Journal of Health and Safety at Work

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Introduction: Extensive human and economic losses due to the fire accidents shows the necessity of scientifically dealing with major industrial fires, , investing in the development of fire protection technologies and determination of the most effective techniques to limit damages. This study is conducted to evaluate the effect of active and passive protection methods on reducing the level of fire risk, in the control room of a thermal power plant.

Material and Method: In the first phase of this study, fire risk level for buildings, goods, occupants and activities was calculated using Fire Risk Assessment Method for Engineering. In the second phase, from 36 sub-factors affecting risk level, three sub-factors of passive protection measures and two sub-factors of active protection measures were analyzed and modified, in reducing the fire risk level of occupants.

Result: The results of the present study showed that occupants’ fire risk level (20.64) is above the acceptable level. Correction of three sub-factors, including constant fire load, floor number, and changing the dimensions and area of the control room up to 40% and installing an automatic fire extinguishing system and training the occupants to use fire extinguishing equipment and emergency evacuation up to 33%, is effective in reducing the fire risk level.

Conclusion: Passive and active protection methods, despite their impacts on reducing the level of risk, cannot provide an acceptable level of fire safety independently. Therefore, combined use of these methods is essential to protect buildings and people, and prevent any interruption in the system’s activities.

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Introduction: Fire safety in healthcare centers is crucial due to the limited evacuation capacity of the occupants and the necessity of not disrupting the operation in these centers. In this study, the fire risk of a public hospital was evaluated using the Fire Risk Assessment Method for Engineering (FRAME). Additionally, the factors affecting fire safety in the hospital were analyzed quantitavely, and fire control strategies were presented. 
Material and Methods: First, the fire risk assessment checklist was filled in all the hospital departments. Then, the values of the factors affecting fire safety were obtained. In the next step, the fire risk for the building, occupants and activities were estimated using Excel software-FRAME. Finally, control strategies and intervention measures were presented based on the value of these factors. 
Results: In the hospital under study, 22% of the departments posed an undesirable fire risk to the building and its property. On the other hand, 90% of the departments had risk levels that were undesirable for the occupants. The results of the initial risk (R0) showed that a balance between potential fire risk and risk acceptance can be established by implementing manual fire extinguishing systems and automatic detectors in all departments. 
Conclusion: ased on the condition of the hospital studied, a balance between potential fire risk level and risk acceptance level was not established. Therefore, there is a need for fire control measures, especially fire safety measures for the occupants. The results of this study can be useful for readers and experts in interpreting fire risk assessments and presenting detailed control measures based on the risk assessment and the value of the parameters.

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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.
 

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Introduction: Hospitals represent a critical fire safety challenge. The presence of patients with limited mobility, specialized staff, and invaluable infrastructure makes them seriously vulnerable. Further, toxic smoke inhalation, as a primary product of fire, is a leading cause of mortality. To address this concern, our study intended to conduct a comprehensive fire safety assessment of a teaching hospital in Tehran (2023-2024) by integrating risk assessment with numerical modeling.
Material and Methods: This study was conducted in a teaching hospital following three consecutive steps: risk assessment, fire scenario design, and fire and smoke modeling. First, vulnerable zones were identified using the FRAME method. Next, fire scenarios and control strategies were designed based on the identified risk factors and a review of the relevant literature. Ultimately, fire and smoke transport was modeled using the CFAST software to ascertain the performance of the proposed strategies.
Results: The risk assessment pinpointed two wards with unacceptably high occupant risk levels: the inpatient ward on the 10th floor, owing to a cluster of unsafe behaviors, and the basement warehouse, owing to improper storage and inadequate emergency exit access. Fire and smoke modeling was performed for these two zones, comparing the “current situation” against a proposed “risk control strategy.” The modeling results revealed that the control strategy positively affected key life safety indicators, significantly ameliorating the Fractional Effective Dose (FED) and Heat Release Rate (HRR).
Conclusion: According to this study, a fire risk assessment provides a clear and detailed perspective on a hospital building’s fire safety. Integrating the results of the FRAME assessment with CFAST simulations results in a comprehensive understanding of the facility’s safety status. These data can be utilized to design effective emergency plans and calculate the Required Safe Egress Time (RSET), thereby preventing life-threatening harm to occupants against toxic gases.