Genetic algorithms in optimal detailed design of reinforced concrete members

TLDR: In this work, genetic algorithms are employed to perform the optimal detailed design of reinforced concrete members of multistory buildings based on a roulette wheel reproduction scheme; single, multiple‐point, and uniform crossover; and constant or variable mutation schemes.

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.