(IFC) SUBMITTED: February 2021 REVISED: November 2021 PUBLISHED: January 2022 EDITOR: Robert Amor DOI: 10.36680/j.itcon.2022.005 Karim Farghaly, Lecturer, University College London; karim.farghaly@ucl.ac.uk Ranjith K. Soman, Research Associate, Imperial College London; ranjithks17@imperial.ac.uk William Collinge, Lecturer, The University of Manchester; william.collinge@manchester.ac.uk Mojgan Hadi Mosleh, Lecturer, The University of Manchester; mojgan.hadimosleh@manchester.ac.uk Patrick Manu, Reader, The University of Manchester; patrick.manu@manchester.ac.uk Clara Man Cheung, Senior Lecturer, The University of Manchester; clara.cheung@manchester.ac.uk SUMMARY: A pronounced gap often exists between expected and actual safety performance in the construction industry. The multifaceted causes of this performance gap are resulting from the misalignment between design assumptions and actual construction processes that take place on-site. In general, critical factors are rooted in the lack of interoperability around the building and work -environment information due to its heterogeneous nature.
| Authors | Farghaly, K.; Soman, R. K.; Collinge, W.; Mosleh, M.; Manu, P.; Cheung, C. |
|---|---|
| Year | 2022 |
| Venue | Journal of Information Technology in Construction (ITcon) 27, 94–108 |
| DOI | 10.36680/j.itcon.2022.005 |
| Source Database | Backtracking |
| Bridge-to-GNN | Category C |
| GNN Architecture | Knowledge Graph Embedding (inferred from title) |
| Graph Encoding | BIM Element Topology Graph (inferred from title) |
| AEC Task | Safety Management (inferred from title) |
| Cohort | Peak Adoption (2022) |
| Implementation Framework | Revit / Dynamo, OWL / SPARQL (heuristic — verify) |
|---|---|
| Primary Metric | Accuracy (heuristic — verify) |
| Primary Value | 75% (heuristic — verify) |
| Quality Assessment | 1/6 · rigor: Low · Code/Data: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Frameworks / libraries | Revit / Dynamo · RDF / OWL / SPARQL |
| Reported metrics + values | Accuracy: 75% |
| Quality (heuristic, 6-flag) | 0/6 · 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