K-means three-dimensional clustering algorithm-based wind pressure coefficient rapid partitioning method and system and storage medium

A clustering algorithm and wind pressure technology, applied in computing, computer components, electrical and digital data processing, etc., can solve problems such as large limitations in partitioning methods

Active Publication Date: 2021-03-12
CHONGQING UNIV
View PDF6 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Because there is no evidence for complex shapes, the promotion of its partition method has great limitations.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • K-means three-dimensional clustering algorithm-based wind pressure coefficient rapid partitioning method and system and storage medium
  • K-means three-dimensional clustering algorithm-based wind pressure coefficient rapid partitioning method and system and storage medium
  • K-means three-dimensional clustering algorithm-based wind pressure coefficient rapid partitioning method and system and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0152] The wind pressure coefficient fast zoning method based on the K-means three-dimensional clustering algorithm provided in this embodiment specifically includes the following steps:

[0153] Acquiring and importing wind pressure coefficient data of building surface points, said wind pressure coefficient data including spatial position information and wind pressure coefficient;

[0154] Set up K-means clustering algorithm model, and determine initial clustering parameter, described initial clustering parameter comprises cluster number K, weighting factor;

[0155] Divide the wind pressure coefficient data into K clusters according to the initial parameters;

[0156] Calculate the cluster center of the initial clustering and the distance between the cluster centers;

[0157] Calculate the minimized sum of squared errors for the clusters according to the weighting factors;

[0158] Calculate the K value range of the number of clusters;

[0159] Calculate the unified index...

Embodiment 2

[0307] This embodiment provides a wind tunnel test to describe in detail the specific process of the wind pressure coefficient rapid partition method based on the K-means three-dimensional clustering algorithm.

[0308] The wind tunnel test of Shizuishan Swimming Pool simulates the atmospheric boundary layer, and the target landform type is Class B landform stipulated in the "Code for Loading of Building Structures". According to the regulations, the envelope structure takes the basic wind pressure of the 50-year return period, and the design wind speed at the height of 10m is 32.25m / s, and meets the profile characteristics such as average wind speed and turbulence degree. The wind field information of the wind tunnel test can be found in Figure 3a and Figure 3b ;Considering the technical requirements of the project's wind tunnel test, the geometric scale ratio of the model is 1 / 100, the speed ratio is 6 / 32.25, the time ratio is 18.4 / 330, and the blocking rate is 3.56%, whic...

Embodiment 3

[0329] The present embodiment provides a wind pressure coefficient fast zoning system based on the K-means three-dimensional clustering algorithm, including a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program Implement the following steps:

[0330] Obtain wind pressure coefficient data of building surface points;

[0331] Set up K-means clustering algorithm model, and determine initial parameter, described initial parameter comprises cluster number K, weighting factor;

[0332] Divide the wind pressure coefficient data into K clusters according to the initial parameters;

[0333] Calculate the cluster center of the initial clustering and the distance between the cluster centers;

[0334] Calculate the minimized sum of squared errors for the clusters according to the weighting factors;

[0335] Calculate the K value range of the number of clusters;

[0336] Calculate the unified index para...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a K-means three-dimensional clustering algorithm-based wind pressure coefficient rapid partitioning method and system, and a storage medium. The method comprises the steps of firstly obtaining wind pressure coefficient data of building surface points; establishing a K-means clustering algorithm model, and dividing the K-means clustering algorithm model into K clusters; respectively calculating the distance between each cluster center and the cluster center; minimizing an error quadratic sum of the clusters; calculating a clustering number K value range; finally, calculating a unified index parameter value and determining an optimal K value; and outputting cluster results. According to the wind pressure coefficient rapid partitioning method provided by the invention,on the basis of one-dimensional clustering of wind pressure extreme value gradient information, a certain weight is given to each parameter in a K-means clustering algorithm, the influence of spatialposition information is considered to assist wind pressure partitioning, and a k value selection range is reduced according to a method based on error sum of squares and contour coefficients. And anoptimal k value is determined by adopting a series of clustering indexes and engineering indexes. By means of the method, the wind pressure coefficient partitioning work can be well completed.

Description

technical field [0001] The invention relates to the technical field of civil engineering analysis, in particular to a wind pressure coefficient rapid partition method, system and storage medium based on a K-means three-dimensional clustering algorithm. Background technique [0002] When observing the damage of the roof structure caused by wind load in recent years, it is found that the damage of the envelope structure is significantly more than that of the main structure under the action of strong wind. Therefore, how to more reasonably complete the task of wind resistance design of the roof envelope has become a problem that has to be considered when designing the roof structure. When observing the damage of the envelope more deeply, it was found that some wind-sensitive parts of the envelope were damaged first. For example, for large-scale roofs, the damage mainly starts from the corners and edges of the roof. Therefore, it is necessary to divide the entire roof structur...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F30/28G06K9/62G06F113/08G06F119/14
CPCG06F30/27G06F30/28G06F2113/08G06F2119/14G06F18/23213
Inventor 杨庆山刘敏殷佳齐韩啓金
Owner CHONGQING UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap