Half-century research developments in maritime accidents: Future directions

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship

The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident's probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

Ship-Fire Net: An Improved YOLOv8 Algorithm for Ship Fire Detection

Ship fire may result in significant damage to its structure and large economic loss. Hence, the prompt identification of fires is essential in order to provide prompt reactions and effective mitigation strategies. However, conventional detection systems exhibit limited efficacy and accuracy in detecting targets, which has been mostly attributed to limitations imposed by distance constraints and the motion of ships. Although the development of deep learning algorithms provides a potential solution, the computational complexity of ship fire detection algorithm pose significant challenges. To solve this, this paper proposes a lightweight ship fire detection algorithm based on YOLOv8n. Initially, a dataset, including more than 4000 unduplicated images and their labels, is established before training. In order to ensure the performance of algorithms, both fire inside ship rooms and also fire on board are considered. Then after tests, YOLOv8n is selected as the model with the best performance and fastest speed from among several advanced object detection algorithms. GhostnetV2-C2F is then inserted in the backbone of the algorithm for long-range attention with inexpensive operation. In addition, spatial and channel reconstruction convolution (SCConv) is used to reduce redundant features with significantly lower complexity and computational costs for real-time ship fire detection. For the neck part, omni-dimensional dynamic convolution is used for the multi-dimensional attention mechanism, which also lowers the parameters. After these improvements, a lighter and more accurate YOLOv8n algorithm, called Ship-Fire Net, was proposed. The proposed method exceeds 0.93, both in precision and recall for fire and smoke detection in ships. In addition, the mAP@0.5 reaches about 0.9. Despite the improvement in accuracy, Ship-Fire Net also has fewer parameters and lower FLOPs compared to the original, which accelerates its detection speed. The FPS of Ship-Fire Net also reaches 286, which is helpful for real-time ship fire monitoring.

Scientific awareness of marine accidents in Europe: A bibliometric and correspondence analysis

Marine accidents are a significant issue that can lead to loss of life, damage to ships and cargo, and harm to the marine environment. To gain better understanding of scientific awareness of marine accidents occurred in European countries, this study conducted a bibliometric and correspondence analysis of the scientific literature. Bibliometric analysis was employed to examine various publications, which were released during the period between 2012 and December 2022. Moreover, correspondence analysis was used to classify and analyze marine accidents based on the severity and the consequence of the accidents. The findings indicate that scientific awareness of researchers in countries where serious and very serious marine accidents occur is also high. However, Norway stands out as the country with the highest scientific awareness of researchers despite experiencing marine accidents with less serious. The most significant factor contributing to the prominence of researchers here is their collaborations with researchers from other countries. Overall, this study sheds light on the need for further research and action to improve marine accident prevention. Collaborative efforts involving researchers, maritime stakeholders, and policymakers are necessary to address the complex challenges of marine accidents and to ensure the maritime safety and protection of the marine environment.

Maritime autonomous surface ships: can we learn from unmanned aerial vehicle incidents using the perceptual cycle model?

Interest in Maritime Autonomous Surface Ships (MASS) is increasing as it is predicted that they can bring improved safety, performance and operational capabilities. However, their introduction is associated with a number of enduring Human Factors challenges (e.g. difficulties monitoring automated systems) for human operators, with their 'remoteness' in shore-side control centres exacerbating issues. This paper aims to investigate underlying decision-making processes of operators of uncrewed vehicles using the theoretical foundation of the Perceptual Cycle Model (PCM). A case study of an Unmanned Aerial Vehicle (UAV) accident has been chosen as it bears similarities to the operation of MASS through means of a ground-based control centre. Two PCMs were developed; one to demonstrate what actually happened and one to demonstrate what should have happened. Comparing the models demonstrates the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when operating from remote control centres. Practitioner Summary: To investigate underlying decision-making processes of operators of uncrewed vehicles using the Perceptual Cycle Model, by using an UAV accident case study. The findings showed the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when monitoring uncrewed systems from remote control centres.

Maritime accidents in the Yangtze River: A time series analysis for 2011-2020

The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.

Have Non-Native English-Speaking Marine Cadet Engineers Been Educated Appropriately?

