CN117634000B - Computational integration design method for gymnasium - Google Patents
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Abstract
The invention belongs to the field of gymnasium tests, and particularly relates to a computational integration design method for a gymnasium, which is characterized in that a gymnasium design scheme cluster is generated according to the design scale requirement in a conceptual design stage, a gymnasium bundle value evaluation frame is formulated, all design schemes are rapidly and comprehensively evaluated through a multi-layer neural network, the design and decision of the gymnasium scheme stage are assisted, the complex performance parameter integration and man-machine interaction working mode of a large-scale gymnasium is assisted, the strategy of performance parameter layered selection decision is adopted, the performance parameters of each stage are selected and integrated, the space-volume concept is selected for analysis in a preliminary space shape stage based on the research, and because the space-volume parameters have strong correlation to the cold and hot loads and acoustic reverberation, a building structure and building lighting parameters are selected in a roof refinement stage, and the building lighting parameters are selected to have certain representativeness.
Description
Technical Field
The invention belongs to the field of gym testing, and particularly relates to a computational integration design method for a gym.
Background
Gyms are important public buildings, and are required to support large activities such as various sports entertainment, and are often used as mark buildings in urban environments. The gym is composed of a bowl-shaped space formed by a competition field and an audience, and a large-span roof is used as an upper covering structure, so that the gym has obvious features, and the stadium scale directly affects the gym shape. Therefore, the stadium design scheme cluster is generated according to the stadium scale and the seat requirement, a stadium numerical evaluation framework is formulated, and all design schemes are comprehensively evaluated through a multi-layer neural network to assist in the design and decision of the stadium scheme stage.
Disclosure of Invention
The present invention is directed to a method for computationally integrating designs for gyms that solves one or more of the technical problems of the prior art, providing at least one advantageous choice or creation.
A method of computationally integrating design for a gym, the method comprising the steps of:
S100, building a gym space parameter model and generating a morphological scheme;
S200, calculating spatial performance parameters of the gym, and screening visual and morphological schemes;
s300, generating a gym roof parameter model and a scheme;
s400, calculating, visualizing and scheme screening performance parameters of the gym roof.
Further, complex performance parameter integration and man-machine interaction working modes of a large stadium adopt a strategy of performance parameter layered selection decision, main performance control factors of each design stage are selected, and data are visualized through Rhino and Grasshopper.
Preferably, for the selection and integration of performance parameters of each stage, based on the research, a space volume is selected for analysis in a preliminary space morphology stage, and a building structure and a building lighting parameter are selected in a roof refinement stage, because the space volume parameter has a strong correlation with the space volume, the cold and hot loads and the acoustic reverberation, and the selection is representative.
Further, in step S100, in the preliminary morphological design stage, a gymnasium space parameter model and a morphological scheme are generated according to the performance parameters of the large stadium and the requirements of the clients, and the method of the technical scheme includes the following steps:
S101: generating a gym parameter model cluster, building a movable field and a seat area according to the gym scale and the movable field requirement, building the movable field and the seat area into a roof plane outline meeting the scale requirement, projecting the cut seat area, using a plurality of control points, adjusting the height through the control points to control the shape of a roof, generating a parameter model of the space volume of a competition hall, and automatically generating various design schemes by using the parameter model; namely, the design scheme with the conventional building shapes and the scheme with the unconventional shapes can be used for simulating the building shapes of various gymnasiums existing in reality;
S102: the design schemes generated by the parameter models are grouped by using a clustering algorithm, and after grouping, the design schemes can be rapidly screened by each group of representative stadiums, so that the problem that a large number of similar schemes are difficult to screen in the automatic generation stadiums can be solved. And selecting design variables related to the building shape of the gymnasium in the design scheme, wherein the design variables comprise: the method comprises the steps of carrying out standardized processing on design variables by adopting numerical values of space clear height of a site center point, structural height of a roof center point and lengths of audience seats on two sides of an activity site, defining the design variables to be 0-1 through CIDIA, eliminating influences of different design variables caused by different definition fields, and classifying and integrating scheme forms by using a self-organizing map clustering method on the processed variable values.
