A risk assessment approach to improve the resilience of a seaport system using Bayesian networks

Abstract

Over the years, many efforts have been focused on developing methods to design seaport systems, yet disruption still occur because of various human, technical and random natural events. Much of the available data to design these systems are highly uncertain and difficult to obtain due to the number of events with vague and imprecise parameters that need to be modelled. A systematic approach that handles both quantitative and qualitative data, as well as means of updating existing information when new knowledge becomes available is required.Resilience, which is the ability of complex systems to recover quickly after severe disruptions, has been recognised as an important characteristic of maritime operations. This paper presents a modelling approach that employs Bayesian belief networks to model various influencing variables in a seaport system. The use of Bayesian belief networks allows the influencing variables to be represented in a hierarchical structure for collaborative design and modelling of the system. Fuzzy Analytical Hierarchy Process (FAHP) is utilised to evaluate the relative influence of each influencing variable. It is envisaged that the proposed methodology could provide safety analysts with a flexible tool to implement strategies that would contribute to the resilience of maritime systems.

Risk assessment framework: Application of Bayesian Belief Networks in an ammunition delaboration project

“…The use of BNs is spreading to almost all areas: safety and reliability modeling, operational risk in finance, information retrieval, environment, medicine or, according to (Fenton & Neil, 2013) dependability, risk analysis and maintenance as well as to architecture design developing models to capture change impact analysis (Tang et al, 2007), data mining, determining and explicitly displaying the relationship among variables, representing expert knowledge and combining expert knowledge and empirical data, and identifying key uncertainties (Marcot & Penman, 2019). In addition to the previously mentioned, Bayesian Belief networks also have a variety of applications in the following fields:…”

Resilience of Critical Infrastructures: Review and Analysis of Current Approaches

“…Resilience is strongly linked to the concept of the progression of unknown transitional states not foreseen by the system. Up to now, few works have focused on taking imperfections into account in resilience management (Hosseini, Al Khaled, & Sarder, ; John, Yang, Riahi, & Wang, ; Mojtahedi, Newton, & Von Mading, ; Nogal, O'Connor, Martinez‐Pastor, & Caufiled, ; Yodo, Wang, & Zhou, ). Appropriate models should be proposed to represent imperfection with a twofold constraint: adaptation to the type of data (elicitation of expertise, data, feedback from experience) and epistemic and aleatory uncertainties.…”

Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release

“…It depicts perfect system resilience with negligible hydrocarbon release, achieving a successful performance of the operation. 5 Shuttle tanker (ST) hose connection 1.1E-01 E 6 Hose aging 1.7E-01 E 7 Joints rupture 4.5E-02 E 8 Valves control system 1.0E-03 E 9 Telemetry system 2.4E-02 E 10 Communication 6.2E-03 E 11 Controllable pitch propeller 1.8E-02 E 12 Zone classification in terms of sensitivity 1.5E-01 E 13 Adequate flow control 1.5E-02 E 14 Level monitoring 1.0E-05 E 15 Oil spillage preparedness program 1.0E-01 E 16 Avoid collision 3.1E-03 E 17 Erroneous operations 3.3E-03 E 18 Corrosion management 3.7E-03 E 19 Secure connection 9.9E-02 E 20 Malfunction of alarm system 9.0E-03 E 21 Hydrocarbon release detection system 2.3E-03 E 22 Tension cause by waves height 4.5E-02 E 23 High wind intensity 1.0E-01 E 24 Low visibility 5.5E-04 E 25 Ice management 1.0E-01 E 26 Emergency shutdown system failure 1.3E-04 E 27 Position reference system 2.0E-03 E 28 Dynamic positioning system 5.0E-04 E 29 Avoid risky maneuvering 7.9E-03 E 30 Vessel motion monitoring 1.0E-01 E 31 Distance keeping 3.8E-02 E 32 Avoid drive-off 5.4E-03 E 33 Early detection 7.2E-05 E 34 Available workforce 1.0E-01 E 35 Reactive maintenance 2.3E-03 E 36 Onsite restoration facility 1.7E-01 E 37 Manning competence 2.7E-01 E 38 Lesson learned 1.0E-01 E 39 Toolkit training 1.6E-03 E 40 Good practice guidance 1.0E-03 E 41 Lack of motivation 1.6E-03 E …”

“…(4) It has been recognized as an important characteristic of maritime operations. (5) Bakkensen et al (6) defined system resilience as the ability of a system to continue its functionality and performance efficiently over the duration of a disruptive event. Guikema et al (7) identified knowledge gaps related to the vulnerabilities, risk, and resilience of modern infrastructure systems that are critical for an improved system performance.…”