Freight transport via ships is the cheapest and most effective way to transfer more than 80% of the global cargo volume. Seafarers have always been multinational, and accordingly, non-English-speaking crew members are becoming an increasing presence on board. Although marine engineers comprise half of the crews among all seafarers on board ships, Standard English guides, such as SMCP for navigation officers designed to reduce communication barriers, are unavailable for marine engineers. IMO conventions require marine engineers to possess adequate English skills. However, marine accidents due to inappropriate communication between crew members continue to occur. In this paper, 185 marine engineer cadets enrolled in two universities who had completed 12 months of training on a commercial ship or school training ship were surveyed in terms of the adequacy of English courses for marine engineers in class. This paper investigated whether the marine engineer English subjects are reviewed and analyzed and whether the English examination for the Certificate of Competence is suitable for the content taught in international maritime instruments and for the actual work of engineers. Finally, this paper aimed to establish a need to develop Standard English for engineers.

TAR-Based Domino Effect Model for Maritime Accidents

To thoroughly figure out the distribution and formation mechanism of maritime accidents, this study proposes a domino effect model based on temporal association rules (TAR) to analyze and mine the secrets behind the accident—the formation mechanism of accident chains. In this study, the British Marine Accident Investigation Branch (MAIB) accident reports are gathered and examined. Of which, Ro-Ro ships, general cargo ships, and container ships are the top three ship types discussed. The domino effect model is applied to the detected accidents, yielding a series of results. These show that the resulting values from unsafe working practices to death while working are very high and are 8.622 (Ro-Ro ship), 5.920 (General cargo ship) and 6.441 (Container ship), respectively. It indicates that unsafe working practices are very prone to accidents involving death while working. The approach is ubiquitous, and the accident chains compiled from them may be widely employed in marine accident prevention and proactive safety management.

Research on Location-Routing Problem of Maritime Emergency Materials Distribution Based on Bi-Level Programming

To solve the location-routing problem of maritime emergency materials distribution (MEMD-LRP) of multi-agent joint decision, a bi-level programming model of MEMD-LRP is constructed. The model is established on the premise of considering the rescue time window and emergency materials distribution priority, aiming at the strong regional characteristics of maritime accidents, strong timeliness of maritime rescue, great difficulty of rescue, high cost of rescue, and so on. From the point of view of the emergency management department, the upper level makes the location decision to minimize the construction cost of the shore-based emergency materials reserves and the time satisfaction loss cost of the accident point. From the point of view of the commercial rescue unit, the lower level plans the routes by minimizing the cost of emergency material distribution, ship transportation, ship dispatch, and time penalty. A hybrid algorithm of the ant colony and tabu search is designed to solve the model, and an example is designed and analyzed based on the case of the Bohai Sea to verify the effectiveness of the model and algorithm. Modeling and algorithm design based on bi-level programming can help decision-makers at different levels in maritime emergency logistics systems to make more scientific and reasonable decisions to improve the service capability of the maritime emergency logistics system.

Risk Scenario Evaluation for Intelligent Ships by Mapping Hierarchical Holographic Modeling into Risk Filtering, Ranking and Management

To identify and screen the risk scenarios for the navigation risk of intelligent ships, the analysis and evaluation of navigational risks were performed in this study. Risk scenarios were developed and evaluated by mapping the hierarchical holographic modeling (HHM) into risk filtering, ranking and management (RFRM). In detail, considering the insignificant influences of some factors on navigational activities, risk factors were filtered and ranked using the RFRM model. Seven final factors were successfully determined, including traffic flow, navigation environment understanding, ship–shore interaction capabilities, target recognition capabilities, communication equipment reliabilities, professional skills, and situation judgments. The results indicated that cargo security can be guaranteed by following navigational risk identification and screening steps, and thus our findings provide theoretical guidance for the dynamic management of maritime organizations and ship companies. In addition, the proposed methodology is desirable for making predictions on maritime traffic risks.

The Development of a Bayesian Network Framework with Model Validation for Maritime Accident Risk Factor Assessment

An integrative approach to maritime accident risk factor assessment in accordance with formal safety assessment is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation of modelling, verification, and validation. The methodology for probabilistic modelling with BNs is well known and its application to risk assessment is based on the model verified though sensitivity analysis only, while validation of the model is often omitted due to a lack of established evaluation measures applicable to scarce real-world data. For this reason, in this work, the modified Lyapunov divergence measure is proposed as a novel quantitative assessor that can be efficiently exploited on an individual accident scenario for contributing causal factor identification, and thus can serve as the measure for validation of the developed expert elicited BN. The proposed framework and its approach are showcased for maritime grounding of small passenger ships in the Adriatic, with the complete grounding model disclosed, quantitative validation performed, and its utilization for causal factor identification and risk factor ranking presented. The data from two real-world grounding cases demonstrate the explanatory capabilities of the developed approach.