Further, in step S200, the stadium spatial performance parameters are calculated, visualized and the morphological scheme is screened at the preliminary morphological design stage;
First round performance parameter calculation, the gymnasium performance judgement parameter of measuring in the three-dimensional model, through performance judgement parameter quick analysis and location to the performance of gymnasium, wherein, performance judgement parameter includes: the space volume of the gym and the sight quality of the gym are the space volume of a gym competition hall, the space volume of the gym comprises two parts, namely an upper part of a seat and an upper part of a sports ground, through simulation analysis, the space volume has strong correlation to cold and hot loads and acoustic reverberation, the cold and hot loads and acoustic conditions of the gym are reflected, the concept of controlling the space volume plays an important role in building energy conservation, and the sight quality of the gym is the percentage of fixed seats with optimal sight distance or clear sight distance. The two parameters are indexes which can be directly and rapidly measured in the computer three-dimensional model, and the spatial performance of the gymnasium can be rapidly positioned.
And the designer performs qualitative analysis by combining the classified stadium parameter model clusters and the first round of performance parameter calculation numerical results, and selects a shape scheme preferentially according to modeling and performance indexes.
Further, in step S300, in a roof refinement stage, a roof structure is refined according to a structure requirement, and a plurality of different windowing schemes are rapidly generated according to a windowing requirement, and the refinement decision includes the following steps:
s301, generating a gymnasium roof structure refinement model with different structure types: forming a single-layer structure net, a double-layer structure net, a single-layer net shell structure, a double-layer space grid structure and a space truss girder structure by defining size parameters by using karamba;
S302, generating a gymnasium roof refining model with different types of skylights: and (3) refining the skylight on the basis of the conceptual model, and generating a roof skylight on the basis of the existing building structure according to the type and the rate of windowing, wherein the types of the skylight are divided into a centralized skylight and a distributed skylight.
Further, in step S400, for the room cover refinement stage, the method for calculating, visualizing and screening the spatial performance parameters of the gym and determining the technical scheme includes the following steps:
S401, calculating a second-round performance simulation parameter: screening one or more roof refinement models, simulating building structures and building lighting by using performance simulation software, and storing simulation result labels, wherein parameter indexes of the building structures = structure dead weight/building area, integral strain energy dfl of the roof structures and vertical deflection span are required to meet the requirements of The numerical value of the integral strain energy dfl of the roof structure is ensured to be smaller, and the lighting parameter indexes of the building are as follows: the autonomous lighting threshold DA, DA represents the frequency of occurrence of the working surface illuminance exceeding a certain target illuminance value in the annual use time period of one point in the room, the autonomous lighting threshold ratio sDA [50% ], sDA [50% ] represents the area ratio of the part in the building reaching 300lx illuminance to more than half of the total use time, and the effective lighting ratio = range lighting/total lighting range.
S402, training a multi-layer neural network according to a performance simulation result, and inputting a roof parameter label, wherein the multi-layer neural network comprises: structure type, dimensional parameters; skylight type, window opening rate, and simulation result label, including: the method comprises the steps of outputting structural performance data and building lighting data, inputting MATLAB for training, verifying and testing, and outputting performance indexes of building structures of stadium scheme clusters and building lighting rapidly, wherein each performance index and an independent neural network model are trained to learn the mapping relation between design variables and the indexes;
The performance index output is randomly grouped into a first group and a second group, the first group of performance index output is used for determining training related parameters and performing multi-layer neural network training, the second group of performance index output is used for verifying and testing the performance of the trained neural network, wherein the related parameters comprise a roof parameter label and a simulation result label, the multi-layer neural network comprises a network input layer, an output layer and an intermediate layer, the number of neurons of the network input layer is equal to the number of types of input data, the number of neurons of the output layer is equal to the number of types of output data, the output data defaults to 1, the number of the intermediate layer and the number of neurons of each layer are freely set, and the setting of the multi-layer neural network directly influences the accuracy of approximate data of the neural network;
S403, the designer combines the classified gymnasium roof refined modeling and the second round performance indexes, wherein the second round performance indexes comprise: and (3) carrying out qualitative analysis on the calculated values of the building lighting to determine a roof thinning scheme.
The beneficial effects of the invention are as follows: the designer combines the characteristics of the preliminary morphological design and the roof fine design, the performance index is optimally selected in two layers, the autonomous learning of the machine is utilized in the second stage, the workload of the designer and the calculation amount of performance optimization calculation are greatly reduced, and the rapid integration design of the stadium morphology and performance is realized.
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The above and other features of the present invention will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, it is evident that the drawings in the following description are merely examples of the present invention, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art, in which
In the figure:
FIG. 1 is a flow chart of a method of computationally integrating designs for a gym;
FIG. 2 is a flow chart illustrating the operation of a method for a computer integrated design for a gym.
