The design process of heating, ventilation, and air conditioning (HVAC) systems is complex and time consuming due to the need to follow design codes. Since the design standards are not fixed, the final outcome often depends on the designer’s experience. The development of building information modeling (BIM) technology has made information throughout the building lifecycle more integrated. BIM-based forward design is now widely used, providing a data foundation for combining HVAC system design with machine learning. This paper proposes an unsupervised learning method based on deep graph generative models to uncover hidden design patterns and optimization strategies from the design results. We trained and validated four deep graph generative models—GAE, GNF, GAN, and diffusion—using HVAC system terminal pipeline layout data.
| Authors | Wang, Hongxin and Jin, Ruiying and Xu, Peng and Gu, Jiefan |
|---|---|
| Year | 2024 |
| Venue | Buildings (2075-5309) |
| DOI | 10.3390/buildings14113405 |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category A |
| GNN Architecture | GraphSAGE (heuristic — verify) |
| Graph Encoding | Not yet extracted from PDF |
| AEC Task | Not yet extracted from PDF |
| Cohort | Mature Applications (2024–2026) |
| Dataset Size | 28 buildings; 28 models; 1600 samples (heuristic — verify) |
|---|---|
| Implementation Framework | PyTorch, Neo4j (heuristic — verify) |
| Key Hyperparameters | batch size 8 (heuristic — verify) |
| Primary Metric | Accuracy (heuristic — verify) |
| Primary Value | 98.5% (heuristic — verify) |
| Key Finding | unsupervised learning method based on deep graph generative models to uncover hidden design patterns and optimization strategies from the design results (heuristic — verify) |
| Quality Assessment | 2/6 · rigor: Low · Benchmark: ✓Baseline: ✓ (heuristic — verify) |
| GNN architectures detected | GraphSAGE / SAGEConv · GAT (Graph Attention) · Graph VAE / VGAE · Knowledge Graph Embedding (TransE/RotatE/DistMult) · GCN |
|---|---|
| Frameworks / libraries | PyTorch · EnergyPlus · Neo4j / Cypher |
| Benchmark datasets referenced | PubMed |
| Hyperparameters (regex-detected) | batch_size=8 |
| Dataset stats found | 28 buildings · 1600 samples · 200 samples · 28 models · 87 buildings · 15 points · 92 buildings |
| Reported metrics + values | Accuracy: 90% · Accuracy: 98.5% · Accuracy: 69.8% · Accuracy: 80% · Accuracy: 55.6% · Accuracy: 75% · Precision: 75% · Precision: 50% |
| Quality (heuristic, 6-flag) | 2/6 · rigor: Low Code/Data: —Benchmark: ✓Baseline: ✓CV/Split: —Ablation: —Reproducible: — |
Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026