Overcoming risk assessment limitations for potential fires in a multi-occupancy building
Cadena, Jaime
Hidalgo, Juan
Maluk, Cristian
Lange, David
Torero, Jose
Osorio, Andres
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Cadena J., Hidalgo J., Maluk C., Lange D., Torero J., Osorio A., 2019, Overcoming risk assessment limitations for potential fires in a multi-occupancy building, Chemical Engineering Transactions, 77, 463-468.
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Decision-making under risk has been a key issue in systems with a potential for major losses such as chemical process industries (Bhopal - 1984, Toulouse - 2001) or high occupancy buildings (World Trade Center - 2001, Grenfell Tower - 2017). For the past decades, engineering disciplines have supported risk management decision-making through the implementation of risk assessments using quantitative approaches. The popularity of this approach relates to the quantitative definition of risk given by Kaplan in 1981, who decomposed risk into a set of scenarios, probability of occurrence and consequences. Recently, research on quantitative risk assessments (QRA) has reported key limitations on identifying the set of scenarios and estimating their probability of occurrence. These limitations may lead to uncertainties of up to three orders of magnitude that affect the QRA's ability of delivering reliable information to stakeholders. This research uses an alternative definition of risk and applies it to a case study of a multi-occupancy building in the event of a fire. The proposed approach quantifies the maximum damage potential (MDP) of the system when all the active safety measures are allowed to fail, even those with low failure frequencies. The system's MDP is compared to its maximum allowable damage (MAD), which is previously defined by the stakeholders. This approach allows defining design modifications and operational rules aiding the development of the building's fire safety strategy. Finally, a comparison between the obtained results and a typical QRA is used to comment on the suitability of the proposed approach when evaluating risk in complex systems.
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