Mechanical, electrical, and plumbing (MEP) systems are crucial to a building, which directly affect the building safety, energy saving, and operational efficiency. Building information models (BIMs) help engineers to view the connection structure of MEP elements, reducing the time for reading drawings and training costs. However, existing MEP systems bring a tremendous challenge to monitoring due to issues with the complicated spatial structure, large scale, and intuitiveness. In addition, there is still a lack of feasible methods to model a representative graph in MEP systems. To address this problem, this study proposes an approach to model a directed representative graph of MEP systems using BIM data. The proposed approach contains two parts, the representative edge extraction and the direction identification. Firstly, MEP elements are converted into triangular meshes on which boundary points are extracted. Secondly, representative sets are developed to extract the representative points.
| Authors | Han, Junjun and Zhou, Xiaoping and Zhang, Weisong and Guo, Qiang and Wang, Jia and Lu, Yixin |
|---|---|
| Year | 2022 |
| Venue | Buildings (2075-5309) |
| DOI | 10.3390/buildings12060834 |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category B |
| GNN Architecture | DGCNN (heuristic — verify) |
| Graph Encoding | BIM Element Topology Graph (inferred from title) |
| AEC Task | Not yet extracted from PDF |
| Cohort | Peak Adoption (2022) |
| Dataset Size | 21 buildings (heuristic — verify) |
|---|---|
| Implementation Framework | Revit / Dynamo (heuristic — verify) |
| Primary Metric | Accuracy (heuristic — verify) |
| Primary Value | 100% (heuristic — verify) |
| Quality Assessment | 1/6 · rigor: Low · Benchmark: ✓ (heuristic — verify) |
| GNN architectures detected | DGCNN · Knowledge Graph Embedding (TransE/RotatE/DistMult) · GCN |
|---|---|
| Frameworks / libraries | Revit / Dynamo |
| Benchmark datasets referenced | PubMed |
| Dataset stats found | 21 buildings |
| Reported metrics + values | Accuracy: 100% |
| Quality (heuristic, 6-flag) | 1/6 · 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