ArticleScientific reports2025
A structural framework for fire and explosion risk in EFRTs: empirical validation using EFA, CFA, and path analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Environmental Risk Awareness Among Romanian High School Students Enrolled in Food-Related Study Programs.Foods (Basel, Switzerland) · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
External Floating Roof Tanks (EFRTs) are prone to fire and explosion due to the convergence of human error, technical failure, and weak emergency response. Existing models often use ranking-based methods that fail to reflect systemic interdependencies. This study aimed to develop and validate a structural model that explains the latent constructs underlying EFRT hazards using a theory-informed, data-driven approach. A structured checklist with 71 indicators across 11 domains was developed through expert input and literature review. Data from 285 professionals were analysed using Exploratory Factor Analysis (EFA) to identify key dimensions, followed by Confirmatory Factor Analysis (CFA) and path analysis for model validation. EFA extracted 11 factors explaining 72% of the total variance. CFA showed strong fit indices (CFI = 0.915, RMSEA = 0.048). Path analysis confirmed significant causal relationships, including those from operational error to technical failure and fire suppression breakdown. A real-world case study involving 11 EFRTs demonstrated that model-predicted high-risk tanks aligned closely with expert evaluations. The final model provides a multidimensional and statistically validated framework for understanding EFRT risks, integrating human, technical, and organizational domains. This model offers practical guidance for safety engineers to identify high-leverage intervention points. It supports the development of predictive safety tools and can be adapted for integration into intelligent fire prevention systems across storage infrastructures.
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