Reinforcement Concrete Steel Bar Trim Loss Optimization Using Metaheuristics Particle Swarm Optimization and Symbiosis Organisms Search

Renaldy Gozal, D. Prayogo
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Abstract

Steel rebars for reinforced concrete usually stated in a form of bar bending schedule. In a project, an inefficient activity of cutting steel rebar can result a waste in the form of trim loss. This can lead to profit loss and cause an impact to environment. The optimization needs to be done in order to minimize the trim loss from the steel rebar cutting. Previously, there has been many studies on steel bar trim loss optimization using metaheuristic methods as well as studies on cutting pattern generator. However, if the steel rebar variation is too many, the cutting pattern generator will produce a large number of cutting patterns. Therefore, this study aims to solve the optimization problem by producing more efficient cutting pattern while still obtaining minimum trim loss at any conditions. The data used in this research is obtained from a real-life office project. In the process, the study will be comparing the performance of both PSO and SOS from each cutting patterns generator and conditions. The performance of the two methods is assessed from its minimum, maximum, average, standard deviation and the convergence graph of each iteration. The result shows that SOS performed better in finding the minimum trim loss on undersupply condition.
基于元启发式粒子群优化和共生生物搜索的钢筋混凝土配筋损耗优化
钢筋混凝土用的钢筋通常以钢筋弯曲表的形式表示。在一个工程中,一个低效的钢筋切割活动可能导致以修剪损失的形式浪费。这可能导致利润损失,并对环境造成影响。需要进行优化,以尽量减少从钢筋切割的装饰损失。在此之前,已经有很多利用元启发式方法优化钢筋切边损耗的研究以及切边模式生成器的研究。但是,如果螺纹钢变化太多,则切割图案发生器将产生大量的切割图案。因此,本研究旨在解决优化问题,在任何条件下都能产生更高效的切削模式,同时获得最小的切边损失。本研究中使用的数据来自于一个真实的办公项目。在此过程中,研究将比较PSO和SOS在每种切割模式产生器和条件下的性能。从算法的最小值、最大值、平均值、标准差和每次迭代的收敛图来评价两种方法的性能。结果表明,在供给不足的情况下,SOS在寻找最小修剪损失方面表现较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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