Risk analysis has become an essential tool for process and product design, due to the need for effective individual and social risk management in order to avoid unwanted events. Nevertheless, there have been only a few studies that analyze changes in a risk scenario over time, that is, the sequence of their events is considered dynamic. The present study develops a Quantitative Risk Analysis (QRA) using a formal language, in this case, Pi-calculus, to model a risk scenario as a dynamic and open system, which was implemented as a series of discrete events. The model was validated with a probabilistic model checker such as PRISM, simplifying it to a Continuous Time Markov Chain (CMTC). In order to verify the proposed model, a dynamic QRA of a case of study was developed and compared to the static QRA. The case utilized was referenced from Probabilistic Risk Assessment for evacuees in building fires (Chu et al. 2007) from which three different risk barriers were considered: automatic sprinkler system, warning alarm system and extractor system. After averaging the most common values found in literature for both operational reliability and performance reliability for each system, their global probability of failure was calculated and used to determine the risk frequency of the overall system. For the traditional QRA the alleged frequency was calculated through a Monte Carlo simulation given that all system's barriers are at their full operation capability at the beginning of the simulation, their operation reliability is constant over time and it is independent of the number of units available to provide the service in each system. The model proposed, on the other hand, allowed considering dynamic perturbations of the system such as: number of servers available in each system, varying operational operability over time and different starting states of each system. The behavior of the risk frequency over time for these three scenarios, were studied in detail and the final results obtained analyzed. Although the static or traditional approach is the conceptual basis for the development of new risk analysis proposals, considering a risk scenario as a dynamic one allows including time as an essential variable within the risk analysis, so that new dependency relationships among different scenarios are taken into account. These perturbations can lead the system to reach the critical state, that is, the state in which all system's barriers are no longer operating, at higher frequencies that will impact in a substantial increase in the perceived risk. Pi Calculus was the mathematical tool that allowed formalizing the risk scenario as a dynamic system able to integrate its activities or events, and to develop Quantitative Risk Analysis taking advantage of the regular one. Its concurrent language was useful to simplify the model into a Continuous Time Markov Chain, in which time was the differential factor by which the generation of new dependency relationships, different to those of cause effect, was possible. For this reason Pi Calculus is a powerful computational tool useful to comprehend all kinds of complex and integrated systems, offering the possibility of extending the proposed model, if deemed appropriate, in terms of the current limits of the risk systems, environmental inputs such as external services, users decision taking like manual alarm system activation, non binary events and interaction between other systems (Domino effects).
This preliminary program is subject to change.
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Modeling of a Risk Scenario as a Dynamic System and Its Verification in a Quantitative Risk Analysis (QRA)
Risk analysis has become an essential tool for process and product design, due to the need for effective individual and social risk management in order to avoid unwanted events. Nevertheless, there have been only a few studies that analyze changes in a risk scenario over time, that is, the sequence of their events is considered dynamic. The present study develops a Quantitative Risk Analysis (QRA) using a formal language, in this case, Pi-calculus, to model a risk scenario as a dynamic and open system, which was implemented as a series of discrete events. The model was validated with a probabilistic model checker such as PRISM, simplifying it to a Continuous Time Markov Chain (CMTC). In order to verify the proposed model, a dynamic QRA of a case of study was developed and compared to the static QRA. The case utilized was referenced from Probabilistic Risk Assessment for evacuees in building fires (Chu et al. 2007) from which three different risk barriers were considered: automatic sprinkler system, warning alarm system and extractor system. After averaging the most common values found in literature for both operational reliability and performance reliability for each system, their global probability of failure was calculated and used to determine the risk frequency of the overall system. For the traditional QRA the alleged frequency was calculated through a Monte Carlo simulation given that all system's barriers are at their full operation capability at the beginning of the simulation, their operation reliability is constant over time and it is independent of the number of units available to provide the service in each system. The model proposed, on the other hand, allowed considering dynamic perturbations of the system such as: number of servers available in each system, varying operational operability over time and different starting states of each system. The behavior of the risk frequency over time for these three scenarios, were studied in detail and the final results obtained analyzed. Although the static or traditional approach is the conceptual basis for the development of new risk analysis proposals, considering a risk scenario as a dynamic one allows including time as an essential variable within the risk analysis, so that new dependency relationships among different scenarios are taken into account. These perturbations can lead the system to reach the critical state, that is, the state in which all system's barriers are no longer operating, at higher frequencies that will impact in a substantial increase in the perceived risk. Pi Calculus was the mathematical tool that allowed formalizing the risk scenario as a dynamic system able to integrate its activities or events, and to develop Quantitative Risk Analysis taking advantage of the regular one. Its concurrent language was useful to simplify the model into a Continuous Time Markov Chain, in which time was the differential factor by which the generation of new dependency relationships, different to those of cause effect, was possible. For this reason Pi Calculus is a powerful computational tool useful to comprehend all kinds of complex and integrated systems, offering the possibility of extending the proposed model, if deemed appropriate, in terms of the current limits of the risk systems, environmental inputs such as external services, users decision taking like manual alarm system activation, non binary events and interaction between other systems (Domino effects).