Neurophysiological assessment of an innovative maritime safety system in terms of ship operators' mental workload, stress, and attention in the full mission bridge simulator

The current industrial environment relies heavily on maritime transportation. Despite the continuous technological advances for the development of innovative safety software and hardware systems, there is a consistent gap in the scientific literature regarding the objective evaluation of the performance of maritime operators. The human factor is profoundly affected by changes in human performance or psychological state. The difficulty lies in the fact that the technology, tools, and protocols for investigating human performance are not fully mature or suitable for experimental investigation. The present research aims to integrate these two concepts by (i) objectively characterizing the psychological state of mariners, i.e., mental workload, stress, and attention, through their electroencephalographic (EEG) signal analysis, and (ii) validating an innovative safety framework countermeasure, defined as Human Risk-Informed Design (HURID), through the aforementioned neurophysiological approach. The proposed study involved 26 mariners within a high-fidelity bridge simulator while encountering collision risk in congested waters with and without the HURID. Subjective, behavioral, and neurophysiological data, i.e., EEG, were collected throughout the experimental activities. The results showed that the participants experienced a statistically significant higher mental workload and stress while performing the maritime activities without the HURID, while their attention level was statistically lower compared to the condition in which they performed the experiments with the HURID (all p < 0.05). Therefore, the presented study confirmed the effectiveness of the HURID during maritime operations in critical scenarios and led the way to extend the neurophysiological evaluation of the HFs of maritime operators during the performance of critical and/or standard shipboard tasks

A quantitative effectiveness analysis to improve the safety management system (SMS) implementation on-board ship

The International Safety Management (ISM) Code seeks to provide safety standards for the safe management and operation of ships. The code stipulates implementing a safety management system (SMS) on-board ship. The SMS requires the implementation of numerous procedures and forms to improve safety at the operational level on-board ship. The SMS effectiveness is vital for sustainable safety management system implementation. This paper aims to perform a comprehensive effectiveness analysis of ISM Code implementation, including forms and procedures, on-board ship to enhance the safety management system (SMS) implementation. To accomplish this, a best-worst method (BWM) is used under a fuzzy logic environment. Whilst the fuzzy BWM has yet to be applied in maritime transportation; it provides a robust analysis approach. The proposed method is improved by adopting the impact level of expert judgments in the aggregation stage of the methodology as well as performing consistency analysis. Besides its robust theoretical insight, the outcomes of the paper provide the utmost contribution to ship management companies and safety professionals for continuous improvement and moni-toring of their safety management systems implementation

A human reliability analysis for ship to ship LNG bunkering process under D-S evidence fusion HEART approach

LNG (Liquid Natural Gas) ship to ship bunkering process is quite a new concept for the maritime industry since the usage of LNG has been increasing worldwide. The LNG bunkering process poses a high risk due to human errors, while a minor error may be catastrophic. The expectation of the ship's crew is to carry out operations without any errors. Therefore, human reliability analysis (HRA) is paramount to improving operational safety during the ship to ship LNG bunkering process. In this context, this paper performs a systematic HRA under the D–S (Dempster-Shafer) evidence fusion-based HEART (human error assessment and reduction technique) approach. While the HEART quantifies human error for the tasks being performed, the extended D-S evidence fusion deals with the limitation of APOA (assessing the proportion of effect) calculation since it significantly relies on evaluating a single rater. The finding shows that human reliability for the ship to ship LNG bunkering process is 5.98E-01 and reasonable, but not at the desired level. The paper's outcomes will contribute to the utmost for LNG ship operators, safety inspectors, and ship owners to establish a safe and efficient ship to ship LNG bunkering process and minimise human error-based accidents