Detailed Description
The conception, specific structure, and technical effects produced by the present application will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
As shown in fig. 1 and 2, a method for a computationally integrated design for a gym, the method comprising the steps of:
S100, building a gym space parameter model and generating a morphological scheme;
S200, calculating spatial performance parameters of the gym, and screening visual and morphological schemes;
s300, generating a gym roof parameter model and a scheme;
s400, calculating, visualizing and scheme screening performance parameters of the gym roof.
Further, complex performance parameter integration and man-machine interaction working modes of a large stadium adopt a strategy of performance parameter layered selection decision, main performance control factors of each design stage are selected, and data are visualized through Rhino and Grasshopper.
Preferably, for the selection and integration of performance parameters of each stage, based on the research, a space volume is selected for analysis in a preliminary space morphology stage, and a building structure and a building lighting parameter are selected in a roof refinement stage, because the space volume parameter has a strong correlation with the space volume, the cold and hot loads and the acoustic reverberation, and the selection is representative.
Further, in step S100, in the preliminary morphological design stage, a gymnasium space parameter model and a morphological scheme are generated according to the performance parameters of the large stadium and the requirements of the clients, and the method of the technical scheme includes the following steps:
S101: generating a gym parameter model cluster, building a movable field and a seat area according to the gym scale and the movable field requirement, building the movable field and the seat area into a roof plane outline meeting the scale requirement and projecting the cut seat area, using eight control points, adjusting the height through the control points to control the shape of a roof, generating a parameter model of the space volume of a competition hall, and automatically generating various design schemes by utilizing the parameter model, namely, a scheme comprising a plurality of design schemes with conventional building shapes and a scheme with unconventional shapes, and also being capable of simulating the building shapes of various types of gymnasiums existing in reality. The competition hall comprises a movable field and a seat area;
S102: the design schemes generated by the parameter models are grouped by using a clustering algorithm, and after grouping, the design schemes can be rapidly screened by each group of representative stadiums, so that the problem that a large number of similar schemes are difficult to screen in the automatic generation stadiums can be solved. And selecting design variables related to the building shape of the gymnasium in the design scheme, wherein the design variables comprise: the method comprises the steps of carrying out standardized processing on design variables by adopting numerical values of space clear height of a site center point, structural height of a roof center point and lengths of audience seats on two sides of an activity site, defining the design variables to be 0-1 through CIDIA, eliminating influences of different design variables caused by different definition fields, and classifying and integrating scheme forms by using a self-organizing map clustering method on the processed variable values.
Further, in step S200, the stadium spatial performance parameters are calculated, visualized and the morphological scheme is screened at the preliminary morphological design stage;
First round performance parameter calculation, the gymnasium performance judgement parameter of measuring in the three-dimensional model, through performance judgement parameter quick analysis and location to the performance of gymnasium, wherein, performance judgement parameter includes: the space volume of the gym and the sight quality of the gym, wherein the space volume of the gym refers to the space volume of a gym competition hall, the space volume of the gym comprises two parts, namely a part above an agent and a part above a sports ground, through simulation analysis, the space volume has strong correlation on cold and hot loads and acoustic reverberation, the cold and hot loads and acoustic conditions of the gym are reflected, the concept of controlling the space volume plays an important role in building energy conservation, and the sight quality of the gym refers to the percentage of fixed agents with optimal sight distance or clear sight distance;
And the designer performs qualitative analysis by combining the classified stadium parameter model clusters and the first round of performance parameter calculation numerical results, and selects a shape scheme preferentially according to modeling and performance indexes.
Further, in step S300, in a roof refinement stage, a roof structure is refined according to a structure requirement, and a plurality of different windowing schemes are rapidly generated according to a windowing requirement, and the refinement decision includes the following steps:
s301, generating a gymnasium roof structure refinement model with different structure types: forming a single-layer structure net, a double-layer structure net, a single-layer net shell structure, a double-layer space grid structure and a space truss girder structure by defining size parameters by using karamba;
S302, generating a gymnasium roof refining model with different types of skylights: and (3) refining the skylight on the basis of the conceptual model, and generating a roof skylight on the basis of the existing building structure according to the type and the rate of windowing, wherein the types of the skylight are divided into a centralized skylight and a distributed skylight.
