Rural house structure optimization design method and system based on building information modeling

Abstract

The invention discloses a rural house structure optimization design method and system based on building information modeling, and relates to the technical field of building structure analysis design, and the method comprises the steps: obtaining geographical climate data, building user demand data and construction condition data of a to-be-built rural house; determining a house design variable set according to the acquired data; randomly generating an initial solution according to the value range of each variable parameter in the house design variables to construct an initial population; performing individual optimization on the initial population by using a genetic algorithm, screening individuals according to fitness, performing iterative genetic evolution operation including crossover and variation, and finally determining an optimal individual; and inputting 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. Therefore, genetic algorithm optimization and a BIM modeling technology are organically fused, and the structural safety, the economical efficiency, the construction efficiency and the intelligent level of rural house design are improved.

Description

Technical Field

[0001] The present application relates to the technical field of building structure analysis and design, and in particular to a rural house structure optimization design method and system based on building information modeling. Background Art

[0003] Due to the complex topography of rural areas, many areas are located in earthquake-prone areas or areas that are greatly affected by strong storm weather. However, in the process of house design and construction, there is a lack of scientific analysis of environmental conditions such as geology and climate, and it is impossible to carry out advanced structural analysis and optimization, resulting in the safety of the house structure not being effectively guaranteed when facing natural disasters (such as earthquakes, storms, etc.).

[0004] In addition, the technical level of rural construction teams varies greatly, and there are many difficulties in the management and communication of construction sites. Traditional two-dimensional design drawings (for example, AutoCAD drawings) are difficult to clearly express complex spatial layouts and construction steps, especially the position and connection relationship of components in three-dimensional space, which can easily lead to misunderstandings and construction errors by construction workers, thus affecting the quality and safety of rural houses.

[0005] In response to the above problems, the industry has not yet proposed a better technical solution. Summary of the invention

[0006] The present application provides a rural house structure optimization design method, system, storage medium, computer program product and electronic device based on building information modeling, which is used to at least solve the problem in the current related technology that the quality and safety of rural houses cannot be guaranteed due to insufficient environmental analysis and insufficient design optimization capabilities.

[0007] In a first aspect, an embodiment of the present application provides a rural house structure optimization design method based on building information modeling, comprising: obtaining geographic climate data, building user demand data and construction condition data of a rural house to be built; the geographic climate data includes topographic information and climate condition information; the building user demand data includes building area, house layout and construction budget cost; the construction condition data includes types of supplyable building materials, types of available construction equipment, equipment rental costs and unit price of construction manpower; according to the acquired geographic climate data, building user demand data and construction condition data, determining a house design variable set; the house design variable set 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. range; randomly generate a set of initial solutions according to the value range of each variable parameter indicated by the housing design variable range to construct an initial population; each individual in the initial population is defined by a corresponding candidate housing structure design scheme, and the candidate housing structure design scheme includes candidate values ​​of various variable parameters; optimize the individuals in the initial population using a genetic algorithm, select individuals according to the fitness function calculation results 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, cost budget matching and construction period corresponding to the candidate housing structure design schemes in the individuals; input 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] In a 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, used to acquire geographic climate data, building user demand data and construction condition data of the rural house to be built; the geographic climate data includes topographic information and climate condition information; the building user demand data includes building area, house layout and construction budget cost; the construction condition data includes types of supplyable building materials, types of available construction equipment, equipment rental costs and unit price of construction manpower; a design variable determination unit, used to determine a house design variable set according to the acquired geographic climate data, building user demand data and construction condition data; the house design variable set 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; the population initialization unit is used to determine the ... An initialization unit is used to randomly generate a set of initial solutions according to the value range of each variable parameter indicated by the housing design variable range to construct an initial population; each individual in the initial population is 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 is used to optimize the individuals in the initial population using a genetic algorithm, select individuals according to the fitness function calculation results 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, cost budget matching and construction period corresponding to the candidate housing structure design scheme in the individual; a BIM modeling unit is used to input 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.

[0009] In a third aspect, an electronic device is provided, comprising: 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 so that the at least one processor can execute the steps of the rural house structure optimization design method based on building information modeling of any embodiment of the present application.

[0010] In a 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 rural house structure optimization design method based on building information modeling of any embodiment of the present application are implemented.

[0011] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the rural house structure optimization design method based on building information modeling of any embodiment of the present application.

[0012] The rural house structure optimization design method and system based on building information modeling provided by this application can produce at least the following technical effects:

[0013] (1) Based on the complex geographical and climatic conditions in rural areas, the impact of natural disasters such as earthquakes and strong winds is fully considered. In house design, by obtaining topographic information and climatic conditions data, a set of design variables that adapt to the local environment can be generated, covering foundation design, structural frame design, and wind and earthquake resistance. Through iterative optimization of genetic algorithms, the safety of the house structure is used as an important part of the fitness function to ensure the structural reliability of the house design and its ability to resist natural disasters.

[0014] (2) Not only the structural safety of the building design is considered, but also the building user requirements (such as building area, house layout and construction budget cost) and construction condition data (such as building materials, equipment and labor costs) are taken 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 maximizes the matching of the construction budget, reduces resource waste and unnecessary expenses, and achieves the compatibility of cost control and design optimization. As a result, the system can autonomously adjust the design variables according to the changing environment and needs, making the house design plan highly flexible and intelligent.

[0015] (3) The optimal design solution is generated into a detailed three-dimensional BIM model 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 relationship of the house, making it easier for the construction team to understand the design plan and reducing construction misunderstandings and errors caused by unclear drawings. At the same time, the BIM model has the ability to dynamically update and provide real-time feedback, and can make real-time adjustments and feedback according to the actual situation during the construction process, further ensuring the construction quality of the house.

[0016] Through this technical solution, genetic algorithm optimization and BIM modeling technology are organically integrated, which significantly improves the structural safety, economy, construction efficiency and intelligence level of rural house design, significantly improves the disaster resistance of houses, and optimizes the construction management and communication processes in the rural house construction process, which has extremely high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flowchart showing an example of a rural house structure optimization design method based on building information modeling according to an embodiment of the present application is shown;

[0019] Figure 2 A flowchart showing another example of a rural house structure optimization design method based on building information modeling according to an embodiment of the present application is shown;

[0020] Figure 3 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 is shown;

[0021] Figure 4 It is a schematic structural diagram of an embodiment of an electronic device of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved shall comply with the provisions of relevant laws and regulations and shall not violate public order and good morals.

[0024] Figure 1 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 is shown.

