Maalek Reza, Maalek Shahrokh
Endowed Chair of Digital Engineering and Construction, Karlsruhe Institute of Technology, 76131, Karlsruhe, Germany.
Digital Innovation in Construction Engineering (DICE) Technologies, Calgary, T2N 0B3, Canada.
Sci Rep. 2023 Nov 10;13(1):19591. doi: 10.1038/s41598-023-46523-z.
Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse "top four" solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements.
骨骼空间结构（SkS）系统是模块化系统，已显示出有望支持大规模定制和建筑可持续性。自第二次世界大战以来，SkS已广泛用于重建工作，特别是用于建造几何形状灵活的自由形式结构。通过采用先进的数字工程和施工实践，现有的SkS设计可以重新利用，以生成满足当代社会当前施工需求的新的最优设计。为此，本研究调查了使用现场信息建模（FIM）框架进行点云处理在现有SkS系统的数字文档编制和生成式重新设计中的应用。提出了三种新算法，用于（i）扩展FIM以包括生成式决策支持；（ii）为SkS生成竣工建筑信息模型（BIM）；（iii）将具有重复模式的SkS设计模块化，以实现最优生产和供应链管理。这些算法纳入了许多新的受人工智能启发的方法，包括用于决策支持的支持向量机（SVM）；用于邻域定义的贝叶斯优化；用于模块化的贝叶斯高斯混合聚类；以及用于选择通过非支配排序遗传算法（NSGA II）获得的顶级帕累托前沿解的蒙特卡罗随机多准则决策（MCDM）。这些算法在四个真实世界的点云数据集上进行了测试和验证，以解决两个生成式建模问题，即工程设计优化和设施选址优化。结果表明，所提出的贝叶斯邻域定义分别比粒子群和均匀采样性能高出34%和27%。所提出的基于SVM的线性特征检测分别比k均值和谱聚类性能高出56%和9%。最后，NSGA II算法与随机MCDM相结合，根据项目特定标准生成了不同的“前四名”解决方案。结果表明，该框架未来有望用于生成仅基于利益相关者要求生成新设计的生成对抗网络的训练数据集。