Further, in step S400, for the room cover refinement stage, the method for calculating, visualizing and screening the spatial performance parameters of the gym and determining the technical scheme includes the following steps:
S401, calculating a second-round performance simulation parameter: screening one or more roof refinement models, simulating a building structure and lighting of the building by using performance simulation software, and storing simulation result labels, wherein parameter indexes of the building structure = structure dead weight/building area, the structure dead weight also represents steel consumption of the structure, analyzing the parameter indexes of the building structure by using finite elements, controlling the parameter indexes of the building structure to enable the parameter index values of the building structure to reach the minimum value, and ensuring that the integral strain energy dfl and the vertical deflection span of the roof structure are required to meet The numerical value of the integral strain energy dfl of the roof structure is ensured to be smaller, and the lighting parameter indexes of the building are as follows: the autonomous lighting threshold DA, DA represents the frequency of occurrence of the working surface illuminance exceeding a certain target illuminance value in the annual use time period of one point in the room, the autonomous lighting threshold ratio sDA [50% ], sDA [50% ] represents the area ratio of the part in the building reaching 300lx illuminance to more than half of the total use time, and the effective lighting ratio = range lighting/total lighting range.
S402, training a multi-layer neural network according to a performance simulation result, and inputting a roof parameter label, wherein the multi-layer neural network comprises: structure type, dimensional parameters; skylight type, window opening rate, and simulation result label, including: the method comprises the steps of outputting structural performance data and building lighting data, inputting MATLAB for training, verifying and testing, and outputting performance indexes of building structures of stadium scheme clusters and building lighting rapidly, wherein each performance index and an independent neural network model are trained to learn the mapping relation between design variables and the indexes;
The performance index output is randomly grouped into a first group and a second group, the first group of performance index output is used for determining training related parameters and performing multi-layer neural network training, the second group of performance index output is used for verifying and testing the performance of the trained neural network, wherein the related parameters comprise a roof parameter label and a simulation result label, the multi-layer neural network comprises a network input layer, an output layer and an intermediate layer, the number of neurons of the network input layer is equal to the number of types of input data, the number of neurons of the output layer is equal to the number of types of output data, the output data defaults to 1, the number of the intermediate layer and the number of neurons of each layer are freely set, and the setting of the multi-layer neural network directly influences the accuracy of approximate data of the neural network;
S403, the designer combines the classified gymnasium roof refined modeling and the second round performance indexes, wherein the second round performance indexes comprise: and (3) carrying out qualitative analysis on the calculated values of the building lighting to determine a roof thinning scheme.
Preferably, each neural network model takes a model with an intermediate layer structure of 6-6-10 as a prototype, and then judges whether the number of intermediate layers or the number of neurons is required to be increased or decreased according to training, checking and testing results, and a training algorithm adopts a back propagation function;
in this process, the workload of the designer and the time spent in measurement are greatly reduced, and the design scheme of the gym can be rapidly and accurately determined through the calculation mode.
Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the invention. Furthermore, the foregoing description of the invention has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the invention that may not be presently contemplated, may represent an equivalent modification of the invention.
Claims (2)
1. A method for a computationally integrated design for a gym, the method comprising the steps of:
S100, building a gym space parameter model and generating a morphological scheme;
S200, calculating spatial performance parameters of the gym, and screening visual and morphological schemes;
s300, generating a gym roof parameter model and a scheme;
S400, calculating, visualizing and screening schemes of performance parameters of the stadium roof;
In step S100, in the preliminary morphological design stage, a gymnasium space parameter model and a morphological scheme are generated according to the performance parameters of the large stadium and the requirements of clients, and the method of the technical scheme comprises the following steps:
S101: generating a gym parameter model cluster, building a movable field and a seat area according to the gym scale and the movable field requirement, building the movable field and the seat area into a roof plane outline meeting the scale requirement and projecting the cut seat area, using a plurality of control points, adjusting the height through the control points to control the shape of a roof, generating a parameter model of the space volume of a competition hall, and automatically generating various design schemes by utilizing the parameter model, namely, a scheme comprising a plurality of conventional building forms and a scheme of unconventional forms;
s102: grouping design schemes generated by the parameter model according to the space morphology, and grouping schemes with similar morphology, so that a designer can conveniently and quickly screen the schemes; and selecting design variables related to the building shape of the gymnasium in the design scheme, wherein the design variables comprise: the method comprises the steps of carrying out standardized processing on design variables by adopting numerical values of space clear height of a site center point, structural height of a roof center point and lengths of audience seats on two sides of an activity site, defining the design variables to be between 0 and 1 through CIDIA, eliminating influences of different design variables caused by different definition fields, and classifying and integrating scheme forms by using a self-organizing map clustering method on the processed variable values;
