Showing 2 results for Industrial Safety
Mehran Maleki Roveshti, Zahra Naghavi-Konjin, Siavash Etemadinezhad, Jamshid Yazdani Charati,
Volume 14, Issue 1 (3-2024)
Abstract
Introduction: Steel erection is known as one of the most hazardous construction activities. From an occupational health and safety perspective, this process carries high risk. Therefore, this study aims to conduct a qualitative risk analysis of steel structure assembly and model it using the Functional Resonance Analysis Method (FRAM).
Material and Methods: In this cross-sectional study, the construction site of a high-rise building steel structure was first visited to identify the main processes involved. Then, semi-structured and open-ended interviews were conducted with 33 workers partaking in this process. Data from the interviews and process identification were entered into FRAM Model Visualiser (FMV) software to investigate and model complex relationships and interactions between daily tasks.
Results: Of the 19 major system component functions identified, four functions had potential instability and defects due to complex human, organizational, and technological function interactions. By intensifying the FRAM graphic model, risks may be imposed on the system if the interactions of these four functions are neglected. These include coordination with the experienced rigger, preparation of the tower crane, attachment of parts at the installation site, and execution of the rescue rope.
Conclusion: The findings demonstrate that conducting qualitative risk assessment and modeling the steel frame construction process using FRAM allows for an in-depth understanding of nonlinear conditions and dynamics resulting from escalating technical-social interactions. This approach enables a comprehensive analysis of system safety status.
Kazem Samimi, Esmaeil Zareie, Mohsen Omidavar, Javad Ghyasi, Parham Azimi, Mostafa Pouyakian,
Volume 15, Issue 3 (10-2025)
Abstract
Introduction: Fire risk assessment in oil storage tanks faces challenges due to incomplete, conflicting, and uncertain data, particularly when empirical evidence is limited. Traditional point-based likelihood estimates often fail to capture expert doubt and epistemic uncertainty. This study aims to develop and evaluate a novel hybrid framework combining Dempster-Shafer Theory (DST) and Bayesian Networks (BN) to improve the trustworthiness of fire risk prediction in such industrial settings.
Material and Methods: The proposed approach integrates DST to model expert uncertainty through interval probabilities (Bel–Pl) and BN to dynamically update causal relationships as new information appears. The study implements computational coding to enable DST calculations for five expert opinions across 243 scenarios, overcoming prior limitations in multi-expert modeling due to computational complexity.
Results: The hybrid DST-BN framework demonstrated superior ability to incorporate incomplete and conflicting expert data, reducing overconfidence linked to point estimates. Interval probabilities offered more trustworthy representations of epistemic uncertainty, while BN integration allowed traceable and adaptable causal modeling. The computational solution facilitated practical application of DST with multiple experts, enhancing the strength of the risk assessment.
Conclusion: This research provides an effective DST-BN hybrid methodology for assessing fire risk in fixed-roof oil tanks, improving accuracy and trustworthiness in complex industrial environments. By addressing the shortcomings of point-based methods and enabling multi-expert participation, the framework supports clearer and more defensible probabilistic inferences. Future work may focus on integrating real-time sensor data and AI-based decision systems to further strengthen dynamic risk assessment capabilities.