[0025] Regarding the execution subject of the method of the embodiment 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 middle terminal, for example, by running a building design optimization function software to perform various operations. Specifically, design optimization is performed by a genetic algorithm so that the house design scheme can dynamically generate the optimal solution according to multi-dimensional input data (such as terrain, climate, user needs, construction conditions, etc.). Through iterative evolution, the system can autonomously adjust the design variables according to the changing environment and needs, so that the design scheme has a high degree of flexibility and intelligence. At the same time, the genetic algorithm can select reasonable materials and construction techniques under the premise of ensuring safety and budget matching. Then, a detailed three-dimensional BIM model is generated by BIM three-dimensional modeling, so that the construction team can more easily understand the design scheme.

[0026] In some examples, it may be integrated and configured in an electronic device or terminal by means of software, hardware, or a combination of software and hardware, and the type of the terminal or electronic device may be diverse, such as a mobile phone, a tablet computer, or a desktop computer, etc.

[0027] like Figure 1 As shown, in step S110, the geographical climate data, building user demand data and construction condition data of the rural house to be built are obtained.

[0028] Here, the geographic climate data includes topographic information and climate conditions information, the building user demand data includes building area, house layout and construction budget cost, and the construction condition data includes the types of supplyable building materials, the types of available construction equipment, equipment rental costs and unit prices of construction labor.

[0029] In some embodiments, GIS (Geographic Information System) and meteorological databases are used to obtain detailed topographic information of the rural house location, 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 underground drainage system, and the foundation design may need to be deeper and wider to ensure stability. In addition, the slope and height difference of the terrain will also affect the layout and orientation selection of the house.

[0030] Climate condition data includes historical meteorological data, such as annual wind speed, rainfall, temperature, earthquake frequency, etc. Here, climate data will be used to optimize the wind resistance design, thermal insulation performance, and drainage system design of the house. For example, in areas with frequent strong winds, houses need additional wind resistance design, and the roof slope and structural rigidity need to be strengthened; in areas with heavy rainfall, the use of roof drainage systems and waterproof materials needs to be considered.

[0031] By detecting user interactions, such as user input information, building user demand data is obtained, including building area, house layout (such as number of rooms, functional divisions, etc.) and available construction budget. In addition, construction condition data can also be determined through user interaction, 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 combined with the local labor market to determine equipment rental prices and construction labor costs.

[0032] Here, the types of building materials available will directly affect the material selection of the house. For example, remote areas may lack high-strength concrete or steel materials, so the design needs to consider the use of brick-concrete structure or wooden structure. In addition, the types of construction equipment available will affect the choice of construction technology, and the lack of equipment may limit the implementation of complex construction technology. It is necessary to choose a simpler construction design scheme during the design. For example, if there is a lack of large lifting equipment, it may be necessary to choose prefabricated components instead of on-site casting. In addition, the equipment rental cost and the unit price of construction labor will be used to evaluate the economic feasibility of different design schemes during the design stage.

[0033] In step S120, a house design variable set is determined based on the acquired geographical climate data, building user demand data, and construction condition data.

[0034] Here, the house design variable set includes foundation design variables, structural frame design variables, wind and earthquake resistance design variables, and material selection design variables, and each house design variable is used to indicate the value range of the corresponding variable parameter. Exemplarily, the value range of each variable parameter is obtained by querying the corresponding variable calibration table with the acquired data.

[0035] In some embodiments, the foundation design variables determine the type and size of the house foundation and its interaction with the foundation, which directly affects the overall stability and anti-settlement ability of the house. Specifically, the foundation design variables include: foundation depth, foundation width and foundation type. Exemplarily, the foundation type (such as strip foundation, independent 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 to 3 meters) is determined according to the soil type and groundwater level, and for 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 is usually matched with the foundation depth to disperse the building load and can be set to a width interval 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 house. Foundation types can include strip foundation, raft foundation, pile foundation, etc. Different foundation types are adapted to 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.

[0036] In addition, according to the building layout and budget, determine the structural type of the house (such as frame structure, brick-concrete structure or light steel structure) and the size range of the main components. The structural frame design variables include the following variable parameters: beam and column cross-section size, wall thickness and floor height. The cross-section size of the beam and column can be calculated according to the floor height of the house. The value range of rural houses can generally be set as 200mm×300mm to 500mm×700mm. Higher floors or heavier load requirements will require larger beam and column cross-sections to improve structural stiffness. The value range of wall thickness can be set to the conventional wall thickness range of 200mm to 500mm, and can be adjusted according to earthquake resistance and insulation requirements. For example, for cold areas, thicker walls can improve insulation effects, while in earthquake-prone areas, increasing wall thickness helps improve structural stability. The floor height generally ranges from 2.6 meters to 3.5 meters and can float according to user needs, but a higher floor height will increase the cross-section size of beams and columns, and the construction cost will also increase accordingly.

[0037] In addition, combined with meteorological and seismic data, parameters such as wind load and seismic action are determined, and corresponding wind-resistant and seismic design schemes are generated (such as adding seismic walls, adjusting the spacing between structural columns, etc.). Wind-resistant and seismic design variables include: lateral force resistance system stiffness, number of shear walls, and steel bar anchorage length. Specifically, the value range of structural stiffness can be adjusted according to seismic zoning and wind speed data (such as through beam and column cross-sectional dimensions, stiffness settings of supporting structures, etc.). The number and arrangement of shear walls directly affect the lateral stiffness of the house, which can be determined according to the height of the house, seismic requirements, and wind speed conditions. Under earthquake and wind loads, the reasonable arrangement of shear walls is crucial. 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 house. The range of steel bar anchorage length is determined according to the stress conditions of the beam-column nodes and seismic requirements, and can be set in accordance with building specifications, such as 150mm to 600mm.

[0038] In addition, candidate building material types are screened from available building material types based on material strength grade and material durability coefficient. Exemplarily, the material strength grade is determined based on the design load of the house, seismic requirements, and wind resistance requirements. For example, the strength grade of concrete can be set to C25 to C40, and the steel strength grade is HRB335 to HRB500. Higher material strength can improve the overall seismic performance of the house, but at the same time it will increase costs. The material durability coefficient is determined based on local climatic conditions (such as humidity, temperature difference, degree of weathering, etc.). The material durability is evaluated by indicators such as its corrosion resistance and anti-aging performance. The value range can be set to 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.

