A method and system for evaluating and optimizing a housing building structure

Abstract

The present invention discloses a method and system for evaluating and optimizing a housing building structure, which relates to the technical field of architectural design. The method includes: establishing a finite element model of the housing building, obtaining relevant parameters, and setting them in the model to analyze the value range of the parameters. Based on the value range, candidate values are selected according to rules, a parameter grid is constructed, and it is divided into a preset number of sub-regions. Parameters are randomly selected from each sub-region as a reference combination, an optimization objective function is set to calculate the target value of the sub-region, and the target sub-region is determined. The grid division and reference parameter selection are repeated for the target sub-region, and the range is gradually narrowed until the parameter space is smaller than the preset threshold. Finally, using an optimization algorithm and the objective function, the optimal housing building parameter combination is determined within the final target sub-region. The present invention solves the problem in the prior art that it is impossible to balance efficiency and accuracy when optimizing the housing building structure.

Description

Technical Field

[0001] The present invention relates to the technical field of architectural design, and particularly to a method and system for evaluating and optimizing a housing building structure. Background Art

[0002] In the current field of construction engineering, structural optimization design has become a key technology for improving building performance and reducing costs. With the increase in the complexity of architectural design and the growing requirements for building safety, economy, and environmental protection, traditional structural optimization design methods are no longer able to meet the current needs. Especially when faced with a large number of parameter combinations, the processing of data volume has become a huge challenge.

[0003] Structural optimization design usually involves multiple variables and complex constraint conditions, such as structural dimensions, material selection, layout methods, etc. Different combinations of these parameters will directly affect the performance of the building, including load-bearing capacity, stability, seismic resistance, thermal performance, etc. In actual operation, designers need to repeatedly adjust and optimize these parameters to achieve the best design effect.

[0004] However, with the expansion and increase in complexity of the building scale, the number and combination methods of parameters increase exponentially, resulting in an extremely large amount of data processing. Traditional optimization methods, such as the exhaustive method, have a large amount of calculation and long time consumption when faced with such a large parameter space. And for random search algorithms, due to the nature of the algorithm being random search, it is easy to miss the main parameter combinations. Therefore, the current housing structure optimization methods cannot balance efficiency and accuracy. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for evaluating and optimizing a housing building structure, aiming to solve the problem in the prior art that it is impossible to balance efficiency and accuracy when optimizing the housing building structure.

[0006] The embodiments of the present invention are implemented as follows:

[0007] A method for evaluating and optimizing a housing building structure, the method comprising:

[0008] Establish a finite element model of the housing building to be optimized, obtain housing building parameters related to the optimization of the housing building structure, set the housing building parameters in the finite element model and perform finite element analysis to obtain the value range of the housing building parameters;

[0009] Select candidate values for each housing building parameter from the value range of the housing building parameters according to a preset rule, and establish a housing building parameter grid based on the Cartesian product of the candidate values of each housing building parameter;

[0010] Divide the grid of building parameters of a house into a preset number of equal sub - regions according to preset rules, and randomly select the data of the building parameters in one grid from each sub - region as the reference building parameter combination of the sub - region;

[0011] Set the optimization objective function for the building structure of the house, determine the objective value of the sub - region according to the reference building parameter combination and the objective function, and determine the target sub - region from among the preset number of sub - regions according to the objective value;

[0012] After re - dividing the target sub - region into grids, respectively select the reference building parameter combinations of the sub - regions therein to calculate the objective value to determine a new target sub - region, so as to narrow down the range of the target sub - region until the parameter space of the target sub - region is less than a preset threshold;

[0013] Determine the final building parameter combination according to the data of the building parameters in the final target sub - region using a preset optimization algorithm and the building structure optimization objective function.

