重新利用现有的骨空间结构 (SkS) 系统设计,使用现场信息建模 (FIM) 框架在未来的建筑项目中提供生成决策支持
Reza Maalek1, Shahrokh Maalek2
1Endowed Chair of Digital Engineering and Construction, Karlsruhe Institute of Technology, 76131, Karlsruhe, Germany. reza.maalek@kit.edu.
Scientific reports
|November 10, 2023
概括
本研究介绍了使用点云处理和现场信息建模 (FIM) 重新设计骨空间结构 (SkS) 系统的新算法. 这些方法可以实现生成设计,提高建筑定制性和可持续性.
科学领域:
- 数字工程和建筑工程.
- 计算设计和优化计算设计和优化
- 可持续的建筑和城市规划
背景情况:
- 骨架空间结构 (SkS) 系统为大规模定制和建筑中的可持续性提供了模块化.
- 斯克斯在战后重建方面有着悠久的历史,使灵活和自由形式的结构成为可能.
- 先进的数字工程可以根据当代需求重新利用现有的SkS设计.
研究的目的:
- 在现场信息建模 (FIM) 框架内调查点云处理,用于数字文档和SkS系统的生成性重新设计.
- 开发和验证用于增强FIM的新型算法,包括生成决策支持,即建建筑信息建模 (BIM) 生成和SkS模块化.
主要方法:
- 开发了三个算法:FIM的生成决策支持扩展,为SkS构建的BIM生成,以及SkS设计的模块化.
- 整合了人工智能启发的方法:支持决策支持的支持向量机器 (SVM),邻里定义的贝叶斯优化,模块化的贝叶斯高斯混合集群.
- 使用蒙特卡洛随机多标准决策 (MCDM) 与非主导排序遗传算法II (NSGA II) 进行解决方案选择.
主要成果:
- 在四个现实世界点云数据集上验证了算法,用于工程设计和设施位置优化.
- 实现了卓越的性能:贝叶斯邻里定义高达34%的表现优于其他,基于SVM的线性特征检测高达56%.
- 与随机MCDM相结合的NSGA II产生了基于项目特定标准的多样化,最佳的解决方案.
结论:
- 拟议的FIM框架有效地支持数字文档和SkS系统的生成性重新设计.
- 由人工智能驱动的算法在设计优化,模块化和决策支持方面取得了显著的改进.
- 该框架显示出用于生成生成对抗网络 (GAN) 的培训数据集的前景,以实现未来的设计自动化.
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