[0039] By determining a multidimensional set of design variables for foundation, structure, wind and earthquake resistance, and materials, multiple constraints and parameter ranges are provided for house design, allowing the design process to flexibly respond to different construction conditions and user needs while meeting safety and practicality.

[0040] In step S130, a group of initial solutions is randomly generated according to the value ranges of the various variable parameters indicated by the house design variable set to construct an initial population.

[0041] Here, each individual in the initial population is defined by a corresponding candidate housing structure design scheme, and the candidate housing structure design scheme includes candidate values ​​of various variable parameters.

[0042] In some embodiments, a Monte Carlo method or other random number generation algorithm can be used to randomly generate a set of initial solutions within the range of values ​​of each design variable. Each solution represents a candidate housing structure design scheme, including random values ​​of various variable parameters such as foundation, structural frame, wind and earthquake resistance measures, material selection, etc. 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 enough search space.

[0043] For example, in step S120, the approximate value range of each design variable parameter has been determined, for example:

[0044] - The foundation depth is 1.5m to 3m.

[0045] - Beam and column cross-section sizes range from 200mm x 300mm to 500mm x 700mm.

[0046] - Wall thickness is 200mm to 500mm.

[0047] -Material strength grades are C25 to C40 (concrete strength).

[0048] According to the above value ranges, several parameter combinations are randomly selected in the parameter space. These combinations represent different house structure design schemes, which constitute 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 300 mm, and a material strength of C30.

[0049] 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 to ensure that the variable combination of each individual is different, that is, the random combination of variable parameters should be diverse. In addition, advanced sampling methods such as Latin hypercube sampling can be used to generate the initial population to ensure that the value of each design variable is evenly distributed in the population, avoid individuals from being concentrated in certain areas of the variable space, and improve the diversity of the population.

[0050] In step S140, the individuals in the initial population are optimized using a genetic algorithm, and individuals are selected according to the fitness function calculation results to perform iterative genetic evolution operations including crossover and mutation, and finally the optimal individual is determined.

[0051] It should be noted that the genetic algorithm is an optimization algorithm that simulates natural evolution. Its core idea is to continuously optimize individuals in the population through the evolutionary strategy of "survival of the fittest". Thus, through iterative crossover, mutation and selection, the optimal solution can be gradually screened out.

[0052] Specifically, starting from the generated initial population, the fitness function of each individual is calculated. Individuals with high fitness are crossovered and mutated to generate new individuals. Individuals with high fitness are selected to enter the next generation according to their fitness. The above steps are repeated and iterated until the convergence condition is met (such as reaching the maximum number of iterations or the fitness reaches a certain threshold), and the optimal individual is determined.

[0053] The crossover operation is one of the core steps of the genetic algorithm. It simulates the 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 the gene mutation in biological evolution. By making small random changes to the design variables of individuals, new individuals are generated, which can increase the diversity of the population and prevent the population from falling into the local optimal solution.

[0054] In the embodiment of the present application, the fitness function of the genetic algorithm is defined according to the structural safety, cost budget matching and construction period corresponding to the candidate housing structure design scheme in the individual. Furthermore, in each generation of the genetic algorithm, the fitness value of each individual in the population needs to be calculated first.

[0055] Specifically, the structural safety can be quickly evaluated by a simplified mechanical model or a preset specification standard. The cost-budget matching degree is obtained by evaluating the total cost of the design solution and comparing it with the budget cost. The construction cycle time is predicted by evaluating the construction complexity of the design solution, such as construction steps, equipment utilization, and labor requirements. More details about the fitness function will be expanded in conjunction with other examples below.

[0056] 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.

[0057] Through the optimization process of the genetic algorithm in step S140, an optimal individual or multiple optimal solutions will eventually be selected, and 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 the optimal individual (such as beam and column size, wall thickness, material type, etc.) need to be extracted and adapted to the information model in the BIM construction module to ensure that these parameters can be smoothly integrated into the BIM model.

[0058] It should be noted that the types of BIM building modules can be diverse. For example, Autodesk Revit or Tekla Structures can be selected, which can support multidisciplinary design collaboration and are suitable for the overall design and management of rural houses in complex environments.

[0059] Here, the BIM system will generate a three-dimensional building model based on the input geometric parameters (such as beam and column size, floor height, wall thickness, etc.). For rural houses, this model will include the design of all major load-bearing structures, enclosure 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 cycle information obtained from the optimal individual. For example, each part of the BIM model (such as beams, columns, shear walls, etc.) contains information such as the type of materials used, specifications, and construction time, so that it can be used in subsequent construction and maintenance stages. At this point, the BIM system is not just a design tool, it can also run through the entire life cycle of house construction, including construction, operation, and maintenance stages. By introducing the optimized design parameters into the BIM model, it provides data support for subsequent construction management, cost control, material procurement, etc.

[0060] In some examples of the embodiments of the present application, the fitness function is expressed by the following formula:

[0061] Q=w1·S 结构 +w2·B 成本 +w3·S 周期 , Formula (1)

[0062] In the formula, Q represents the fitness of the individual, S 结构 represents the structural safety of the candidate house structure design scheme in the individual, B 成本 represents the cost budget matching degree of the candidate house structure design scheme in the individual, S 周期 represents the degree of matching of the construction period corresponding to the candidate house structure design scheme in the individual; w1, w2 and w3 are fitness weights.

[0063] Here, the fitness weight can be dynamically adjusted according to the specific requirements of the project. For example, if the project is located in an earthquake-prone area, the weight of seismic safety can be increased to give priority to solutions with stronger seismic resistance. In addition, if the project emphasizes economy, the weight of cost-budget matching can be increased to encourage the algorithm to give priority to lower-cost design solutions. Therefore, 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 so that the house design is more in line with the specific project business needs.

[0064] Structural safety S 结构 It mainly measures the stability and bearing capacity of a house under common loads (such as wind loads, earthquake loads, etc.). For example, a simplified physical model can be used in combination with structural design specifications to calculate the lateral force resistance, earthquake resistance and structural stiffness of the house structure.

[0065] Specifically, for S结构 The calculation is:

[0066] S 结构 =w 风 ·S 风 +w 震 ·S 震 , Formula (2)

[0067]

[0068] F 地 =C 震 ·M 房屋 ·g , Formula (6)

[0069] In the formula, S 风 Indicates the wind resistance safety of the house, S 震 Indicates the earthquake safety of the house, w 风 and w 震 is the structural safety weight; K 侧 is the lateral stiffness of the building, F 风 is the wind load; C d A is the wind pressure coefficient, which is determined according to the shape of the building and the roughness of the windward surface; 风 is the windward area of ​​the house; 风 is the air density, V 风 Design wind speed for the house; F 地 is the earthquake load; C 震 M is the seismic coefficient, which is set according to the design earthquake intensity of the area where the house is located; 房屋 is the weight of the house and g is the acceleration due to gravity.

