Automasi Deteksi Konflik Struktur Arsitektural Menggunakan Algoritma Kecerdasan Buatan Berbasis Cloud
DOI:
https://doi.org/10.51903/zx5gw766Keywords:
Clash Detection, Graph Networks, Cloud Computing, OpenBIM IFC, Design CoordinationAbstract
The increasing complexity of modern construction projects has intensified coordination challenges among architectural, structural, and Mechanical-Electrical-Plumbing (MEP) disciplines. Conventional Building Information Modeling (BIM)-based clash detection methods are capable of identifying spatial conflicts but often generate high false-positive rates and require time-consuming manual verification. This study aims to develop an automated conflict detection framework by integrating Graph Neural Networks (GNN) and cloud computing within an OpenBIM Industry Foundation Classes (IFC) environment. A quantitative experimental approach was employed using a coordinated BIM model containing 44,800 three-dimensional objects from architectural, structural, and MEP disciplines. Spatial relationships among building elements were represented as graphs and analyzed using a GNN-PointNet++ architecture deployed on a cloud-based GPU environment. The results demonstrate that the proposed system successfully classified hard clash, soft clash, 4D time clash, and regulatory clash conflicts, achieving a precision of 98.67%, recall of 99.15%, and F1-Score of 98.91%. Furthermore, the framework reduced total design coordination time from 11.50 hours to 0.35 hours, corresponding to a computational efficiency of 96.95%, while maintaining 100% IFC interoperability and a false alarm rate of 1.32%. The novelty of this research lies in the integration of GNN-based spatial graph learning and cloud computing for automatic multi-type conflict classification in OpenBIM IFC models. The proposed framework improves both the accuracy and efficiency of BIM-based design coordination for digital construction environments.
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Copyright (c) 2026 Evanard Putranata, Mirelle Adisty (Author)
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