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Published on: April 20, 2016
A structural framework for fire and explosion risk in EFRTs: empirical validation using EFA, CFA, and path analysis
Parisa Moshashaei1, Omid Akbarzadeh2, Mohammad Asghari-Jafarabadi3
1School of Built Environment and Design, Faculty of Art and Design, University of Canberra, Canberra, Australia.
External Floating Roof Tanks (EFRTs) face fire risks from human error and technical failures. This study developed a validated structural model to identify and mitigate these complex EFRT hazards, improving safety engineering.
Area of Science:
- Industrial Safety
- Risk Management
- Fire Engineering
Background:
- External Floating Roof Tanks (EFRTs) present significant fire and explosion risks due to combined human error, technical failures, and inadequate emergency responses.
- Current risk assessment models often rely on simplistic ranking methods, failing to capture the systemic interdependencies inherent in EFRT operations.
Purpose of the Study:
- To develop and validate a robust structural model for understanding the latent constructs associated with EFRT hazards.
- To integrate human, technical, and organizational factors into a comprehensive risk assessment framework for EFRTs.
Main Methods:
- A structured checklist of 71 indicators across 11 domains was created using expert input and literature review.
- Exploratory Factor Analysis (EFA) identified key dimensions from data collected from 285 professionals.
- Confirmatory Factor Analysis (CFA) and path analysis were employed for model validation and causal relationship assessment.
Main Results:
- EFA identified 11 factors explaining 72% of the variance in EFRT hazards.
- CFA confirmed a strong model fit (CFI=0.915, RMSEA=0.048).
- Path analysis revealed significant causal links, such as operational errors leading to technical failures and fire suppression breakdown.
Conclusions:
- The validated structural model offers a multidimensional framework for EFRT risk assessment, integrating critical human, technical, and organizational domains.
- This model provides practical insights for safety engineers to pinpoint high-leverage intervention points and develop predictive safety tools.
- The framework can be adapted for intelligent fire prevention systems in storage infrastructure.
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