An Offshore Risk Analysis Method Using Fuzzy Bayesian Network

Abstract

The operation of an offshore installation is associated with a high level of uncertainty because it usually operates in a dynamic environment in which technical and human and organizational malfunctions may cause possible accidents. This paper proposes a fuzzy Bayesian network (FBN) approach to model causal relationships among risk factors, which may cause possible accidents in offshore operations. The FBN model explicitly represents cause-and-effect assumptions between offshore engineering system variables that may be obscured under other modeling approaches like fuzzy reasoning and Monte Carlo risk analysis. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinions when quantitative data are lacking in early design stages with a high level of innovation or when only qualitative or vague statements can be made. The model is also a modular representation of uncertain knowledge due to randomness and vagueness. This makes the risk and safety analysis of offshore engineering systems more functional and easier in many assessment contexts. A case study of the collision risk between a floating production, storage and offloading unit and the authorized vessels due to human errors during operation is used to illustrate the application of the proposed model.

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

“…These combined models have the potential to solve difficult problems. For example, Ren et al [246] developed fuzzy Bayesian networks by incorporating fuzzy logic into Bayesian network models such that the variables can be both discrete and continuous. Table 3.…”

Dynamic Risk Assessment in Construction Projects Using Bayesian Networks

“…Ren et al [15] indicated that the most important use of BN is in revising probabilities in light of actual observations of events. It is therefore possible to calculate the probability distribution of potential safety risks and identify the most likely potential causes in occurrence of accidents.…”

Using fuzzy logic to generate conditional probabilities in Bayesian belief networks: a case study of ecological assessment

“…The first group of researchers used the direct assignment method because the number of the CPs was small and the experts could easily estimate the CPs. While the subjective estimations for the CPs are usually expressed linguistically, several studies (Li and Kao 2005;Ren et al 2009;Kao et al 2011;Li et al 2012) extend the concept of a conventional BBN to allow fuzzy probabilities (fuzzy BBN). The second group of researchers (Das 2004;Baker and Mendes 2010) devised the weighted sum algorithm, which means that experts estimate less CPs, but they must additionally assess the relative strengths (weights) of the influences of the parentnodes on a child-node.…”