Marine accident learning with fuzzy cognitive maps (MALFCMs)

Statistical analysis of past accidents in maritime may demonstrate the trends for certain contributing factors in accidents, however, there is a lack of a suitable technique to model the complex interrelations between these factors. Due to aforementioned complex interrelations and insufficient information stored in accident databases, it was not possible to understand the importance of each factor in accidents, which prevented researchers from considering these factors in risk assessments. Therefore, there is a need for a capable technique to estimate the importance of each factor. The results of such a technique can be used to inform risk assessments and predict the effectiveness of risk control options. Thus, this study introduces a new technique for Marine Accident Learning with Fuzzy Cognitive Maps (MALFCMs). The novelty of MALFCM is the application of fuzzy cognitive maps (FCMs) to model the relationships of maritime accident contributors by directly learning from an accident database as well as having the ability to combine expert opinion. As each fuzzy cognitive map is derived from real occurrences supported by expert opinion, the results can be considered more objective. Thus, MALFCM may overcome the main disadvantage of fuzzy cognitive maps by eliminating or controlling the subjectivity in results. • A novel MALFCM method to weight human-contributing factors into maritime accidents has been developed. • With MALFCM method the main disadvantage of traditional FCMs is overcome. • The MALFCM method can produce logical results even by solely using information from historical data in the absence of expert judgement

Marine accident learning with fuzzy cognitive maps (MALFCMs) : a case study on fishing vessels

Despite advanced safety systems installed on ships, marine accidents still occurs at a more-or-less constant rate. This situation can be attributed to the fact that accidents occurred in a complex way and the role of humans into past accidents is not properly understood in this process. Furthermore, a number of factors are combined to result in a failure/accident but interrelations of these factors are not well understood. Therefore, shipping industry can benefit from a practical method, which is capable of considering the interrelations and identifying the importance weightings for each factor involved in an accident. Thus, in this paper, a new technique for Marine Accident Learning with Fuzzy Cognitive Maps (MALFCMs) is developed and demonstrated. The method utilises Fuzzy Cognitive Maps (FCMs) to model the relationships by also integrating information from an accident database. By applying accident data instead of expert judgement, MALFCMs may overcome the main disadvantage of FCMs by controlling the subjectivity in results attributed to expert opinion. Within this study, MALFCMs is applied to fishing vessels accident data, in order to compare the results with the findings of an existing report provided by the European Maritime Safety Agency (EMSA). In order to make this comparison, Collision and Fire/explosion accidents were selected and comparatively analysed in this paper. Our study shows that MALFCM can produce results, which are in line with the findings from aforementioned EMSA report

Marine accident learning with Fuzzy Cognitive Maps : a method to model and weight human-related contributing factors into maritime accidents

Previous statistical maritime accident studies are focused on identifying human factors. However, the previous studies were not capable of modelling the complex interrelations that exist between these factors. As accidents are complex processes, researchers fail to agree on the contribution of each human factor. Therefore, in this research study, a new Fuzzy Cognitive Map (FCM)-based technique known as MALFCMs has been introduced and applied. Its novelty is the application of FCM concepts to model the relationships of accident contributors by combining historic accident data with expert opinion. Our approach is capable of integrating information obtained from real occurrences, therefore, the results can be considered more objective. Thus, in this paper, MALFCMs was applied to grounding/stranding accidents in general-cargo vessels, revealing that ‘unprofessional behavior’, ‘lack of training’, and ‘inadequate leadership and supervision’ are the most critical factors, with a normalised importance weighting of 13.25%, 13.24%, and 13.24% respectively

Application of fuzzy cognitive maps to investigate the contributors of maritime collision accidents

Maritime transport has been striving to reduce ship accidents since its origins, which results in loss of lives or properties and damage for the environment. Hence, a continuous effort to enhance safety is a crucial requirement for the maritime sector, for which several approaches have been tried for the past years. This paper presents the first results of a study which aim is to assess the factors affecting collision accidents in order to enhance safety and resilience. This aim is achieved by using Fuzzy Cognitive Maps (FCMs) method, which consider and evaluates importance of these factor by calculating and assigning individual weights to them. Moreover, FCM appears to be a suitable approach since it can take into account both, fuzzy data and past accidents experiences. Hence, in this paper with the help of FCM, past accidents from the Marine Accident Investigation Branch (MAIB) database regarding collision are analysed to identify the contributors of collision accidents and their FCM weightings

Application of card sorting approach to classify human factors of past maritime accidents

Maritime accidents are complex processes in which many factors are involved and contribute to accident development. In order to capture underlying factors in accidents, countries adapted an accident investigation system with the aim of learning from these rare events and prevent similar occurrences in the future. Often these accident investigation reports are converted into databases, which lack a concise and user-friendly classification system, as a result there are a lot of inadequacies in data-collection and tagging procedures. Therefore, the authors propose to apply an approach to classify human factors (HFs) appeared in past maritime accidents, aiming to develop a set of HFs categories which can be used for accidents learning. For this purpose, an accident database was obtained and a two-stage approach is adapted to conduct analysis: first, an open card-sorting case study is organised to group the HFs extracted from an historical accident database. Second, a hybrid card-sorting method is utilized to fully achieve the classification of HFs. Our study revealed issues where HFs are weakly defined and similar factors are duplicated by investigators who populate the database. High level categories were developed and presented which covers great majority of HFs concerns involved in accidents

Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

Addressing safety is considered a priority starting from the design stage of any vessel until end-of-life. However, despite all safety measures developed, accidents are still occurring. This is a consequence of the complex nature of shipping accidents where too many factors are involved including human factors. Therefore, there is a need for a practical method, which can identify the importance weightings for each contributing factor involved in accidents. As a result, by identifying the importance weightings for each factor, risk assessments can be informed, and risk control options can be developed and implemented more effectively. To this end, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) approach incorporated with Bayesian networks (BNs) is suggested and applied in this study. The MALFCM approach is based on the concept and principles of fuzzy cognitive maps (FCMs) to represent the interrelations amongst accident contributor factors. Thus, MALFCM allows identifying the importance weightings for each factor involved in an accident, which can serve as prior failure probabilities within BNs. Hence, in this study, a specific accident will be investigated with the proposed MALFCM approach

Application of data-mining techniques to predict and rank maritime non-conformities in tanker shipping companies using accident inspection reports

The application of data mining techniques is an extended practice in numerous domains; however, within the context of maritime inspections, the aforementioned methods are rarely applied. Thus, the application of data-mining techniques for the prediction and ranking of non-conformities identified during vessel inspections could be of significant managerial contribution to the safety of shipping companies, as non-conformities could potentially lead to real accidents if not addressed adequately. Hence, specific data mining methods are investigated and applied in this paper to predict and rank non-conformities on oil tankers using a database recorded by tanker shipping companies in Turkey from 2006 to 2019. The results of this study reveal that specific non-conformities, for instance, inadequate ice operations or inadequate general appearance and condition of hull, superstructure and external weather decks, are not company-based problems, rather they are industry wide issues for all tanker shipping companies

A practical application of the Hierarchical Task Analysis (HTA) and Human Error Assessment and Reduction Technique (HEART) to identify the major errors with mitigating actions taken after fire detection onboard passenger vessels

Fire onboard passenger ships is a major hazard not only for the personnel, passengers, and the environment, but also for the vessel itself. Therefore, the response actions carried out by crewmembers after a fire has been detected onboard a passenger vessel are of outermost importance. SAFEMODE project aims to promote contemporary safety thinking through a collection of carefully selected Human Factors (HFs) Fact Sheets that includes the most-known HFs techniques for accident investigations, to help accident investigators and safety managers within maritime organisations. Therefore, this paper proposes to apply two of the above-mentioned Fact Sheets, namely Hierarchical Task Analysis (HTA) and Human Error Assessment and Reduction Technique (HEART). Hence, this paper initially demonstrates how HTA can be applied to model the human response actions to a fire onboard a passenger vessel, and secondly, it utilises a systematic human error reduction and prediction approach, namely HEART, to predict and quantify which errors are likely to occur. Results from this paper reveal that six human response errors are most likely to occur, with a Human Error Probability of 0.16 according to the HEART analysis. Finally, this paper also suggests remedial measures to mitigate each error identified

Application of a SOAM-based systemic method to conduct a comprehensive analysis of a maritime accident

The current trends in maritime accidents worldwide are often linked to environmental, economic, and human consequences, such as oil spills, insurance costs, or human injuries or fatalities. Despite the continuous improvement in safety measures, maritime accidents are still occurring, and this remains a major concern in our society. The main aim of this paper is to contribute to the current safety measures by identifying the significant human and organisational accident contributors, and therefore, reducing the current accident trends. With this aim, this paper proposes to apply a systemic method known as the Systemic Occurrence Analysis Methodology (SOAM) for the first time in the maritime domain. SOAM, which is a 'Swiss-cheese" based organisational technique for analysing incidents and accidents, was developed by EUROCONTROL for the aviation domain. SOAM methodology is fully applied to three maritime accident collisions to identify the major accident contributors, absent or failed barriers, human involvement, and contextual conditions

SEAbrary : an electronic repository of maritime safety knowledge

PURPOSE: SEAbrary is an electronic repository of maritime safety knowledge related to maritime operations, management and design with a focus on Human Factors (HFs). It is also a portal, a common entry point, that enables users to access the safety data made available on the websites of various maritime organizations - regulators, service providers and industries. SEAbrary's main objective is to build a single point of source for maritime safety knowledge by making globally available and accessible the safety knowledge accumulated by various maritime organisations, entities and initiatives. METHOD: SEAbrary adopts the concept of Media-wiki products – anyone, who has content access, can comment, propose modification to an existing article, suggest a new topic or submit a draft article. However, there is an important difference that distinguishes SEAbrary from other wikies. A robust content management and control process supported by appropriate user rights management ensures the needed quality, credibility and consistency of stored safety data. FINDINGS: SEAbrary is being developed as part of Safemode project that is founded by European Commission. It is fully developed and User Acceptance Tests (UAT) are conducted through the Safemode project end users. CONCLUSION: Initial validation results show that the proposed SEAbrary web platform is working effectively. This presentation will demostrate the current version of the platform and its functionalities to diseminate it to the maritime audiance