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Assessing the Effectiveness of Generative Artificial Intelligence in Hazard Identification on Construction Sites
- Truvace record
- TRV-2026-2567raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Controlled or validated comparison
- Sources
- 1 sourcePeer-reviewed
- sha256
- e67d88349be0…check it
The construction industry remains one of the most perilous, where hazard identification is often inconsistent. Hazards are still missed when teams rely mainly on traditional approaches like checklists, Job Safety Analysis (JSAs)/Job Hazard Analysis (JHAs), and individual experience. This study evaluated whether a GEN-AI-assisted approach improved hazard identification performance compared with traditional approaches, using expert-verified ground truth for scoring. A quantitative within-subjects experiment was co…
The gain
In an experiment with 51 participants, generative AI support produced significantly higher hazard identification F1 scores than traditional checklist and job hazard analysis approaches, both before and after a short training session.
What this doesn’t fix
The finding applies to this dataset and experimental setup, the training session showed no statistically significant change over time, and the authors say final AI outputs need human validation.
Sources
1 on file- Buildingsdoi:10.3390/buildings16122401
Cite this record
v1Truvace Impact Record TRV-2026-2567, v1: “Assessing the Effectiveness of Generative Artificial Intelligence in Hazard Identification on Construction Sites.” Truvace, 2026-11-12. /record/TRV-2026-2567 (accessed on the day you copy it). sha256 e67d88349be093d8…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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