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[24] Modular Design of Steel Box Girders: A BIM-Driven Framework Integrating Knowledge Graphs and Data

EBSCO – Screened Construction Safety & Knowledge Management 2025

Paper Description extracted from PDF · abstract · 141 words

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.

AuthorsSi, Matao and Wang, Lin and Dong, Yanjie and Chen, Yulong and Tan, Le and Han, Daguang
Year2025
VenueBuildings (2075-5309)
DOI10.3390/buildings15244574
Source DatabaseEBSCO (Applied Science & Technology)
Bridge-to-GNNCategory C
GNN ArchitectureGNN (heuristic — verify)
Graph EncodingBIM Element Topology Graph (inferred from title)
AEC TaskKnowledge Graph Construction (inferred from title)
CohortMature Applications (2024–2026)

Extracted Methodology heuristic — verify before citing

Implementation FrameworkRevit / Dynamo, Neo4j, OWL / SPARQL (heuristic — verify)
Primary MetricAccuracy (heuristic — verify)
Primary Value95% (heuristic — verify)
Quality Assessment1/6 · rigor: Low ·
Baseline: ✓ (heuristic — verify)

Detected Technical Stack auto-detected from PDF · regex catalogue match

GNN architectures detectedPointNet / PointNet++ · Knowledge Graph Embedding (TransE/RotatE/DistMult)
Frameworks / librariesRevit / Dynamo · Neo4j / Cypher · RDF / OWL / SPARQL
Reported metrics + valuesAccuracy: 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: —

Lineage Families

safety-km structural-frame-gnn (auto-suggested)

Enabled By / Precursors

[28]

Extended By

None in corpus

Infrastructure Dependencies

BIM (auto-suggested) Knowledge Graph (RDF/OWL/SPARQL) (auto-suggested) Revit / Dynamo (detected from PDF) Neo4j / Cypher (detected from PDF) RDF / OWL / SPARQL (detected from PDF)

Research Frontier Tags

knowledge-graph bim-graph frontier-2025+

Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026