Quantitative Analysis on Risk Influencing Factors in the Jiangsu Segment of the Yangtze River

Quantitative risk influencing factors (RIFs) are proposed, using the Conjugate Bayesian update approach to analyze 945 collision accidents and incidents cases from the Jiangsu Segment of the Yangtze River over five years from 2012 to 2016. The accident probability is compared under a pairwise comparison mode in order to reflect the relative risk between accidental situations. The Bayesian update mode is constructed to quantitatively evaluate the relative importance of different RIFs. The riskiest segment of Jiangsu Waterways as well the main causations of collisions are identified based on the distributions of collision risk in the six segments of the waterways. The results can support managers to develop the most effective policies to mitigate the collision risk.

Identifying Cognitive Mechanism Underlying Situation Awareness of Pilots' Unsafe Behaviors Using Quantitative Modeling

Situation awareness (SA) of pilots’ unsafe behavior can ensure safety onboard. Thus, the cognitive mechanism that controls the SA leading to unsafe behavior must be articulated. This study employs the SA model and theory of planned behavior (TPB) to articulate a quantitative model of ship safe piloting. Firstly, the hierarchical classification framework of unsafe behaviors was constructed as an analytical foundation for rational and unconscious behaviors in sight of cognitive processes, and then the measurement elements of the cognitive mechanisms for behaviors were identified. Subsequently, based on the structural model, a hypothetical model of the cognitive path for unsafe behaviors was proposed by using the extended TPB, where there are four independent variables (i.e., attitude (ATD), subjective norm (SN), and perceived behavioral control (PBC)), one mediating variables (i.e., SA) and two dependent variables (i.e., behavioral intention (BI) and unsafe behaviors (BE)). Finally, this hypothetical model was analyzed with the data resources from extended TPB questionnaire of 295 pilots. Analysis results show that relationships of causation and mediation in the cognitive mechanism are in line with the behavior pattern and SA have a pronounced mediating effect and a strong relevance to the causal chain of extended TPB framework. This study integrated the SA three-level model to understand the motivation–cognition–action–feedback (MCAF) mechanism of pilots’ unsafe behaviors under cognitive mode of information processing through structural model. It would make a valuable contribution to the assessment and intervention of safety behaviors, and provide a basic framework for monitoring the situation awareness of pilot by man-machine interactive measurement technology in the future.

Ecological consequences of space rocket accidents in Kazakhstan between 1999 and 2018

In this paper, we briefly described the ecological consequences of six space rocket accidents launched from the Baikonur Cosmodrome between 1999 and 2018 and focused on an assessment of efficiency of soil remediation following the accidental crash of launch vehicle Proton-M on July 2, 2013, which resulted in the severest environmental impact in the modern Russian space industry. On the day after the accident, the content of carcinogenic unsymmetrical dimethylhydrazine and nitrosodimethylamine, as well as nitrate in soils of the crash site exceeded their maximal permissible concentrations by 8900, 6100 and 85 times, respectively. Mitigation measures included soil detoxication by a solution of 10% H₂O₂ and 1% iron complexonate, soil excavation and ploughing. Two years later (in April 2015), both unsymmetrical dimethylhydrazine and nitrosodimethylamine concentrations were below 0.05 mg/kg and nitrate concentration did not exceed 3.9 g/kg. As compared to background sites, soils of the crash site had significantly (P-value<0.05) lower values of pH and the content of total organic carbon, basicity from soda and carbonates and higher total nitrogen and soluble salt contents. Soil microbial communities were the most vulnerable component of the disturbed arid ecosystems, as their suppressed condition was indicated by a low biochemical oxygen demand and a very low cellulase activity.

Augmented Reality Lights for Compromised Visibility Navigation

This paper considers the feasibility of using augmented reality (AR) as a tool for enhancing visualization in maritime operations to avoid collision in different environmental conditions. According to the International Maritime Organization (IMO 2010), 90% of maritime accidents due to collisions at sea are caused in part by human error. This study investigates the new technology (AR) used to superimpose holographic images onto the real world; now reaching a state of readiness for commercial application. This paper demonstrates the competence of AR technology to serve as a maritime navigational aid. The research explores the viability of improving navigational safety in low visibility by projecting holograms of real-world objects in the same geo-location as the real object to make them “visible”. The paper presents the logical deconstruction of the technical problems and identified solutions, together with results of experiments used to validate the concept and technology readiness for real word maritime application. The paper presents a verified demonstrator; a proposed holographic bridge interface with an innovative way of presenting information using AR technology. Furthermore, it identifies that new technologies offer the opportunity for enhanced operator performances, with the expectation being that this should lead to reduce risk to persons, property, and the environment.