[0070] Here, the wind pressure coefficient C d It is a parameter that reflects the shape and roughness of the windward surface of a building and the influence of the building's surrounding environment on wind load. It is usually determined based on the building's geometric shape and airflow conditions. There are relevant reference values ​​in some common building design specifications. The calculation method of the example is as follows:

[0071] C d =C h ·C s , Formula (7)

[0072] In the formula, C h is the building height correlation coefficient, reflecting the influence of wind pressure changing with building height. s It is the building shape coefficient, which reflects the influence of the shape of the windward side of the building on the wind load.

[0073] For low-rise buildings, the wind pressure varies less along the height. For example, a 2-story or 3-story rural house takes C h=1, when the height exceeds 10 meters, C can be used h = 1.1 to 1.3. For a general rectangular building, C s = 1.0 to 1.2; For houses with sloping roofs, the shape coefficient varies with the inclination angle. Generally, houses with an inclination angle of less than 30 degrees can take C s = 0.8 to 1.0; for circular or other complex geometric shapes, C s =1.1 to 1.3.

[0074] Therefore, by running the above function module, the structural safety of each candidate design scheme can be quickly estimated.

[0075] Cost budget matching degree B 成本 It measures whether the construction cost of the design plan matches the budget. The construction cost mainly includes material cost, equipment rental cost and labor cost. Material cost is calculated based on the type, strength grade, usage and unit price of the selected material. Labor cost is calculated based on the type of work and labor required during the construction process. Equipment rental cost is the rental fee of mechanical equipment used during the construction process.

[0076] Specifically, for B 成本 The calculation is:

[0077]

[0078] C 总 =C 材料 +C 设备 +C 人工 , formula (9)

[0079]

[0080] 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 of the a-th building material, P a is the unit price of the a-th building material, A is the number of building materials involved in the candidate house structure design scheme in the individual; T j is the usage time of the jth device, R j is the rental unit price of the jth type of equipment, J is the number of types of construction equipment involved in the candidate house structure design scheme in the individual; N α is the number of workers of type α, W α is the hourly wage of the αth type of worker, T αis the construction time of the αth type of worker, and I is the number of types of work involved in the candidate house structure design scheme in the individual.

[0081] Therefore, by estimating the cost of labor, materials, and machinery during the construction process and comparing them with the budget, the fitness function can quickly evaluate the cost matching of the design and achieve efficient cost estimation with low computing resource usage.

[0082] The construction period is determined by the complexity of the construction process, the supply time of materials, the length of time equipment is used, and the efficiency of manual operation. The total construction period can be estimated by adding up the time required for each construction task.

[0083] Specifically, for S 周期 The calculation is:

[0084]

[0085] Where, T 基准 It represents the expected construction period of the project, T 施工 represents the construction period calculated based on the candidate house structure design scheme in the individual, W l is the workload of the first construction task, E l is the construction efficiency of the lth construction task, and q is the total number of construction tasks involved in the candidate house structure design scheme in the individual.

[0086] Therefore, 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 give priority to solutions with short construction cycles and strong operability, and avoid choosing designs that are complex to construct despite their excellent performance in terms of safety or cost.

[0087] The fitness function provided by the embodiment of the present application covers three key dimensions: structural safety, cost budget matching and construction period. According to the calculation of structural safety by wind safety and earthquake safety, the fitness function can ensure the structural stability and disaster resistance of the design under natural disasters (such as earthquakes and strong winds). The total construction cost is estimated based on the accurately calculated construction costs of materials, equipment and labor, and compared with the budget. The fitness function can evaluate the economy of the solution and ensure that the design achieves the optimization effect while meeting the budget. By estimating the workload and efficiency of the construction tasks, the fitness function can evaluate the construction feasibility and efficiency of the design to ensure that the housing construction is completed on time or ahead of schedule.

[0088] Therefore, by comprehensively evaluating these key dimensions, the fitness function can comprehensively and accurately screen out design solutions that have superior performance in terms of safety, cost, and construction time, so that the house design can achieve the best balance while meeting multiple needs, ensuring the high quality and efficiency of the project.

[0089] In some examples of the embodiments of the present application, the crossover in the iterative genetic evolution operation may adopt a dynamic crossover operation and local optimization, and the mutation in the iterative genetic evolution operation may adopt an adaptive mutation operation.

[0090] In some embodiments, in the crossover, dynamic variable weights are introduced for the candidate values ​​of various variable parameters in the candidate housing structure design scheme corresponding to the individual, and a crossover operation is performed based on the sensitivity of each type of variable parameter to fitness to generate a new individual.

[0091] In the crossover process, the crossover strength of each design variable is determined according to its weight. For variables with larger weights, conservative crossover is used, 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 used to allow more exploration to increase the diversity of the population.

[0092]

[0093] In the formula, represents the value of the i-th variable parameter in the new individual generated by the crossover operation, G i Indicates the contribution of the i-th variable parameter to fitness, w i represents the i-th type variable parameter, and are the values ​​of the i-th variable parameters in the first and second parent individuals selected from the t-th generation, respectively, and ΔQ is the change in fitness value; is the change range of the given i-th variable parameter, indicating the small adjustment used for sensitivity analysis; Q(x1,x2,...,x i ,...,x n ) represents the individual fitness value without sensitive fine-tuning, Indicates that the i-th variable parameter x i Minor changes The individual fitness value after .

[0094] By adopting the above-mentioned crossover operation, weights are dynamically assigned to different design variables (such as beam-column sections, shear wall layout, material selection, etc.) according to their sensitivity to fitness. Variables with high sensitivity will be treated conservatively during the crossover process, while variables with low sensitivity are allowed to change more freely. This ensures that the crossover operation retains excellent genes while enhancing the global exploration capability. Especially in the design of rural house structures, key design parameters (such as earthquake resistance and wind resistance) can be stably inherited, while non-critical parameters (such as material selection and construction procedures) can generate diversified exploration through crossover. In addition, by dynamically adjusting the weights, variables with high sensitivity are less subject to 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.

[0095] For the generated new individuals, a better combination of design variables is searched in the local range according to the search radius to generate crossover individuals by local area tuning.