[0014] Further, in the above - mentioned building structure evaluation and optimization method, the step of establishing the grid of building parameters according to the Cartesian product of the candidate values of each building parameter includes:

[0015] Obtain the number of sets in the Cartesian product of the candidate values of each building parameter, and determine the number of unit grids of the grid of building parameters according to the number of sets in the Cartesian product;

[0016] Determine all possible combinations of the number of rows and the number of columns according to the number of unit grids, and determine the target combination with the smallest difference between the number of rows and the number of columns from among all the combinations of the number of rows and the number of columns;

[0017] Randomly fill the building parameter combinations composed of the candidate values of each building parameter into each unit grid to obtain the grid of building parameters.

[0018] Further, in the above - mentioned building structure evaluation and optimization method, the step of respectively and randomly selecting the data of the building parameters in one grid from each sub - region as the reference building parameter combination of the sub - region includes:

[0019] Randomly select a preset number of unit grids from each sub - region, and obtain the building parameter combinations in the unit grids as the reference building parameter combinations;

[0020] The step of determining the objective value of the sub - region according to the reference building parameter combination and the objective function includes:

[0021] The output values obtained by respectively inputting the benchmark housing construction parameter combinations into the objective function are averaged to obtain the target value of the sub-region.

[0022] Further, in the above housing construction structure evaluation and optimization method, the step of randomly selecting a preset number of unit grids from the sub-region and obtaining the housing construction parameter combinations within the unit grids as the benchmark housing construction parameter combinations includes:

[0023] Determine any two non-adjacent vertices in the sub-region and obtain the connection line between the two vertices;

[0024] Taking the two vertices as the starting point and the end point respectively, the curve formed with the preset frequency and preset amplitude is used as the grid reference line;

[0025] Determine the unit grids passed by the peak points and valley points of the grid reference line as the selected unit grids.

[0026] Further, in the above housing construction structure evaluation and optimization method, the step of dividing the housing construction parameter grid into a preset number of equal sub-regions according to a preset rule includes:

[0027] Obtain the central grid point in the housing construction parameter grid, and extend corresponding straight lines in the horizontal and vertical directions respectively with the central grid point as the origin to divide the housing construction grid into a preset number of equal sub-regions.

[0028] Further, in the above housing construction structure evaluation and optimization method, the expression of the objective function is:

[0029] ;

[0030] Wherein, α and β are respectively the weight coefficients, x is the vector of housing construction parameters, Wk is the weighted coefficient of each group of structural members, Ck(x) is the k th group of structural member cost functions, Ak is the weighted coefficient of each group of structural members, P is the ratio of the actual safety factor to the safety factor required by the code for the k th group of structural members.

[0031] Further, in the above housing construction structure evaluation and optimization method, the housing construction parameters include beam-column section dimensions, wall thickness, and steel bar anchorage length.

[0032] Another object of the present invention is to provide a housing construction structure evaluation and optimization system, and the system includes:

[0033] A building module, configured to establish a finite element model of a building to be optimized, obtain building parameters related to the optimization of the building structure, set the building parameters in the finite element model and perform finite element analysis to obtain the value range of the building parameters;

[0034] A selection module, configured to select candidate values of each building parameter from the value range of the building parameters according to a preset rule, and establish a building parameter grid according to the Cartesian product of the candidate values of each building parameter;

[0035] A division module, configured to divide the building parameter grid into a preset number of equal sub-regions according to a preset rule, and randomly select the data of the building parameters in one grid from each sub-region as the reference building parameter combination of the sub-region;

[0036] A setting module, configured to set an optimization objective function for the building structure, determine the objective value of the sub-region according to the reference building parameter combination and the objective function, and determine the target sub-region from the preset number of sub-regions according to the objective value;

[0037] A shrinking module, configured to perform grid division on the target sub-region again, select the reference building parameter combination of the sub-region therein respectively to calculate the objective value to determine a new target sub-region, so as to shrink the range of the target sub-region until the parameter space of the target sub-region is smaller than a preset threshold;

[0038] An optimization module, configured to determine the final building parameter combination according to the data of the building parameters in the final target sub-region by using a preset optimization algorithm and the building structure optimization objective function.