in step S200, calculating, visualizing and screening a morphological scheme for the spatial performance parameters of the gym at the preliminary morphological design stage;
First round performance parameter calculation, the gymnasium performance judgement parameter of measuring in the three-dimensional model, through performance judgement parameter quick analysis and location to the performance of gymnasium, wherein, performance judgement parameter includes: the stadium space volume refers to the space volume of a stadium competition hall, and comprises two parts, namely a seat upper part and a stadium ground upper part, and the stadium sight quality refers to the percentage of fixed seats with optimal sight distance or clear sight distance;
The designer performs qualitative analysis by combining the classified stadium parameter model clusters and the first round of performance parameter calculation numerical results, and rapidly selects a shape scheme according to modeling and performance indexes;
In step S300, in the step of refining the roof, the structure of the roof is refined according to the structure requirement, and a plurality of different windowing schemes are rapidly generated according to the windowing requirement, and the refinement decision comprises the following steps:
s301, generating a gymnasium roof structure refinement model with different structure types: forming a single-layer structure net, a double-layer structure net, a single-layer net shell structure, a double-layer space grid structure and a space truss girder structure by defining size parameters by using karamba;
S302, generating a gymnasium roof refining model with different types of skylights: the method comprises the steps of thinning a skylight on the basis of a conceptual model, and generating a roof skylight on the basis of an existing building structure according to a windowing type and a windowing rate, wherein the skylight type is divided into a centralized skylight and a distributed skylight;
in step S400, for the room cover refinement stage, the method for calculating, visualizing and screening the spatial performance parameters of the gymnasium and determining the technical scheme includes the following steps:
S401, calculating a second-round performance simulation parameter: screening one or more kinds of roof refinement models, simulating building structures and building lighting by using performance simulation software, and storing simulation result labels, wherein parameter indexes of the building structures = structural dead weight/building area, integral strain energy dfl of the roof structures and vertical deflection span are required to meet dfl- s 3 p 0 a 0 n, the integral strain energy dfl of the roof structures is ensured to have smaller numerical value, and the lighting parameter indexes of the building are as follows: the autonomous lighting threshold DA, DA represents the occurrence frequency of the illuminance of a working surface of a point in an indoor use time period exceeding a certain target illuminance value, the autonomous lighting threshold ratio sDA [50% ], sDA [50% ] represents the area ratio of a part of the building reaching 300lx illuminance to more than half of the total use time, and the effective lighting value ratio = range lighting/total lighting range;
S402, training a multi-layer neural network according to a performance simulation result, and inputting a roof parameter label, wherein the method comprises the following steps: structure type, dimensional parameters, sunroof type, window opening rate, and simulation result label, including: the method comprises the steps of outputting structural performance data and building lighting data, inputting MATLAB for training, verifying and testing, and outputting performance indexes of building structures of stadium scheme clusters and building lighting rapidly, wherein each performance index and an independent neural network model are trained to learn the mapping relation between design variables and the indexes;
The performance index output is randomly grouped into a first group and a second group, the first group of performance index output is used for determining training related parameters and performing multi-layer neural network training, the second group of performance index output is used for verifying and testing the performance of the trained neural network, wherein the related parameters comprise a roof parameter label and a simulation result label, the multi-layer neural network comprises a network input layer, an output layer and an intermediate layer, the number of neurons of the network input layer is equal to the number of types of input data, the number of neurons of the output layer is equal to the number of types of output data, the output data defaults to 1, the number of the intermediate layer and the number of neurons of each layer are freely set, and the setting of the multi-layer neural network directly influences the accuracy of approximate data of the neural network;
S403, the designer combines the classified gymnasium roof refined modeling and the second round performance indexes, wherein the second round performance indexes comprise: and (3) building structure and building lighting, and performing qualitative analysis on calculated values of the building structure and the building lighting to determine a roof refinement scheme.
2. The method of claim 1, wherein complex performance parameters of a stadium are integrated and man-machine interaction is performed in a hierarchical decision strategy, the main performance control factors of each design stage are selected, and data are visualized by Rhino and Grasshopper.
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| CN110532687A (en) * | 2019-08-29 | 2019-12-03 | 哈尔滨工业大学 | One kind cold ground gymnasium building nature ventilation parameterization design method |
| CN114756921A (en) * | 2022-02-24 | 2022-07-15 | 同济大学 | Web end sports stadium design modeling method based on parameterized logic drive |
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| 基于数据库分析的北方大中型体育馆形体空间要素能耗关联度研究;王超;张伶伶;张民意;李科衡;;华中建筑;20190604(06);全文 * |
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