Background: Steel box girders are widely employed in bridge engineering due to their excellent mechanical properties and construction convenience, yet their modular design still encounters bottlenecks such as knowledge reuse difficulties and information silos. This study proposes a BIM-driven framework based on knowledge graphs and data fusion. By constructing a professional knowledge graph comprising 85 core entity types and150 semantic relationships (integrated with over 15,000 knowledge units), systematic management of design knowledge is achieved. The developed BIM reverse modeling technology improves parametric modeling efficiency by 30–40%, while the data fusion mechanism supports over 90% accuracy in design conflict detection. The intelligent decision-making system built upon this framework meets 75% of business scenario requirements while effectively assisting critical decisions such as module selection. Results demonstrate that this framework significantly enhances design collaboration efficiency and intelligence through knowledge structuring and deep data integration.
| Authors | Si, Matao and Wang, Lin and Dong, Yanjie and Chen, Yulong and Tan, Le and Han, Daguang |
|---|---|
| Year | 2025 |
| Venue | Buildings (2075-5309) |
| DOI | 10.3390/buildings15244574 |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category C |
| GNN Architecture | GNN (heuristic — verify) |
| Graph Encoding | BIM Element Topology Graph (inferred from title) |
| AEC Task | Knowledge Graph Construction (inferred from title) |
| Cohort | Mature Applications (2024–2026) |
| Implementation Framework | Revit / Dynamo, Neo4j, OWL / SPARQL (heuristic — verify) |
|---|---|
| Primary Metric | Accuracy (heuristic — verify) |
| Primary Value | 95% (heuristic — verify) |
| Quality Assessment | 1/6 · rigor: Low · Baseline: ✓ (heuristic — verify) |
| GNN architectures detected | PointNet / PointNet++ · Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Frameworks / libraries | Revit / Dynamo · Neo4j / Cypher · RDF / OWL / SPARQL |
| Reported metrics + values | Accuracy: 90% · Accuracy: 95% · Accuracy: 88% · Precision: 82% · Recall: 70% |
| 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