[0039] Another object of the present invention is to provide a readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0040] Another object of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor, and when the processor executes the program, the steps of the above method are implemented.

[0041] The present invention establishes a housing construction parameter grid based on the candidate values of housing construction structure parameters, divides the parameter grid, and in the case of a large amount of parameter data, determines the target sub-region where an optimal solution may appear according to the divided sub-regions. When the parameter space of the target sub-region is less than a preset threshold, finally search within the target sub-region to determine the final combination of housing construction parameters. In this way, both the high search efficiency and the accuracy of determining housing construction parameters are ensured. It solves the problem in the prior art that it is impossible to balance high efficiency and accuracy when optimizing the housing construction structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic flowchart of a method for evaluating and optimizing a housing construction structure provided by an embodiment of the present invention;

[0043] Figure 2 It is a structural block diagram of a housing construction structure evaluation and optimization system in the third embodiment of the present invention.

[0044] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. SPECIFIC EMBODIMENTS

[0045] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0046] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0048] The following will specifically describe in detail how to balance high efficiency and accuracy when optimizing the housing construction structure in combination with specific embodiments and drawings.

[0049] Embodiment 1

[0050] Please refer to Figure 1 , which shows the method for evaluating and optimizing the housing building structure in the first embodiment of the present invention. The method includes steps S10 to S15.

[0051] Step S10: Establish a finite element model of the housing building to be optimized, obtain housing building parameters related to the optimization of the housing building structure, set the housing building parameters in the finite element model, and perform finite element analysis to obtain the value range of the housing building parameters.

[0052] Among them, use ANSYS software to establish a finite element model of the housing building to be optimized, set housing building parameters related to the optimization of the housing building structure in the model, such as the cross-sectional dimensions of beams and columns, the thickness of walls, and the anchorage length of steel bars. Perform finite element analysis in ANSYS software, such as static calculation, dynamic analysis, or fatigue analysis, etc., to evaluate the impact of the above housing building parameters on the overall housing. Thus, according to the results of the analysis and calculation, the appropriate value range interval of the housing building parameters, that is, the feasible region of the structural design parameters, can be determined.

[0053] Step S11: Select candidate values for each housing building parameter from the value range of the housing building parameters according to a preset rule, and establish a housing building parameter grid based on the Cartesian product of the candidate values of each housing building parameter.

[0054] Specifically, after determining the value range of the housing building parameters, since specific values need to be determined during structural optimization, therefore, a preset number of specific values can be selected from this range as the candidate values of the housing building parameters. These candidate values are used for subsequent optimization calculations, so as to determine the final value from them. In specific implementation, the number of candidate values to be selected can be determined according to the size of the value range, and the final number of candidate values can be selected within the value range at preset intervals respectively. At the same time, after selecting the candidate values of each housing building parameter, its corresponding Cartesian product can be obtained to establish a housing building parameter grid.

[0055] Among them, the Cartesian product is a basic concept in set theory, referring to the set of all possible ordered pairs between two or more sets. In the embodiment of the present invention, the Cartesian product of the housing building parameters refers to the set formed by all possible combination methods of taking one element from the set of candidate values of each housing building parameter. And the housing building parameter grid is a grid that contains all the combination methods of the housing building parameters.

[0056] Specifically, in some alternative embodiments of the present invention, the step of establishing a housing building parameter grid based on the Cartesian product of the candidate values of each housing building parameter includes:

[0057] Obtain the number of sets within the Cartesian product of the candidate values of each building parameter, and determine the number of unit grids of the building parameter grid according to the number of sets within the Cartesian product;

[0058] Determine all possible combinations of the number of rows and columns according to the number of unit grids, and determine the target combination with the smallest difference between the number of rows and the number of columns from all combinations of the number of rows and columns;

[0059] Randomly fill the building parameter combinations formed by the candidate values of each building parameter into each unit grid to obtain the building parameter grid.

