values of the i-th type of variable parameter corresponding to the first parent individual and the second parent individual selected from the t-th generation, and △Q is the change amount of the fitness value; is the change range of the given i-th type of variable parameter, representing the small adjustment amount for sensitivity analysis; Q(x1, x2, …, x i , …, x n ) represents the individual fitness value without sensitive fine-tuning, represents the individual fitness value after a small change i occurs in the i-th variable parameter x ; For the generated new individual, search for a better combination of design variables within a local range according to the search radius to generate a crossover individual by means of local area optimization: In the formula, which represents the adjustment amount for local optimization; r 搜索 is the search radius during local optimization and is used to control the amplitude of the optimization adjustment; In the mutation, dynamically adjust the mutation rate of the mutation operation according to the change speed of the population fitness: where μ t+1 represents the mutation rate of the (t + 1)-th generation, μ t represents the mutation rate of the t-th generation, △G 平均 represents the average fitness difference between the t-th generation and the (t - 1)-th generation, G 平均 represents the average fitness of the t-th generation; In the formula, represents the value of the i-th variable parameter in the (t + 1)-th generation of individuals generated by the mutation operation; rand(-r 变异 , r 变异 ) represents a random perturbation amount within the range from -r 变异 to r 变异 , and r 变异 represents the maximum mutation amplitude.

5. The method according to claim 1, wherein Performing finite element analysis on the target BIM model to obtain the stress distribution information, maximum displacement, and inter-storey displacement angle of the house load-bearing members in the target BIM model under wind load and earthquake load, including: Performing finite element analysis on the target BIM model to obtain the overall stiffness matrix of the house structure; Calculating the displacements of each house load-bearing member according to the overall stiffness matrix and external loads; Determining the maximum displacement according to the maximum value among the displacements of each house load-bearing member; Fitting the displacement distribution information of each floor according to the displacements of each house load-bearing member to determine the inter-storey displacement angle of each floor; Among them, the calculation of the overall stiffness matrix of the house structure is: K y = ∫ V L T · D · L dV, where K y represents the local stiffness matrix of the y-th finite element; L is the derivative matrix of the shape function, which is used to describe the relationship between the displacement field of the element and the nodal displacements; D is the elastic matrix of the material corresponding to the finite element, V is the element volume of the finite element; Z y is the assembly matrix, which is used to map the stiffness matrix K y in the local coordinate system of the element to the global coordinate system; K 总 represents the global stiffness matrix of the building structure, and Y represents the total number of finite elements in the target BIM model; The calculation of the displacement of the house load-bearing member is: [K 总 节点 ·δ 节点 =F 节点 ,​ where, [K 总 节点 represents the nodal stiffness matrix of the building's load-bearing members, and δ 节点 represents the nodal displacement generated by the building's load-bearing members under the action of loads, and F 节点 represents the vector of nodal forces generated by external loads acting on the nodes of the building's load-bearing members;​ The calculation of the inter-storey displacement angle is: where θ s is the inter-story drift angle of the s-th floor, and H s is the floor height of the s-th floor; △h s is the relative horizontal displacement of the s-th floor, representing the displacement difference between the s-th floor and the (s - 1)-th floor.

6. A rural housing structure optimization design system based on building information modeling for implementing the method according to any one of claims 1-5; The system includes: A data acquisition unit for acquiring the geographical and climatic data, building user demand data, and construction condition data of the rural house to be built; the geographical and climatic data includes topographic and geomorphic information and climate condition information; the building user demand data includes building area, house layout, and construction budget cost; the construction condition data includes available building material types, available construction equipment types, equipment rental cost, and construction labor unit price; A design variable determination unit for determining a set of house design variables according to the acquired geographical and climatic data, building user demand data, and construction condition data; the set of house design variables includes foundation design variables, structural frame design variables, wind and earthquake resistance design variables, and material selection design variables; each house design variable is used to indicate the value range of the corresponding variable parameter; A population initialization unit, which is used to randomly generate a set of initial solutions according to the value ranges of various variable parameters indicated by the housing design variable ranges, so as to construct an initial population; each individual in the initial population is respectively defined by a corresponding candidate housing structure design scheme, and the candidate housing structure design scheme includes candidate values of various variable parameters; A genetic optimization unit, which is used to optimize the individuals in the initial population by using a genetic algorithm, and screen individuals according to the calculation results of the fitness function to perform iterative genetic evolution operations including crossover and mutation, and finally determine the optimal individual; the fitness function of the genetic algorithm is defined according to the structural safety factor, cost budget matching degree and construction period corresponding to the candidate housing structure design scheme in the individual; A BIM modeling unit, which is used to input the target values of various variable parameters indicated by the optimal individual into the BIM construction module to determine the target BIM model for the rural house.

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