[0096] Specifically, for the generated new individuals, fine-tuning is performed in local areas (such as material costs or construction procedures), similar to the "local hill climbing method". By setting a search radius, a better combination of design variables is searched in a local range to improve the individual fitness.

[0097]

[0098] In the formula, It represents the adjustment amount used for local optimization; r 搜索 It is the search radius during local optimization, which is used to control the magnitude of optimization adjustment.

[0099] By adopting the above-mentioned local adjustment of the optimal solution, after the crossover operation generates new individuals, local optimization is used to further fine-tune the design variables in the new individuals, especially those minor parameters that have little effect on fitness. Through local search (limited by the search radius), a better solution is found in a local area of ​​the design space. For rural house design, local optimization can make adjustments in material selection, construction details, etc. to further optimize cost and construction time.

[0100] In particular, local optimization keeps individual changes within a reasonable range and does not cause excessive changes due to crossover or mutation, thereby maintaining the stability of optimization and avoiding unnecessary large design jumps. By optimizing the local area of ​​a specific design, the crossover individuals can be further enhanced on the basis of their original good fitness, thus improving the local fitness.

[0101] During mutation, the mutation rate of the mutation operation is dynamically adjusted according to the speed of change of the population fitness.

[0102] The adaptive mutation rate can be dynamically adjusted according to the change of fitness through the iterative process of the monitoring algorithm. The mutation rate changes with the change of population fitness. When the fitness of the population converges slowly, the mutation rate is increased to increase the diversity of the population; when the fitness increases rapidly, the mutation rate is reduced to maintain good genes.

[0103]

[0104] In the formula, μ t+1 represents the mutation rate of the t+1th generation, μ t represents the mutation rate of the tth generation, ΔG 平均 represents the average fitness difference between the tth generation and the t-1th generation, G 平均 represents the average fitness of the tth generation.

[0105]

[0106] In the formula, Indicates the value of the i-th variable parameter in the t+1-th generation individual generated by the mutation operation; rand(-r 变异 ,r 变异 ) means in -r 变异 to r 变异 The amount of random disturbance in the interval, r 变异 Indicates the maximum variation.

[0107] Through the embodiment of the present application, the population fitness changes are monitored and the mutation rate is adaptively adjusted to ensure that the mutation rate is large in the early exploration stage to ensure population diversity; the mutation rate gradually decreases during the later convergence to reduce the damage to excellent individuals. For rural house structure design, different design schemes can be explored through diversity in the early stage based on the dynamic mutation mechanism, and stably converge to the optimal scheme in the later stage.

[0108] In addition, for complex combinations of design variables (such as material selection, seismic design, wind load considerations, etc.), mutation operations can randomly change some design variables, further expand the search space, and prevent the optimization from falling into local optimality. Adaptive mutation can flexibly respond to changes in the design, especially when the design requirements are dynamically adjusted, such as changing the budget, adjusting the seismic standards, etc. The dynamic adjustment of the mutation rate can timely increase the variation range and quickly find a design solution that meets the new requirements.

[0109] Figure 2 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 is shown.

[0110] like 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.

[0111] For details of step S210, please refer to Figure 1 The description of the operations in will not be repeated here.

[0112] In step S220, finite element analysis is performed on the target BIM model to obtain stress distribution information, maximum displacement and inter-story displacement angle of the load-bearing components of the building in the target BIM model under wind load and seismic load.

[0113] 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, to perform detailed mechanical analysis on the load-bearing components of the house.

[0114] In order to perform mechanical calculations, the target BIM model needs to be divided into finite element meshes. Meshing is the basis of finite element analysis, and the quality and density of the mesh will directly affect the calculation accuracy. Regarding the selection of mesh density, in the key areas of load-bearing components (such as beams, columns, shear walls, etc.), the mesh should be denser to capture local stress concentration and displacement changes; in non-critical areas (such as large-area plates and the middle part of the wall), relatively coarse meshes can be used to reduce the amount of calculation.

[0115] Here, the boundary conditions of the finite element analysis are defined based on the foundation fixing conditions, soil characteristics, and the building structure support relationship.

[0116] Regarding the fixed conditions of the foundation, if the foundation is a solid concrete foundation, the foundation part can be set as a fixed boundary condition, that is, it is assumed that the foundation of the house cannot translate or rotate. For soft soil foundations, it is necessary to consider the settlement or slip of the foundation and define elastic supports or spring supports to simulate the flexible characteristics of the foundation.

[0117] For soil properties, the interaction between soil and foundation will affect the stress of the house. The interaction between soil and house foundation can be simulated by adding spring units or contact units. The soil stiffness depends on the actual geological exploration data.

[0118] For the building structure support relationship, it can express the support relationship of beams, columns, shear walls and other components inside the house. For example, the connection between beams and columns can be set to hinged or rigid, and the connection between walls and foundations can be fixed or sliding, etc. These connection conditions are crucial to the force analysis of the structure.

[0119] In order to realize the seismic and wind-resistant FEA (Finite Element Analysis) processing of the building structure, it is necessary to define the specific action mode of wind load and seismic load. More specifically, the corresponding wind load and seismic load can be calculated through user input information or physical model. For the details of calculating wind load and seismic load through physical model, please refer to the above description of formula (4) and formula (6), which will not be repeated here.

[0120] By applying wind loads and seismic loads and combining boundary conditions, finite element analysis is performed to obtain the response behavior of the house structure under different external forces, including stress distribution information, maximum displacement and inter-story displacement angle. Stress distribution information refers to the analysis of the stress distribution of the load-bearing components of the house (such as beams, columns, shear walls, etc.) under the action of wind loads and seismic loads, finding the stress concentration area, and evaluating whether structural failure will occur. The maximum displacement indicates the displacement of the house under wind loads and seismic loads, especially the maximum displacement of the top of the house. The inter-story displacement angle is an important indicator to measure the seismic performance of the house structure, which indicates the relative horizontal displacement between each floor of the house under the action of an earthquake.

[0121] In step S230, based on the stress distribution information, the maximum displacement and the inter-story displacement angle, it is verified whether the building structure indicated by the target BIM model meets the earthquake and wind resistance safety requirements.

[0122] In some embodiments, by checking the stress distribution of the load-bearing components, it is ensured that the stress level of each component is within the bearing capacity of the material. By comparing the maximum displacement obtained by simulation with the displacement limit specified in the specification, the stability of the house is evaluated. The size of the inter-story displacement angle directly affects the seismic performance of the house. By comparing it with the inter-story displacement angle specified in the specification, the stability of the house is evaluated.

