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使用遗传算法为可变环境条件塑造曲线钢筋结构——迈向可持续发展

Shaping of Curvilinear Steel Bar Structures for Variable Environmental Conditions Using Genetic Algorithms-Moving towards Sustainability.

作者信息

Dzwierzynska Jolanta

机构信息

Department of Architectural Design and Engineering Graphics, Rzeszow University of Technology, Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.

出版信息

Materials (Basel). 2021 Mar 2;14(5):1167. doi: 10.3390/ma14051167.

DOI:10.3390/ma14051167
PMID:33801344
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7958325/
Abstract

The successful and effective shaping of curvilinear steel bar structures is becoming an increasingly complex and difficult task, due to the growing demands to satisfy both economic and environmental requirements. However, computer software for algorithmic-aided design makes it possible to take into account many aspects affecting structures, as early as the initial design stage. In this context, the paper presents an optimization method for shaping the curvilinear steel bar canopies of hyperbolic paraboloid and cylindroid shapes, in order to obtain effective structures adapted to external environmental conditions. The best structural solutions in terms of the structure's shape, topology and support positions are obtained as the effects of multi-criteria optimizations with the application of genetic algorithms. The following are used as the optimization criteria: minimal structure mass and minimal deflections of the structure's members, as well as their maximal utilization. Additionally, the best canopy locations in relation to the sides of the world are determined through analyzing their shadow casts for various locations, so the structures have the least impact on the surroundings. This research, with its interdisciplinary character, aims to present the possibility of applying generative shaping tools to obtain structurally effective and environment-adaptive curvilinear steel bar structures in the first phase of their design, which can support sustainable designing.

摘要

由于要满足经济和环境要求的需求不断增加,成功且有效地塑造曲线钢筋结构正变得越来越复杂和困难。然而,用于算法辅助设计的计算机软件使得早在初始设计阶段就能够考虑到影响结构的许多方面。在此背景下,本文提出了一种用于塑造双曲抛物面和柱状曲面形状的曲线钢筋雨篷的优化方法,以便获得适应外部环境条件的有效结构。通过应用遗传算法进行多准则优化,得出了在结构形状、拓扑和支撑位置方面的最佳结构解决方案。以下用作优化标准:结构质量最小、结构构件的挠度最小以及它们的最大利用率。此外,通过分析雨篷在不同位置的阴影投射来确定其相对于世界各边的最佳位置,从而使结构对周围环境的影响最小。这项具有跨学科性质的研究旨在展示在设计的第一阶段应用生成式塑造工具来获得结构有效且适应环境的曲线钢筋结构的可能性,这有助于可持续设计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/a80243f7886f/materials-14-01167-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/6cf7262ad21f/materials-14-01167-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/cb114e913e97/materials-14-01167-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/132ec7c1f07f/materials-14-01167-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/a458ae95b162/materials-14-01167-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/d99643c6e923/materials-14-01167-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/a600e63eb81e/materials-14-01167-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/9642ba7dd151/materials-14-01167-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/2f9e0338dbc3/materials-14-01167-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/0af67f98756c/materials-14-01167-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/5b991ccfcb6a/materials-14-01167-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/a80243f7886f/materials-14-01167-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/6cf7262ad21f/materials-14-01167-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/cb114e913e97/materials-14-01167-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/132ec7c1f07f/materials-14-01167-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/a458ae95b162/materials-14-01167-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/d99643c6e923/materials-14-01167-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/a600e63eb81e/materials-14-01167-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/9642ba7dd151/materials-14-01167-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/2f9e0338dbc3/materials-14-01167-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/0af67f98756c/materials-14-01167-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/5b991ccfcb6a/materials-14-01167-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a988/7958325/a80243f7886f/materials-14-01167-g011.jpg

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