[0060] Among them, according to the calculated number of sets of the Cartesian product (i.e., the number of all possible building parameter combinations), determine the number of unit grids of the building parameter grid. Here, the "unit grid" can be understood as an independent space or position used to represent each building parameter combination; after determining the total number of unit grids, consider how to arrange these grids in the form of a two-dimensional table. This involves determining the number of rows and columns of the table. Since the total number of grids is fixed, there are multiple combinations of the number of rows and columns that can achieve this total; among all possible combinations of the number of rows and columns, select a combination with the smallest difference between the number of rows and the number of columns as the target combination. The purpose of this step is to create a grid layout that is as balanced as possible, avoiding a too large disparity in the number of rows or columns, so as to facilitate subsequent operations and analysis; finally, randomly assign all the previously calculated possible building parameter combinations to each unit grid. In this way, each grid represents a specific parameter combination, forming a complete building parameter grid. This grid can be used for further analysis, simulation, or optimization processes.

[0061] Step S12, divide the building parameter grid into a preset number of equal sub-regions according to a preset rule, and randomly select the data of the building parameters within one grid from each sub-region as the reference building parameter combination of the sub-region.

[0062] Among them, in order to reduce the parameter space and improve the efficiency of the optimization search, divide the parameter grid into multiple equal sub-regions. Within each divided sub-region, randomly select a grid cell, and the building parameter combination within this selected grid cell is regarded as the reference parameter combination of this sub-region. The "reference parameter combination" means that within this sub-region, this set of parameters is regarded as a representative or "typical" parameter set. The random selection process ensures that the reference parameter combination of each sub-region has a certain degree of randomness and diversity.

[0063] Specifically, in some alternative embodiments of the present invention, the step of dividing the housing construction parameter grid into a preset number of equal sub-regions according to a preset rule includes:

[0064] Obtain the central grid point in the housing construction parameter grid, and extend corresponding straight lines in the horizontal and vertical directions respectively with the central grid point as the origin to divide the housing construction grid into a preset number of equal sub-regions.

[0065] First, it is necessary to determine the central grid point of the entire housing construction parameter grid. This central grid point can be the average of the central positions of all grid cells in the grid, or a fixed point pre-calculated according to the geometric shape and size of the grid. Once the central point is determined, straight lines can be extended from this point in the horizontal direction (i.e., the row direction of the grid) and the vertical direction (i.e., the column direction of the grid) respectively. These straight lines divide the grid into multiple parts, and each part represents a sub-region. Generally, the housing construction parameter grid will be divided into four equal sub-regions.

[0066] Step S13, set the housing construction structure optimization objective function, determine the target value of the sub-region according to the reference housing construction parameter combination and the objective function, and determine the target sub-region from the preset number of sub-regions according to the target value.

[0067] Among them, before optimizing the housing construction structure, it is first necessary to clarify the optimization objective. This objective is usually one or more indicators related to the performance of the building structure, such as cost, strength, stability, energy consumption, etc. Specifically, the expression of the objective function is:

[0068] ;

[0069] Among them, α and β are the weight coefficients respectively, x is the vector of housing construction parameters, Wk is the weighted coefficient of each group of structural members, Ck(x) is the k group of the cost function of the structural members, Ak is the weighted coefficient of each group of structural members, P is the ratio of the actual safety factor to the safety factor required by the code for the k group of structural members.

[0070] Further, substitute these benchmark parameter combinations into the objective function to calculate the corresponding objective values. This objective value reflects the performance or performance of this sub-region in terms of the optimization objective. After obtaining the objective values of all sub-regions, it is necessary to compare these values to determine which sub-region performs best in terms of the optimization objective, and select a sub-region that performs best in terms of the optimization objective as the target sub-region. These sub-regions contain possible optimal or sub-optimal parameter combinations and are the focus of subsequent detailed analysis and optimization.