[0123] Through the embodiments of the present application, finite element analysis is used to simulate the actual deformation and stress response of the house under the real wind load and earthquake load, providing dynamic simulation verification, which can directly observe the stress conditions of the house under various load conditions, and ensure the safety of the house design in actual use. By analyzing the stress distribution information, maximum displacement and inter-story displacement angle of the house structure, it is possible to accurately determine whether the house can meet the safety requirements of earthquake resistance and wind resistance, and the verification method based on finite element analysis can discover potential design defects and weak links.

[0124] Through the stress distribution information of FEA analysis, it is easy to find possible stress concentration areas in the house structure (such as beam-column connections, shear wall bottom, etc.), visualize the weak points in the house design, and optimize or strengthen the design in advance to avoid structural failure. Through FEA analysis, the real deformation of the house in 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, the design can be optimized in time.

[0125] In some examples of the embodiments of the present application, regarding the details of the above step S220, the shear stress of each load-bearing component of the house is calculated, and the stress distribution information is obtained according to the position of each load-bearing component of the house and the corresponding shear stress.

[0126] Specifically, the formula used to calculate the shear stress of the load-bearing components of a building is:

[0127]

[0128] In the formula, σ u is the shear stress of the u-th load-bearing component of the building, which is used to measure the internal mechanical response of the component under external load; F 外 is the external load determined by the combined force of wind load and earthquake load, f u is the shear force of the u-th load-bearing component of the building, b u is the cross-sectional area of ​​the u-th load-bearing component of the building; e u (t) is the relationship function between the elastic modulus of the u-th load-bearing component of the house and the time t, which is pre-calibrated according to the degradation test data of various types of building materials.

[0129] Here, the external load F 外 and member cross-sectional properties b u Combined with the above, the stress distribution of each load-bearing component under different loads can be directly calculated, effectively integrating different types of loads. Based on accurate stress calculation, it helps engineers better understand the mechanical performance of houses under various load combinations (such as wind and earthquake at the same time), and avoids blindly designing excessive component sections and unnecessary waste of materials. For example, if the stress distribution of a certain load-bearing component is reasonable and within a safe range, there is no need to use materials with excessive strength. Therefore, on the premise of ensuring that the overall safety of the house and the local bearing capacity meet the design requirements, it is possible to reasonably select economical materials and avoid using materials with excessive strength and high cost, thereby reducing the total construction cost of the project.

[0130] 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 uThe decrease of (t) will affect the load-bearing capacity of the structure. By introducing the time-varying elastic modulus e into the function u (t), allowing designers to fully consider the impact of material degradation on structural safety in stress calculations. Therefore, by regularly updating the material elastic modulus value, the stress distribution of the house under different service life can be dynamically evaluated. For example, as the elastic modulus decreases due to material aging, the stress of the load-bearing components will gradually increase, which can help to evaluate the safety of future houses in different time periods in advance and ensure the long-term structural safety of rural houses.

[0131] On the other hand, the target BIM model is subjected to finite element analysis to obtain the overall stiffness matrix of the building structure. The displacement of each load-bearing component of the building is calculated based on the overall stiffness matrix and external loads. The maximum displacement is determined based on the maximum displacement of each load-bearing component of the building. The displacement distribution information of each floor is fitted based on the displacement of each load-bearing component of the building to determine the inter-floor displacement angle of each floor.

[0132] Specifically, the calculation of the overall stiffness matrix of the building structure is:

[0133] K y =∫ V L T ·D·LdV, Formula (22)

[0134]

[0135] 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 node displacement; D is the elastic matrix of the material corresponding to the finite element element, and V is the unit volume of the finite element element; Z y is the assembly matrix, which is used to transform the stiffness matrix K of the local coordinate system of the unit y Mapped to the global coordinate system; K 总 represents the overall stiffness matrix of the house structure, and Y represents the total number of finite element units in the target BIM model.

[0136] It should be noted that the assembly matrix is ​​responsible for linking the stiffness matrix in the local coordinate system of the unit with the corresponding degrees of freedom in the global coordinate system of the structure. The nodal degrees of freedom involved in the stiffness matrix of each unit will occupy a specific position in the degree of freedom matrix of the entire structure. The assembly matrix assembles the stiffness matrices of all units into an overall stiffness matrix by mapping the degrees of freedom of the nodes.

[0137] The calculation for the displacement of the load-bearing components of the building is:

[0138] [K 总 ] 节点 ·δ节点 =F 节点 , Formula (24)

[0139] In the formula, [K 总 ] 节点 represents the node stiffness matrix of the load-bearing components of the building, δ 节点 F represents the node displacement of the load-bearing components of the building under load. 节点 Represents the nodal force vector generated by external loads acting on the nodes of the building's load-bearing components.

[0140] Through the assembly and refined calculation of the stiffness matrix, the deformation behavior of the building under wind load or earthquake load can be simulated more accurately. Especially when dealing with complex building structures, the displacement of each node can be accurately evaluated, and the maximum displacement of the entire structure can be further calculated, thereby improving the accuracy of the house structure analysis. Accurate displacement calculation can effectively prevent the structure from deforming too much when it exceeds the bearing capacity, improve the safety and reliability of the design, and also avoid over-design and save materials.

[0141] The calculation for the inter-story displacement angle is:

[0142]

[0143] In the formula, θ s is the interlayer displacement angle of the sth layer, H s is the floor height of the sth floor; Δh s is the relative horizontal displacement of the sth layer, indicating the displacement difference between the sth layer and the s-1th layer.

[0144] Here, by fitting the relative horizontal displacement Δh of each layer s With floor height H s , provides a direct and effective way to evaluate the seismic and wind resistance of buildings, and can accurately evaluate the inter-story displacement angle θ of each floor of the building under seismic loads s To ensure that it is within the range allowed by the code. If the inter-story drift angle exceeds the design code, the designer can adjust the component size, material selection or structural system in time according to these data to improve the seismic performance.

[0145] Through the embodiment of the present application, the local stiffness matrix K is constructed by finite element analysis. y And combine them into the overall stiffness matrix K 总, calculate the displacement 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 quickly iterate and optimize the design. By introducing finite element analysis, when it is found that the displacement of a part of the structure or the inter-story displacement angle exceeds the specification, it can be adjusted immediately in the model, and the simulation can be used to verify whether the new design meets the specification requirements, thereby supporting rapid iteration in the design process, allowing designers to obtain calculation results in a timely manner by dynamically adjusting parameters such as materials, sections, and loads, thereby optimizing the structural design, greatly shortening the time of traditional manual analysis and improving design efficiency.

