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  5. Genetic Algorithm for Optimal Placement of Steel Plate Shear Walls for Steel Frames

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Article
English
2022

Genetic Algorithm for Optimal Placement of Steel Plate Shear Walls for Steel Frames

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0 Files

English
2022
Buildings
Vol 12 (6)
DOI: 10.3390/buildings12060835

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Jianian He
Jianian He

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Jianian He
Shuhong Lin
Yicheng Li
+2 more

Abstract

Frame structures equipped with steel plate shear walls (SPSWs) have been widely used in high-rise buildings due to their good seismic performance. In this study, the strip model and combined strip model were used to analyze the performance of SPSWs. Furthermore, an improved genetic algorithm (IGA) was established to optimize the steel frame for 5, 10, and 20 stories. For each layer, layout optimization was conducted to determine the best configurations of the SPSWs, and two cases of size optimizations of conventional SPSW configurations in the frame were conducted. Results indicate the following: (i) the total weight of the five-story steel frame of layout optimization to determine the best SPSW configuration was approximately 10% lighter than those of the size optimization of conventional SPSW configurations, and this proportion gap expanded to approximately 15–25% for the 10-story and 20-story steel frames; (ii) the steel frame weight could be significantly reduced if the average percentages of story shear resisted by web plates are increased; and (iii) in the steel frame of layout optimization, the structural elements, especially the plate elements, were better utilized than those of size optimization when their inter-story drift ratios met the specification requirements. These results highlight the prominent performances of some important indicators of the design of an SPSW system with the layout optimized using IGA.

How to cite this publication

Jianian He, Shuhong Lin, Yicheng Li, Xian Dong, Shizhe Chen (2022). Genetic Algorithm for Optimal Placement of Steel Plate Shear Walls for Steel Frames. Buildings, 12(6), pp. 835-835, DOI: 10.3390/buildings12060835.

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Publication Details

Type

Article

Year

2022

Authors

5

Datasets

0

Total Files

0

Language

English

Journal

Buildings

DOI

10.3390/buildings12060835

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