[0071] Step S14: After re-meshing the target sub-region, select the benchmark housing construction parameter combinations of the sub-regions therein respectively to calculate the objective values to determine a new target sub-region, so as to narrow down the range of the target sub-region until the parameter space of the target sub-region is smaller than a preset threshold.

[0072] Among them, after initially determining the target sub-region, in order to more finely explore the parameter space within this region, it is necessary to perform re-meshing on it. The meshing method is the same as before. The meshed grid further divides the target sub-region into smaller sub-regions, and each sub-region contains fewer parameter combinations, thus making the subsequent parameter analysis and optimization process more accurate and efficient. In each newly meshed sub-region, select one or more benchmark housing construction parameter combinations. These combinations represent one or more representative samples of the parameter space within this sub-region. Substitute these benchmark parameter combinations into the previously set objective function to calculate the corresponding objective values. These objective values reflect the performance or performance of each sub-region in terms of the optimization objective. In this way, one or more objective values can be determined for each newly meshed sub-region to determine a new target sub-region. Repeat the above steps (i.e., meshing, objective value calculation, and new target sub-region determination) to continuously narrow down the range of the target sub-region until it is smaller than the preset threshold. This threshold is a parameter space size limit preset according to the optimization requirements and problem complexity and is not limited here.

[0073] Step S15: Determine the final housing construction parameter combination according to the data of the housing construction parameters within the final target sub-region using the preset optimization algorithm and the housing construction structure optimization objective function.

[0074] Among them, after a series of iterative and optimization steps, a target sub-region with a smaller range and better parameter combinations is obtained. The parameter data within this sub-region represents one or more sets of housing construction parameters that may meet the optimization objective. The preset optimization algorithm is a mathematical method used to find the optimal or approximate optimal solution within a given parameter space. In specific implementation, methods including but not limited to gradient descent method, genetic algorithm, particle swarm optimization algorithm, and global search algorithm can be adopted.

[0075] In summary, for the method for evaluating and optimizing the housing building structure in the above embodiments of the present invention, a housing building parameter grid is established based on the candidate values of the housing building structure parameters, and the parameter grid is divided. In the case of a large amount of parameter data, the target sub-region where an optimal solution may appear is determined according to the divided sub-regions. When the parameter space of the target sub-region is smaller than the preset threshold, finally, a search is performed within the target sub-region to determine the final housing building parameter combination. In this way, both high search efficiency and the accuracy of determining the housing building parameters are ensured. The problem in the prior art that it is impossible to balance high efficiency and accuracy when optimizing the housing building structure is solved.

[0076] Embodiment 2

[0077] This embodiment also proposes a method for evaluating and optimizing the housing building structure. The difference between the method for evaluating and optimizing the housing building structure in this embodiment and the method for evaluating and optimizing the housing building structure in Embodiment 1 is as follows:

[0078] The step of randomly selecting the data of the housing building parameters in one grid from each sub-region as the reference housing building parameter combination of the sub-region includes:

[0079] Randomly select a preset number of unit grids from each sub-region, and obtain the housing building parameter combinations in the unit grids as the reference housing building parameter combinations;

[0080] The step of determining the target value of the sub-region according to the reference housing building parameter combination and the target function includes:

[0081] Calculate the average value of the output values obtained by inputting the reference housing building parameter combinations into the target function respectively to obtain the target value of the sub-region.

[0082] Among them, after the entire housing building parameter grid is divided into a preset number of sub-regions, for each sub-region, a certain number of unit grids are randomly selected. This number is preset and can be determined according to factors such as the complexity of the optimization problem and the expected accuracy. For each sub-region, calculate the average value of the target values corresponding to all the reference housing building parameter combinations. This average value represents the overall performance or performance of the sub-region in terms of the optimization goal. By calculating the average value, a more robust and reliable sub-region target value can be obtained.

[0083] In addition, to further improve the accuracy of optimization, when selecting the benchmark housing construction parameters, any two non-adjacent vertices in the sub-region are determined, and the connection line between the two vertices is obtained. Taking the two vertices as the starting point and the end point respectively, a curve formed by a preset frequency and a preset amplitude is used as the grid reference line. The unit grids passed by the peak points and trough points of the grid reference line are determined as the unit grids for selecting the benchmark housing construction parameters. Among them, the unit grids where the peak points and trough points are located are selected as the source of the benchmark housing construction parameters. This is because these grid points are more likely to contain optimal or sub-optimal parameter combinations, as they are located at the extreme points of the curve. In this way, we can ensure that the selected benchmark parameters are not only representative but also more likely to contain parameter combinations that have an important impact on the optimization goal.

[0084] In summary, in the housing construction structure evaluation and optimization method in the above embodiments of the present invention, a housing construction parameter grid is established through the candidate values of the housing construction structure parameters, and the parameter grid is divided. In the case of a large amount of parameter data, the target sub-region where an optimal solution may appear is determined according to the divided sub-regions. When the parameter space of the target sub-region is less than a preset threshold, finally, a search is performed within the target sub-region to determine the final housing construction parameter combination. In this way, both high search efficiency and the accuracy of determining housing construction parameters are ensured. It solves the problem in the prior art that it is impossible to balance high efficiency and accuracy when optimizing the housing construction structure.

[0085] Embodiment III

[0086] Please refer to Figure 2 , which shows the housing construction structure evaluation and optimization system proposed in Embodiment III of the present invention. The system includes:

[0087] A building module 100, configured to establish a finite element model of the housing to be optimized, obtain housing construction parameters related to the housing construction structure optimization, set the housing construction parameters in the finite element model, and perform finite element analysis to obtain the value range of the housing construction parameters;

[0088] A selection module 200, configured to select candidate values of each housing construction parameter from the value range of the housing construction parameters according to a preset rule, and establish a housing construction parameter grid according to the Cartesian product of the candidate values of each housing construction parameter;

[0089] A division module 300, configured to divide the housing construction parameter grid into a preset number of equal sub-regions according to a preset rule, and randomly select the data of the housing construction parameters in one grid from each sub-region as the benchmark housing construction parameter combination of the sub-region;

[0090] A setting module 400 is configured to set an optimization objective function for the housing building structure, determine the target value of a sub-region according to the reference housing building parameter combination and the objective function, and determine a target sub-region from among a preset number of sub-regions according to the target value;

[0091] A shrinking module 500 is configured to perform grid division on the target sub-region again, then calculate the target value by respectively selecting the reference housing building parameter combinations of the sub-regions therein to determine a new target sub-region, so as to shrink the range of the target sub-region until the parameter space of the target sub-region is smaller than a preset threshold;

[0092] An optimization module 600 is configured to determine the final housing building parameter combination according to the data in the housing building parameters within the final target sub-region by using a preset optimization algorithm and the housing building structure optimization objective function.

[0093] Further, for the above housing building structure evaluation and optimization system, wherein the step of establishing a housing building parameter grid according to the Cartesian product of the candidate values of each housing building parameter includes:

[0094] Obtain the number of sets within the Cartesian product of the candidate values of each housing building parameter, and determine the number of unit grids of the housing building parameter grid according to the number of sets within the Cartesian product;

[0095] Determine all possible combination forms of the number of rows and the number of columns according to the number of unit grids, and determine a target combination with the smallest difference between the number of rows and the number of columns from among all the combination forms of the number of rows and the number of columns;

[0096] Randomly fill the housing building parameter combinations formed by the candidate values of each housing building parameter into each unit grid to obtain the housing building parameter grid.

