Integrative risk-based assessment modelling of safety-critical marine and offshore applications

This research has first reviewed the current status and future aspects of marine and offshore safety assessment. The major problems identified in marine and offshore safety assessment in this research are associated with inappropriate treatment of uncertainty in data and human error issues during the modelling process. Following the identification of the research needs, this thesis has developed several analytical models for the safety assessment of marine and offshore systems/units. Such models can be effectively integrated into a risk-based framework using the marine formal safety assessment and offshore safety case concepts. Bayesian network (BN) and fuzzy logic (FL) approaches applicable to marine and offshore safety assessment have been proposed for systematically and effectively addressing uncertainty due to randomness and vagueness in data respectively. BN test cases for both a ship evacuation process and a collision scenario between the shuttle tanker and Floating, Production, Storage and Offloading unit (FPSO) have been produced within a cause-effect domain in which Bayes' theorem is the focal mechanism of inference processing. The proposed FL model incorporating fuzzy set theory and an evidential reasoning synthesis has been demonstrated on the FPSO-shuttle tanker collision scenario. The FL and BN models have been combined via mass assignment theory into a fuzzy-Bayesian network (FBN) in which the advantages of both are incorporated. This FBN model has then been demonstrated by addressing human error issues in a ship evacuation study using performance-shaping factors. It is concluded that the developed FL, BN and FBN models provide a flexible and transparent way of improving safety knowledge, assessments and practices in the marine and offshore applications. The outcomes have the potential to facilitate the decision-making process in a risk-based framework. Finally, the results of the research are summarised and areas where further research is required to improve the developed methodologies are outline

Realising advanced risk-based port state control inspection using data-driven Bayesian networks

In the past decades, maritime transportation not only contributes to economic prosperity, but also renders many threats to the industry, causing huge casualties and losses. As a result, various maritime safety measures have been developed, including Port State Control (PSC) inspections. In this paper, we propose a data-driven Bayesian Network (BN) based approach to analyse risk factors influencing PSC inspections, and predict the probability of vessel detention. To do so, inspection data of bulk carriers in seven major European countries from 2005 to 2008 1 in Paris MoU is collected to identify the relevant risk factors. Meanwhile, the network structure is constructed via TAN learning and subsequently validated by sensitivity analysis. The results reveal two conclusions: first, the key risk factors influencing PSC inspections include number of deficiencies, type of inspection, Recognised Organisation (RO) and vessel age. Second, the model exploits a novel way to predict the detention probabilities under different situations, which effectively help port authorities to rationalise their inspection regulations as well as allocation of the resources. Further effort will be made to conduct contrastive analysis between ‘Pre-NIR’ period and ‘Post-NIR’ period to test the impact of NIR started in 2008. © 2018 Elsevier Lt

A novel approach of collision assessment for coastal radar surveillance

For coastal radar surveillance, this paper proposes a data-driven approach to estimate a blip’s collision probability preliminarily based on two factors: the probability of it being a moving vessel and the collision potential of its position. The first factor can be determined by a Directed Acyclic Graph (DAG), whose nodes represent the blip’s characteristics, including the velocity, direction and size. Additionally, the structure and conditional probability tables of the DAG can be learned from verified samples. Subsequently, the obstacles in a waterway can be described as collision potential fields using an Artificial Potential Field model, and the corresponding coefficients can be trained in accordance with the historical vessel distribution. Then, the other factor, the positional collision potential of any position is obtained through overlapping all the collision potential fields. For simplicity, moving speeds of obstacles are considered in this research. Eventually, the two factors are characterised as two pieces of evidence, and the collision probability of a blip is estimated by combining them with Dempster’s rule. Through ranking blips on collision probabilities, those that pose high threat to safety can be picked up in advance to remind supervisors. Particularly, a good agreement between the proposed approach and the manual work was found in a preliminary test

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

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

A risk-based game model for rational inspections in port state control

This paper analyses the game relationship between port authorities and ship owners under the new inspection regime (NIR). Based on 49328 inspection reports from Paris Memorandum of Understanding (MoU) (2015-2017), we present a Bayesian Network (BN) model to determine vessel detention rates after adding company performance as a new indicator in PSC inspection. A strategic game model is formulated by incorporating the BN model outcomes. The optimal inspection rate from the game model can help improve port authority performance in PSC. An empirical study is conducted to illustrate the insights of the results and provide suggestions for port authorities

Integrative risk-based assessment modelling of safety-critical marine and offshore applications

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A spatio-temporal probabilistic model of hazard and crowd dynamics in disasters for evacuation planning

Managing the uncertainties that arise in disasters – such as ship fire – can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this challenge, we propose a novel spatio-temporal probabilistic model that integrates crowd with hazard dynamics, using a ship fire as a proof-of-concept scenario. The model is realized as a dynamic Bayesian network (DBN), supporting distinct kinds of crowd evacuation behavior – both descriptive and normative (optimal). Descriptive modeling is based on studies of physical fire models, crowd psychology models, and corresponding flow models, while we identify optimal behavior using Ant-Based Colony Optimization (ACO). Simulation results demonstrate that the DNB model allows us to track and forecast the movement of people until they escape, as the hazard develops from time step to time step. Furthermore, the ACO provides safe paths, dynamically responding to current threats