Identification of critical inputs in QRA studies using Monte Carlo-based sensitivity analysis

Quantitative Risk Assessment (QRA) is a well-established methodology used in different fields to identify, quantify and evaluate the risks associated with human and industrial activities. It provides a structured and extensive approach to calculate risk values, providing as a result the ability to identify major risk contributors and to assist with decision-making among others. Owed to its extensiveness and complexity, numerous decisions and assumptions are to be made throughout its execution. With the intention of harmonizing and facilitating the execution of QRAs, multiple guidelines and methodologies have been developed. However, these diverge depending on the country, region, and can further be translated differently by each risk analyst, aggravating the uncertainty in the estimation of QRA results. Consequently, QRA reliability has frequently been questioned, reiterating and analysing its – inherent – uncertainties and related implications (e.g., Rae et al., 2014). This scrutiny has led to proposing and developing diverse strategies for treating its uncertainty (e.g., Abrahamsson, 2002; Xu et al., 2023) as well as discussing the role of sensitivity analysis in QRA (e.g., Flage and Aven, 2009).

In this context, the Monte Carlo (MC) methods provide a suitable framework for performing uncertainty analysis to complex problems (Sin and Espuña, 2020) where the description of the context, e.g., possible inputs, can be highly uncertain, as is the case for QRA (Abrahamsson, 2002; Li et al., 2022). Instinctively, these methods also serve as an effective framework for sensitivity analysis, since it is closely related to uncertainty analysis. MC-based sensitivity analysis has been applied to specific sections composing QRA in Pandya et al. (2012), however, the focus of this work was set on analysing the influence of model parameters in the calculated output.
This study presents an initial framework for applying MC-based Global Sensitivity Analysis (GSA) to QRA with the goal of pinpointing the most critical input parameters driving uncertainty in risk estimates. The aim is to quantify the contribution of individual input uncertainties to the variance of the overall risk outputs, i.e., the impact that the assumptions and decisions made throughout the QRA studies may have in the calculated output.