Simulation-based Probabilistic Risk Assessment

TL;DR

This paper reviews simulation-based probabilistic risk assessment (SPRA), a methodology for evaluating risks in complex systems using various statistical and probabilistic tools, highlighting its classifications, strengths, and weaknesses.

Contribution

It provides a comprehensive review and classification of SPRA methodologies, discussing their strengths, weaknesses, and potential improvements.

Findings

SPRA methods are classified into three categories.

Strengths and weaknesses of SPRA are analyzed.

Suggestions for addressing shortcomings are discussed.

Abstract

Simulation-based probabilistic risk assessment (SPRA) is a systematic and comprehensive methodology that has been used and refined over the past few decades to evaluate the risks associated with complex systems. SPRA models are well established for cases with considerable data and system behavior information available. In this regard, multiple statistical and probabilistic tools can be used to provide a valuable assessment of dynamic probabilistic risk levels in different applications. This tutorial presents a comprehensive review of SPRA methodologies. Based on the reviewed literature, SPRA methods can be classified into three categories of dynamic probabilistic logic methods, dynamic stochastic analytical models, and hybrid discrete dynamic event and system simulation models. In this tutorial, the key strengths and weaknesses, and suggestions on ways to address real and perceived shortcomings of available SPRA methods are presented and discussed.