Genetic algorithms in optimal detailed design of reinforced concrete members

Koumousis, VK; Arsenis, SJ

dc.contributor.author	Koumousis, VK	en
dc.contributor.author	Arsenis, SJ	en
dc.date.accessioned	2014-03-01T01:13:46Z
dc.date.available	2014-03-01T01:13:46Z
dc.date.issued	1998	en
dc.identifier.issn	10939687	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12716
dc.subject	Genetic Algorithm	en
dc.subject	Reinforced Concrete	en
dc.subject.other	Optimization	en
dc.subject.other	Reinforced concrete	en
dc.subject.other	Structural design	en
dc.subject.other	Tall buildings	en
dc.subject.other	Multistory buildings	en
dc.subject.other	Reinforcing bars	en
dc.subject.other	Genetic algorithms	en
dc.title	Genetic algorithms in optimal detailed design of reinforced concrete members	en
heal.type	journalArticle	en
heal.identifier.primary	10.1111/0885-9507.00084	en
heal.identifier.secondary	http://dx.doi.org/10.1111/0885-9507.00084	en
heal.publicationDate	1998	en
heal.abstract	Genetic algorithms emulate biologic evolutionary concepts to solve search and optimization problems. In this work, they are employed to perform the optimal detailed design of reinforced concrete members of multistory buildings. The objective is to convert the required reinforcement in square centimeters, given at a number of cross sections, into a set of reinforcing bars of specific diameter and length located at specific places along the member taking into account different criteria and rules of design practice. The anchorage lengths are taken into account, and the bars are cut at appropriate locations. For such problems, enumeration methods lead to expensive solutions, whereas genetic algorithms tend to provide near-optimal solutions in reasonable computing time. The genetic algorithms used in this work are based on a roulette wheel reproduction scheme; single, multiple-point, and uniform crossover; and constant or variable mutation schemes. A constant or variable elitist strategy is also used that passes the best designs of a generation to the next generation. The method decides the detailed design on the basis of a multicriterion objective that represents a compromise between a minimum weight design, a maximum uniformity, and the minimum number of bars for a group of members. By varying the weighting factors, designs with different characteristics result. Various parameters of the genetic algorithm are considered, and the corresponding results are presented.	en
heal.journalName	Computer-Aided Civil and Infrastructure Engineering	en
dc.identifier.doi	10.1111/0885-9507.00084	en
dc.identifier.volume	13	en
dc.identifier.issue	1	en
dc.identifier.spage	43	en
dc.identifier.epage	52	en