[0146] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of actions combined, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application. In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0147] Figure 3 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 is shown.

[0148] like Figure 3 As shown, the rural housing 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.

[0149] The data acquisition unit 310 is used to obtain the geographic climate data, building user demand data and construction condition data of the rural house to be built; the geographic climate data includes topographic information and climate condition information; the building user demand data includes building area, house layout and construction budget cost; the construction condition data includes types of supplyable building materials, types of available construction equipment, equipment rental costs and unit prices of construction labor.

[0150] The design variable determination unit 320 is used to determine a set of house design variables based on the acquired geographic climate data, building user demand data and construction condition data; the house design variable set 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.

[0151] The population initialization unit 330 is used to randomly generate a set of initial solutions according to the value range of each variable parameter indicated by the house design variable range to construct an initial population; each individual in the initial population is defined by a corresponding candidate house structure design scheme, and the candidate house structure design scheme includes candidate values ​​of various variable parameters.

[0152] The genetic optimization unit 340 is used to optimize the individuals in the initial population using a genetic algorithm, screen the individuals according to the fitness function calculation results, 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, cost budget matching and construction period corresponding to the candidate house structure design schemes in the individuals.

[0153] 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.

[0154] In some embodiments, an embodiment of 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 execute the steps of any of the above-mentioned rural house structure optimization design methods based on building information modeling in the present application.

[0155] In some embodiments, the embodiments of the present application also provide a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes any step of the above-mentioned rural house structure optimization design method based on building information modeling.

[0156] In some embodiments, the embodiments of the present application also provide an electronic device, comprising: 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.

[0157] Figure 4 is a schematic diagram of the hardware structure of an electronic device for executing a rural house structure optimization design method based on building information modeling provided by another embodiment of the present application, such as Figure 4 As shown, the device includes:

[0158] One or more processors 410 and memory 420, Figure 4 A processor 410 is taken as an example.

[0159] The device for executing the rural house structure optimization design method based on building information modeling may further include: an input device 430 and an output device 440 .

[0160] The processor 410, the memory 420, the input device 430 and the output device 440 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0161] 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 rural housing structure optimization design method based on building information modeling in the embodiment 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, the rural housing structure optimization design method based on building information modeling in the above method embodiment is realized.

[0162] The memory 420 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may 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 a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 420 may optionally include a memory remotely arranged relative to the processor 410, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0163] The input device 430 may receive input digital or character information and generate signals related to user settings and function control of the electronic device. The output device 440 may include a display device such as a display screen.

[0164] The one or more modules are stored in the memory 420, and when executed by the one or more processors 410, the rural house structure optimization design method based on building information modeling in any of the above method embodiments is executed.

[0165] The above-mentioned product can execute the method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of the present application.

[0166] The electronic devices of the embodiments of the present application exist in various forms, including but not limited to:

[0167] (1) Mobile communication equipment: This type of equipment is characterized by having mobile communication functions and its main purpose is to provide voice and data communications. This type of terminal includes: smart phones, multimedia phones, functional phones, and low-end phones.

[0168] (2) Ultra-mobile personal computer devices: These devices belong to the category of personal computers, have computing and processing functions, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, etc.

[0169] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players, handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0170] (4) Other onboard electronic devices with data interaction functions, such as on-board devices installed in vehicles.

[0171] The device embodiments described above are merely illustrative, wherein 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 distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0172] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for 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.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A rural house structure optimization design method based on building information modeling, comprising: Obtain geo-climatic data, building user demand data, and construction condition data for the rural houses to be built; The geographical climate data includes topographic information and climate condition information; the building user demand data includes building area, house layout and construction budget cost; the construction condition data includes types of supplyable building materials, types of available construction equipment, equipment rental costs and construction labor unit price; Determine a housing design variable set according to the acquired geographical climate data, building user demand data and construction condition data; the housing design variable set includes foundation design variables, structural frame design variables, wind and earthquake resistance design variables and material selection design variables; each housing design variable is used to indicate the value range of the corresponding variable parameter; A set of initial solutions is randomly generated according to the value ranges of the various variable parameters indicated by the house design variable set to construct an initial population; each individual in the initial population is defined by a corresponding candidate house structure design scheme, and the candidate house structure design scheme includes candidate values ​​of various variable parameters; The individuals in the initial population are optimized by using a genetic algorithm, and the individuals are selected according to the fitness function calculation results to perform iterative genetic evolution operations including crossover and mutation, and finally the optimal individual is determined; the fitness function of the genetic algorithm is defined according to the structural safety, cost budget matching and construction period corresponding to the candidate housing structure design schemes in the individuals; 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 the rural house.

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 and column cross-sectional dimensions, wall thickness and floor 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, and the candidate building material types are selected from the available building material types based on material strength grade and material durability coefficient.

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

4. The method according to any one of claims 1 to 3, wherein: The fitness function is expressed by the following formula: Q=w1·S 结构 +w2·B 成本 +w3·S 周期 , In the formula, Q represents the fitness of the individual, S 结构 represents the structural safety of the candidate house structure design scheme in the individual, B 成本 represents the cost budget matching degree of the candidate house structure design scheme in the individual, S 周期 Indicates the matching degree of the construction period corresponding to the candidate house structure design scheme in the individual; w1, w2 and w3 are fitness weights; Among them, for S 结构 The calculation is: S 结构 =w 风 ·S 风 +w 震 ·S 震 , F 地 =C 震 ·M 房屋 ·g, In the formula, S 风 Indicates the wind resistance safety of the house, S 震 Indicates the earthquake safety of the house, w 风 and w 震 is the structural safety weight; K 侧 is the lateral stiffness of the building, F 风 is the wind load; C d A is the wind pressure coefficient, which is determined according to the shape of the building and the roughness of the windward surface; 风 is the windward area of ​​the house; 风 is the air density, V 风 Design wind speed for the house; F 地 is the earthquake load; C 震 M is the seismic coefficient, which is set according to the design earthquake intensity of the area where the house is located; 房屋 is the weight of the house, g is the acceleration due to gravity; Among them, for B 成本 The calculation is: C 总 =C 材料 +C 设备 +C 人工 , 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 of the a-th building material, P a is the unit price of the a-th building material, A is the number of building materials involved in the candidate house structure design scheme in the individual; T j is the usage time of the jth device, R j is the rental unit price of the jth type of equipment, J is the number of types of construction equipment involved in the candidate house structure design scheme in the individual; N α is the number of workers of type α, W α is the hourly wage of the αth type of worker, T α is the construction time of the αth type of worker, and I is the number of types of work involved in the candidate house structure design scheme in the individual; Among them, for S 周期 The calculation is: Where, T 基准 It represents the expected construction period of the project, T 施工 represents the construction period calculated based on the candidate house structure design scheme in the individual, W l is the workload of the first construction task, E l is the construction efficiency of the lth construction task, and q is the total number of construction tasks involved in the candidate house structure design scheme in the individual.

