[22] Graph-based deep fusion for architectural text representation

Paper Description extracted from PDF · abstract · 141 words

This study introduces a framework that leverages the synergistic potential of Virtual Reality (VR) and Machine Learning (ML) to enhance graphical modeling in engineering and architectural design. Traditional clash detection methods in Building Information Modeling (BIM) systems are predominantly reactive, identifying discrepancies only after their occurrence, leading to costly and time-consuming design revisions. By integrating ML algorithms with VR-driven BIM, our approach proactively identifies and resolves clashes, as demonstrated across 28 diverse engineering projects. The results indicate a reduction in design clashes by 16% and iterative revisions by 15%, culminating in a 12% decrease in overall project timelines. This research underscores the transformative impact of combining VR and ML on additive manufacturing (AM) workflows, significantly improving efficiency and reducing the iterative nature of traditional methods. The findings highlight the framework’s scalability and adaptability, promising substantial advancements in engineering and architecture practices.