Recovering Predefined Door-Placement Rules from BIM Geometry: A Controlled Benchmark of Graph Transformers over Neural Signed Distance Function Embeddings

Design errors carried over into construction lead to rework, material waste, and avoidable embodied carbon. Rule-based compliance checking in Building Information Modelling (BIM) can identify explicit design issues. However, current rule-based engines struggle to capture complex spatial arrangements and contextual geometric relationships among surrounding components. Artificial intelligence (AI) offers a promising alternative by learning tacit, geometry-dependent conventions of expert practice directly from BIM data. Yet existing AI approaches either learn from 2D representations or rely on handcrafted geometric features that presuppose the target rules. We conduct a controlled study on a parametric benchmark derived from a single apartment plan. A graph transformer predicts where a masked door belongs along a known host wall. It learns from BIM topology and pose-conditioned neural signed distance function (SDF) embeddings, without explicit coordinate features. On unseen rigid transforms, the model achieves 79.1% side accuracy. A graph attention baseline reaches 68.9%, and a message-passing baseline stays at chance. On a second plan with a different topology, it reaches 68.2%. Doubling the training data does not raise this. Without retraining, the first plan’s model reaches 74.0% on the second. Performance on an unseen room-semantic configuration falls to chance. This is a necessary step towards learning tacit design conventions.