[0097] Further, for the above housing building structure evaluation and optimization system, wherein the step of respectively and randomly selecting the data of the housing building parameters within one grid from among the sub-regions as the reference housing building parameter combination of the sub-region includes:

[0098] Randomly select a preset number of unit grids from among the sub-regions, and obtain the housing building parameter combinations within the unit grids as the reference housing building parameter combinations;

[0099] The step of determining the target value of the sub-region according to the reference housing building parameter combination and the objective function includes:

[0100] Respectively input the reference housing building parameter combinations into the objective function, and calculate the average value of the output values obtained to obtain the target value of the sub-region.

[0101] Further, in the above housing building structure evaluation and optimization system, the step of randomly selecting a preset number of unit grids from the sub-regions and obtaining the housing building parameter combinations within the unit grids as the reference housing building parameter combinations includes:

[0102] Determine any two non-adjacent vertices in the sub-region and obtain the connection line between the two vertices;

[0103] Taking the two vertices as the starting point and the ending point respectively, and using the curve formed with a preset frequency and a preset amplitude as the grid reference line;

[0104] Determine the unit grids passed by the peak points and valley points of the grid reference line as the selected unit grids.

[0105] Further, in the above housing building structure evaluation and optimization system, the step of dividing the housing building parameter grid into a preset number of equal sub-regions according to a preset rule includes:

[0106] Obtain the central grid point in the housing building parameter grid, and use the central grid point as the origin to extend corresponding straight lines in the horizontal and vertical directions respectively to divide the housing building grid into a preset number of equal sub-regions.

[0107] Further, in the above housing building structure evaluation and optimization system, the expression of the objective function is:

[0108] ;

[0109] Wherein, α and β are the weight coefficients respectively, x is the vector of housing building parameters, Wk is the weighted coefficient of each group of structural members, Ck(x) is the k th group of structural members' cost function, Ak is the weighted coefficient of each group of structural members, P is the ratio of the actual safety factor to the safety factor required by the code for the k th group of structural members.

[0110] Further, in the above housing building structure evaluation and optimization system, the housing building parameters include beam-column section dimensions, wall thickness, and steel bar anchorage length.

[0111] The functions or operation steps realized when the above modules are executed are substantially the same as those in the above method embodiments, and will not be elaborated here.

[0112] Embodiment 4

[0113] On the other hand, the present invention also provides a readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method according to any one of the above-mentioned Embodiments 1 to 2 are implemented.

[0114] Embodiment 5

[0115] On the other hand, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the program, the steps of the method according to any one of the above-mentioned Embodiments 1 to 2 are implemented.

[0116] The technical features of each of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0117] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable storage medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0118] More specific examples (non-exhaustive list) of computer-readable storage media include the following: an electrical connection part (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable storage medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0119] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0120] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0121] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. An evaluation and optimization method for a housing building structure, characterized in that, The method includes: Establishing a finite element model of the building to be optimized, obtaining building parameters related to the structural optimization of the building, setting the building parameters in the finite element model and performing finite element analysis to obtain the value range of the building parameters; Selecting candidate values of each building parameter from the value range of the building parameters according to a preset rule, and establishing a building parameter grid according to the Cartesian product of the candidate values of each building parameter; Dividing the building parameter grid into a preset number of equal sub-regions according to a preset rule, and randomly selecting the data of the building parameters in one grid from each sub-region as the reference building parameter combination of the sub-region; Setting an optimization objective function for the building structure, determining the objective value of the sub-region according to the reference building parameter combination and the objective function, and determining the target sub-region from the preset number of sub-regions according to the objective value; Performing grid division on the target sub-region again, and then selecting the reference building parameter combination of the sub-region therein to calculate the objective value to determine a new target sub-region, so as to narrow the range of the target sub-region until the parameter space of the target sub-region is less than a preset threshold; Determining the final building parameter combination according to the data of the building parameters in the final target sub-region by using a preset optimization algorithm and the building structure optimization objective function; The step of establishing a building parameter grid according to the Cartesian product of the candidate values of each building parameter includes: Obtaining the number of sets in the Cartesian product of the candidate values of each building parameter, and determining the number of unit grids of the building parameter grid according to the number of sets in the Cartesian product; Determining all possible combinations of the number of rows and the number of columns according to the number of unit grids, and determining the target combination with the smallest difference between the number of rows and the number of columns from all combinations of the number of rows and the number of columns; Randomly filling the building parameter combinations composed of the candidate values of each building parameter into each unit grid to obtain the building parameter grid.

