Incorporating Context into BIM-Derived Data—Leveraging Graph Neural Networks for Building Element Classification

The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of such models on classification tasks using geometric information. This research explores the hypothesis that incorporating contextual information into the ML models can improve classification accuracy. To test this, we created a graph data structure where each building element is represented as a node assigned with basic geometric information. The connections between the graph nodes (edges) represent the immediate neighbors of that node, capturing the contextual information expressed in the BIM model. We devised a process for extracting graphs from BIM files and used it to construct a graph dataset of over 42,000 building elements and used the data to train several types of ML models. We compared the classification results of models that rely only on geometry, to graph neural networks (GNNs) that leverage contextual information. This work demonstrates that graph-based models for building element classification generally outperform classic ML models. Furthermore, dividing the graphs that represent complete buildings into smaller subgraphs further improves classification accuracy. These results underscore the potential of leveraging contextual information via graphs for advancing ML capabilities in the BIM environment

Evaluating the Influence of Varied External Shading Elements on Internal Daylight Illuminances

This paper presents an assessment and comparison of the effects of static and kinetic external shading elements on the dynamic measurement of daylighting. For this purpose, we used a method and parametric tool developed previously for the design and analysis of external shading elements in buildings. The proposed approach was used to compare static and dynamic movement scenarios for achieving optimal internal adjusted useful daylight illuminances (AUDI). The current paper presents the results of a methodical analysis, which compared various types of louvers in static and dynamic operation scenarios for a typical office in a Mediterranean climate. The results show that dynamically adjusted louvers perform notably better than fixed or seasonally adjusted modes of operation. The results show that dynamic operation scenarios can increase the AUDI by up to 51%. The results also show that in some conditions the existing rules of thumb fail to predict the correct design approach to louver geometry and that the use of rules of thumb in architectural daylight design needs to be revaluated

Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts

In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality. The aim of this study was to develop a furnishing method that leverages machine learning as a means for enhancing design processes. A secondary aim was to develop a set of evaluation metrics for assessing the quality of the results generated from such methods, enabling comparisons between the performance of different models. To achieve these aims, floor plans were tagged and assembled into a comprehensive dataset that was then employed for training and evaluating three conditional generative adversarial network models (pix2pix, BicycleGAN, and SPADE) to generate furniture layouts within given room boundaries. Post-processing methods for improving the generated results were also developed. Finally, evaluation criteria that combine measures of architectural design with standard computer vision parameters were devised. Visual architectural analyses of the results confirm that the generated rooms adhere to accepted architectural standards. The numerical results indicate that BicycleGAN outperformed the two other models. Moreover, the overall results demonstrate a machine-learning workflow that can be used to augment existing interior design processes