MULTI-OBJECTIVE OPTIMIZATION DESIGN METHOD OF MULTI-RIBBED COMPOSITE WALL BASED ON GA

Hou Lina; Yao Qianfeng; Huang Wei; Jing Gang; Chen Guoxin

doi:10.13204/j.gyjz200910013

Volume 39 Issue 10

Dec. 2014

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Hou Lina, Yao Qianfeng, Huang Wei, Jing Gang, Chen Guoxin. MULTI-OBJECTIVE OPTIMIZATION DESIGN METHOD OF MULTI-RIBBED COMPOSITE WALL BASED ON GA[J]. INDUSTRIAL CONSTRUCTION, 2009, 39(10): 48-51. doi: 10.13204/j.gyjz200910013

Citation:

Hou Lina, Yao Qianfeng, Huang Wei, Jing Gang, Chen Guoxin. MULTI-OBJECTIVE OPTIMIZATION DESIGN METHOD OF MULTI-RIBBED COMPOSITE WALL BASED ON GA[J]. INDUSTRIAL CONSTRUCTION, 2009, 39(10): 48-51. doi: 10.13204/j.gyjz200910013

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MULTI-OBJECTIVE OPTIMIZATION DESIGN METHOD OF MULTI-RIBBED COMPOSITE WALL BASED ON GA

doi: 10.13204/j.gyjz200910013

1.
Institute of Civil Engineering,Xi’an University of Architecture &Technology Xi’an 710055,China

Received Date: 2008-12-16
Publish Date: 2009-10-20

Abstract

Abstract

Multi-ribbed composite wall is the main load-bearing member in the multi-ribbed slab structure(MRSS).The study on the optimization design method of the wall is an important part of the design theory for the structural system.It is discussed the multi-objective optimization design method of the multiribbed composite wall.The multi-objective optimization design mathematical model which takes both the security(shear resistance of the wall) and economy(cost of the wall) into consideration is established.Then the genetic algorithm(GA) is employed to solve the proposed model.At the same time,a case of optimization design is given.Theoretical analysis and calculated results show that applying the multi-objective optimization design method to the optimization design of the multi-ribbed composite wall can make every objective satisfactory and give consideration to both the security and economy of the wall,and this method was found to be reasonable,practicable and have some meaning to the practical engineering.
- Multi-Ribbed Composite Wall,
- Multi-objective Optimization Design,
- Genetic Algorithm

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References(8)

References

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