2. The method for evaluating and optimizing the housing building structure according to claim 1, wherein The step of randomly selecting the data of the building parameters in one grid from each sub-region as the reference building parameter combination of the sub-region includes: Randomly selecting a preset number of unit grids from the sub-region, and obtaining the building parameter combination in the unit grid as the reference building parameter combination; The step of determining the objective value of the sub-region according to the reference building parameter combination and the objective function includes: Calculating the average value of the output values obtained by respectively inputting the reference building parameter combination into the objective function to obtain the objective value of the sub-region.

3. The method for evaluating and optimizing the housing building structure according to claim 2, characterized in that, The step of randomly selecting a preset number of unit grids from the sub-region, and obtaining the building parameter combination in the unit grid as the reference building parameter combination includes: Determining any two non-adjacent vertices in the sub-region, and obtaining the connection line between the two vertices; Taking the two vertices as the starting point and the ending point respectively, and taking the curve formed with a preset frequency and a preset amplitude as the grid reference line; Determining the unit grids passed by the peak points and valley points of the grid reference line as the selected unit grids.

4. The method for evaluating and optimizing the housing building structure according to claim 1, wherein The step of dividing the housing construction parameter grid into a preset number of equal sub-regions according to a preset rule includes: Obtain the central grid point in the housing construction parameter grid, and extend corresponding straight lines in the horizontal and vertical directions respectively with the central grid point as the origin to divide the housing construction grid into a preset number of equal sub-regions.

5. The method for evaluating and optimizing the housing building structure according to claim 1, characterized in that The expression of the objective function is: ； Among them, α and β are weight coefficients respectively, x is a vector of housing construction parameters, Wk is the weighted coefficient of each group of structural components, Ck(x) is the k cost function of the Ak group of structural components, P is the weighted coefficient of each group of structural components, k is the ratio of the actual safety factor to the safety factor required by the code for the 6. The method for evaluating and optimizing the housing building structure according to any one of claims 1 to 5, characterized in that, The housing construction parameters include beam-column section sizes, wall thicknesses, and steel bar anchorage lengths.

7. A house building structure evaluation and optimization system, characterized in that, For implementing the housing construction structure evaluation and optimization method according to any one of claims 1 to 6, the system includes: A building module, configured to build a finite element model of the housing to be optimized, obtain housing construction parameters related to the housing construction structure optimization, set the housing construction parameters in the finite element model, and perform finite element analysis to obtain the value range of the housing construction parameters; A selection module, configured to select candidate values of each housing construction parameter from the value range of the housing construction parameters according to a preset rule, and establish a housing construction parameter grid according to the Cartesian product of the candidate values of each housing construction parameter; A division module, configured to divide the housing construction parameter grid into a preset number of equal sub-regions according to a preset rule, and randomly select the data of the housing construction parameters in one grid from each sub-region as the reference housing construction parameter combination of the sub-region; A setting module, configured to set an objective function for housing construction structure optimization, determine the target value of the sub-region according to the reference housing construction parameter combination and the objective function, and determine a target sub-region from the preset number of sub-regions according to the target value; A shrinking module, configured to re-divide the target sub-region into grids, then select the reference housing construction parameter combinations of the sub-regions therein respectively to calculate the target value to determine a new target sub-region, so as to shrink the range of the target sub-region until the parameter space of the target sub-region is less than a preset threshold; An optimization module, configured to determine the final housing construction parameter combination according to the data of the housing construction parameters in the final target sub-region by using a preset optimization algorithm and the housing construction structure optimization objective function.

8. A readable storage medium, on which a computer program is stored, characterized in that, When the program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 6.

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