5. The method according to claim 4, wherein: The step of selecting individuals according to the fitness function calculation result to perform iterative genetic evolution operations including crossover and mutation includes: In the crossover, dynamic variable weights are introduced for the candidate values ​​of various variable parameters in the candidate housing structure design scheme corresponding to the individual, and a crossover operation is performed according to the sensitivity of each type of variable parameter to fitness to generate a new individual: In the formula, represents the value of the i-th variable parameter in the new individual generated by the crossover operation, G i Indicates the contribution of the i-th variable parameter to fitness, w i represents the i-th type variable parameter, and are the values ​​of the i-th variable parameters in the first and second parent individuals selected from the t-th generation, respectively, and ΔQ is the change in fitness value; is the change range of the given i-th variable parameter, indicating the small adjustment used for sensitivity analysis; Q(x1,x2,...,x i ,...,x n ) represents the individual fitness value without sensitive fine-tuning, Indicates that the i-th variable parameter x i Minor changes The individual fitness value after ; For the generated new individuals, a better combination of design variables is searched in the local range according to the search radius to generate crossover individuals by local area tuning: In the formula, It represents the adjustment amount used for local optimization; r 搜索 It is the search radius during local optimization, which is used to control the amplitude of optimization adjustment; In the mutation, the mutation rate of the mutation operation is dynamically adjusted according to the changing speed of the population fitness: In the formula, μ t+1 represents the mutation rate of the t+1th generation, μ t represents the mutation rate of the tth generation, ΔG 平均 represents the average fitness difference between the tth generation and the t-1th generation, G 平均 represents the average fitness of the tth generation; In the formula, Indicates the value of the i-th variable parameter in the t+1-th generation individual generated by the mutation operation; rand(-r 变异 ,r 变异 ) means in -r 变异 to r 变异 The amount of random disturbance in the interval, r 变异 Indicates the maximum variation.

6. The method according to any one of claims 1 to 3, wherein: After 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, the method further includes: Performing finite element analysis on the target BIM model to obtain stress distribution information, maximum displacement and inter-story displacement angle of the load-bearing components of the building in the target BIM model under wind load and seismic load; the boundary conditions of the finite element analysis are defined according to the foundation fixing conditions, soil characteristics and building structure support relationship; According to the stress distribution information, maximum displacement and inter-story displacement angle, it is verified whether the building structure indicated by the target BIM model meets the earthquake and wind resistance safety requirements.

7. The method according to claim 6, wherein: The finite element analysis is performed on the target BIM model to obtain stress distribution information, maximum displacement and inter-story displacement angle of the load-bearing components of the building in the target BIM model under wind load and seismic load, including: Calculate the shear stress of each load-bearing component of the building, and obtain stress distribution information based on the position of each load-bearing component of the building and the corresponding shear stress; The formula used to calculate the shear stress of the load-bearing components of a building is: In the formula, σ u is the shear stress of the u-th load-bearing component of the building, which is used to measure the internal mechanical response of the component under external load; F 外 is the external load determined by the combined force of wind load and earthquake load, f u is the shear force of the u-th load-bearing component of the building, b u is the cross-sectional area of ​​the u-th load-bearing component of the building; e u (t) is the relationship function between the elastic modulus of the u-th load-bearing component of the house and the time t, which is pre-calibrated according to the degradation test data of various types of building materials.

8. The method according to claim 6, wherein: The finite element analysis is performed on the target BIM model to obtain stress distribution information, maximum displacement and inter-story displacement angle of the load-bearing components of the building in the target BIM model under wind load and seismic load, including: Performing finite element analysis on the target BIM model to obtain an overall stiffness matrix of the building structure; Calculating the displacement of each load-bearing component of the building according to the overall stiffness matrix and the external load; Determine the maximum displacement according to the maximum displacement of each load-bearing component of the building; Fit the displacement distribution information of each floor according to the displacement of each load-bearing component of the building to determine the inter-story displacement angle of each floor; The calculation of the overall stiffness matrix of the building structure is: K y =∫ V L T ·D·LdV, 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 node displacement; D is the elastic matrix of the material corresponding to the finite element element, and V is the unit volume of the finite element element; Z y is the assembly matrix, which is used to transform the stiffness matrix K of the local coordinate system of the unit y Mapped to the global coordinate system; K 总 represents the overall stiffness matrix of the building structure, and Y represents the total number of finite element units in the target BIM model; The calculation for the displacement of the load-bearing components of the building is: [K 总 ] 节点 ·d 节点 =F 节点 , In the formula, [K 总 ] 节点 represents the node stiffness matrix of the load-bearing components of the building, δ 节点 It represents the node displacement of the load-bearing components of the building under load, F 节点 It represents the nodal force vector generated by the external load acting on the node of the load-bearing component of the building; The calculation for the interlayer displacement angle is: In the formula, θ s is the interlayer displacement angle of the sth layer, H s is the floor height of the sth floor; Δh s is the relative horizontal displacement of the sth layer, indicating the displacement difference between the sth layer and the s-1th layer.

9. A rural house structure optimization design system based on building information modeling, comprising: A data acquisition unit, used to acquire geographical climate data, building user demand data and construction condition data of the rural house to be built; The geographical climate data includes topographic information and climate condition information; the building user demand data includes building area, house layout and construction budget cost; the construction condition data includes types of supplyable building materials, types of available construction equipment, equipment rental costs and construction labor unit price; A design variable determination unit is used to determine a set of house design variables according to the acquired geographical climate 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 a value range of a corresponding variable parameter; A population initialization unit is used to randomly generate a set of initial solutions according to the value range of each variable parameter indicated by the housing design variable range to construct an initial population; each individual in the initial population is 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 is used to optimize the individuals in the initial population using a genetic algorithm, select individuals according to the fitness function calculation results 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, cost budget matching and construction period corresponding to the candidate housing structure design schemes in the individuals; The BIM modeling unit is used to input 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.

Images