Numerical risk analysis of gas freeing process in oil/chemical tanker ships

Gas freeing is one of the most hazardous and critical operations on oil/chemical tankers. The process aims to remove toxic gases from tanks and bring oxygen to a healthy level (21%) in the tank atmosphere. Significant shipboard operations such as hot work or entry into the tank after tank cleaning require a successful gas-freeing process; otherwise, the consequences may be devastating for human life, cargo, ship, and the marine environment. Therefore, proactive planning and comprehensive risk assessment are essential to enhance operational safety onboard tanker ships. In this study, the potential hazards of the gas-freeing process are evaluated under the Fine–Kinney based intuitionistic fuzzy TODIM (Tomada de Decisão Interativa e Multicritério) approach. While the Fine Kinney and TODIM methods are used for calculating the risk score with the probability, exposure, and consequence parameters and determining the risk priority, intuitionistic fuzzy sets (IF) help to overcome uncertainty in human decision-making. The research findings are anticipated to guide maritime professionals, safety inspectors, and shipboard personnel in enhancing the gas-freeing process's safety and potential mitigating risks on oil/chemical tanker ships

Prediction of human–machine interface (HMI) operational errors for maritime autonomous surface ships (MASS)

The human factor is a hot topic for the maritime industry since more than 80 percent of maritime accidents are due to human error. Minimizing human error contributions in maritime transportation is vital to enhance safety levels. At this point, the maritime autonomous surface ships (MASS) concept has become one of the most significant aspects to minimize human errors. The objective of this research is to predict the human–machine interface (HMI)-based operational errors in autonomous ships to improve safety control levels. At this point, the interaction between shore-based operator and controlling system (cockpits) can be monitored and potential HMI operational errors can be predicted. This research utilizes a Success Likelihood Index Method (SLIM) under an interval type-2 fuzzy sets (IT2FSs) approach. While the SLIM provides a prediction of the human–machine interface (HMI) operational errors, the IT2FSs tackles uncertainty and vagueness in the decision-making process. The findings of this paper are expected to highlight the importance of human–machine interface (HMI) operational errors in autonomous ships not only for designers but also for operational aspects

An extended human reliability analysing under fuzzy logic environment for ship navigation

© 2022 Informa UK Limited, trading as Taylor & Francis Group.Preparation for a sea voyage is one of the fundamental aspects of navigation. Several complexities are involved during the preparation of the ship for navigation due to the nature of maritime work. At this point, analysing human-related error is of paramount importance to ensure the safety of the ship and the crew. This paper describes the principles of a methodology, namely fuzzy-based shipboard operation human reliability analysis (SOHRA), to quantitatively perform human error assessment through procedures of preparing the ship for navigation. While the SOHRA (a marine-specific HRA approach) quantifies human error, the fuzzy logic deals with ambiguity and vagueness in the human error detection problem. The findings show that the total HEP (Human error probability) is found 1.49E-01 for preparing the ship for navigation. Consequently, the paper provides practical contributions to shore-based safety professionals, ship managers, and masters of the ship since it performs a systematic human reliability assessment and enhances safety control levels in the operational aspect

HUMAN FACTORS\u27 CONTRIBUTION INTO MARITIME ACCIDENTS BY APPLYING THE SHIELD HF TAXONOMY

Despite the continuous improvement of safety measures, maritime accidents remain a concern in our society. Thus, as the literature has shown, over the last ten years, the frequency of groundings and collisions accidents in the maritime domain has increased. An official accident investigation is conducted for each serious maritime accident, however, the level of detail changes from accident to accident, hence, the details about human contributors and organizational issues are not systematically analyzed and reported in a way that makes future extraction of trends and comparisons possible. With the aim to better capture human and organizational factors, this paper proposes to utilize the Safety Human Incident &amp; Error Learning Database (SHIELD) HF Taxonomy, which was developed in the context of the European Union SAFEMODE project, in line with the key components of NASA-HFACS, HERA, and Reason\u27s Swiss Cheese Model. Therefore, in this study, ten collision and ten grounding maritime accidents reported by various maritime agencies are analyzed via the SHIELD HF Taxonomy to identify the main accident contributors, including design deficiencies. The paper demonstrates the benefits of using the SHIELD HF taxonomy for identifying the underlying causes as well as developing mitigating design solutions