The Application of Approximate Reasoning Methodologies to Offshore Engineering Design Based on Risk Assessment

Abstract

In dealing with complex and ill-defined systems of an offshore application, modelling of human reasoning for the purpose of risk assessment requires the effectiveness of a systematic logic-based approach. Floating production, storage and offloading (FPSO) installations, for example, combine traditional process technology with marine technology, and thus are quite dependent on technical design and operational safety control. Such safety-critical dependencies require novel approaches to properly analyse the risk involved. Hence, a proposed framework utilising approximate reasoning and evidential reasoning approaches is provided for modelling the assessment task. As based on fuzzy set theory, the model enables uncertainties to be described mathematically and further processed in the analysis of the structures. The forms of membership functions that could be used in representing fuzzy linguistic variables to quantify risk levels are presented. A case study of collision risk between FPSO and shuttle tanker due to technical failure during tandem offloading operation is used in this paper to illustrate the application of the proposed model. Furthermore, the obtained results from the case study provide confirmation that at various stages of offshore engineering systems design process the framework of incorporated approximate reasoning is a suited and convenient tool for attaining reliable risk analysis.

An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks

“…Some typical inputs (e.g. a single deterministic value, an interval, a triangular distribution, and a trapezoidal distribution) may be encountered due to the possible uncertainties involved (Eleye- Datubo, 2004). They are usually represented using fuzzy membership functions based on historical data or expert experience (Yang et al, 2008).…”

Marine and Offshore Safety Assessment by Incorporative Risk Modeling in a Fuzzy‐Bayesian Network of an Induced Mass Assignment Paradigm

“…This process of measuring the output attribute is in a similar fashion as those undertaken for all 11 PSFs in the antecedent of the FL rule base. The typical fuzzy set definition for the output attribute (i.e., human performance, H p ), which is represented by membership functions in which each fuzzy set overlaps to a certain degree with its neighbors (Eleye‐Datubo et al ., 2004; Sii et al ., 2004, 2005), can be expressed as shown in Fig. 7.…”

“…On the other hand, if information associated with a node exhibits uncertainty that is, vague, ambiguous, or fuzzy, then it cannot be represented precisely by a probability distribution. Thus, fuzzy logic (FL) (Zadeh, 1975) may have to be utilized to achieve a possibility distribution via a rule‐base inference engine that permits the subjective reasoning (Eleye‐Datubo et al ., 2004).…”

“…Hence, the causal formalism of using a combined fuzzy and Bayesian approach can be made possible. The resulting proposed route is given by the model name–‐“fuzzy Bayesian network.” In recent research, developments, and applications, FL and BN have both emerged as powerful and effective tools for reasoning under conditions of uncertainty (Eleye‐Datubo et al ., 2004, 2006; Sii et al ., 2004, 2005; Wang, 2006). Thus, it is certainly quite appropriate to investigate the amalgamation of both techniques.…”