Transformer-based multi-view learning for BIM clash classification: Penetration taxonomy and constructability analysis

Automated clash detection in Building Information Modeling (BIM) often produces an excessive number of results, many of which are irrelevant or non-critical to constructability. This burden requires project teams to manually filter clashes, introducing inefficiency and subjectivity. To address this limitation, this study proposed a deep learning–based framework for classifying irrelevant clashes into penetration categories that reflect their constructability implications. A taxonomy of 15 categories was developed through expert consultation, differentiating penetrations by orientation, size, and shape. Two multi-view architectures were evaluated: the Multi-View Convolutional Neural Network (MVCNN), a widely adopted baseline, and the Multi-View Vision Transformer (MVT), a state-of-the-art architecture designed to capture inter-view dependencies through attention mechanisms. A dataset generated from a federated BIM model was used to train and test both models. Results showed that MVT achieved superior performance across accuracy and F₁-score, with particular improvements in minority categories involving small or diagonal penetrations. IoU-based analysis further demonstrated that MVT attended more precisely to clash regions, enhancing interpretability. The findings confirmed that Transformer-based multi-view learning offers significant advantages for clash classification. By linking automated classification with reinforcement and constructability requirements, the proposed framework supports more reliable constructability analysis, cost estimation, and project planning.