Improved Strategies for the Maritime Industry to Target Vessels for Inspection and to Select Inspection Priority Areas

Inspection authorities such as the Port State Control Memoranda of Understanding use different policies and targeting methods to select vessels for inspections and rely primarily on past inspection outcomes. One of the main goals of inspections is to improve the safety quality of vessels and to reduce the probability of future incidents. This study shows there is room for improvement in targeting vessels for inspections and in determining vessel-specific inspection priority areas (e.g., bridge management versus machinery related items). For the year 2018, sixty percent of vessels that experienced very serious or serious (VSS) incidents were not selected for inspection up to three months prior to the incident and forty percent of the vessels that were inspected still had incidents of which only four percent were detained. Furthermore, one can observe a very low correlation (−0.04) between the probabilities of detention and incidents (VSS) for the year 2018. The proposed approach treats detention and incident types as separate risk dimensions and evaluates seven targeting methods against random selection of vessels using empirical data for 2018. The analysis is based on three comprehensive data sets that cover the world fleet and shows potential gains (reduction of false negative events) of 14–27 percent compared to random selection. This can be further improved by adding eight inspection priority risk areas that help inspectors to focus inspections by providing insight in the individual risk profile of vessels. Policy makers can further customize the approach by classifying the risk of vessels into categories and by selecting inspection targets and benchmark samples. A small application example is provided to demonstrate feasibility of the proposed approach for policy makers and inspection authorities.

AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

Analysis and Evaluation of Eye Behavior for Marine Operation Training - A Pilot Study

This paper presents a new analysis approach for evaluating situation awareness in marine operation training. Taking advantage of eye tracking technology, the situation awareness reflected by visual attention can be visualized and analyzed. A scanpath similarity comparison method that allows group-wise comparisons is proposed. The term 'Expert zone' is introduced to evaluate the performance of novice operator based on expert operators' eye movement. It is used to evaluate performance of novice operators in groups in certain segment of marine operation. A pilot study of crane lifting experiment was carried out. Two target stages of operation for the load descending until total immersion to the seabed were selected and analyzed for both novice and expert operators. The group-wise evaluation method is proven to be able to access the performance of the operator. Besides that, from data analysis of fixation-related source and scanpath, the similarities and dissimilarities of eye behavior between novice and expert is concluded with the scanpath mode in target segment.

Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models

Liquefied nature gas (LNG) is a green energy. LNG-fueled vessels are extremely complex engineering systems. In view of the inherent hazardous properties of LNG fuel, LNG fueling is not only an important part, but it is also full of high risks in the operation of LNG-fueled vessels (LNGFVs). Therefore, it is necessary to study the risk factors, and the intrinsic relationship among them between the LNG and the vessel, and to simulate the system dynamics in the process of LNGFV operation. During the process of fueling of LNGFV, at every moment the vessel interacts with the energy and information of the surrounding environment. First, the impact of the three interactions of the fueling operation process, ship factors, and environmental factors were analyzed on the risk of fueling operation, and a complete node system was proposed as to the complex system dynamics mode. Second, by analyzing the boundary conditions of the system, the relationship of factors was established via the tools of system dynamics (SD). Based on the catastrophe theory (CA), the dynamics model for the fueling of LNG is set up to study the system’s risk mutation phenomenon. Third, combined with the simulation results of the case analysis, the risk evolution mode of the LNGFV during the fueling process was obtained, and constructive opinions were put forward for improving the safe fueling of the LNGFV. Application examples show that formal description of risk emergence and transition is a prerequisite for the quantitative analysis of the risk evolution mode. In order to prevent accidents, the coupling synchronization of risk emergence should be weakened, and meanwhile risk control should be implemented.

Understanding the Outcome in the Chinese Changjiang Disaster in 2015: A Retrospective Study

BACKGROUND

Rescue after a maritime disaster remains a great challenge in emergency medicine.

OBJECTIVE

We performed an overview of rescue efforts among the victims in the sunken cruise ship Eastern Star in the 2015 Changjiang River marine disaster, as well as possible preventive measures in maritime transport situations.

METHODS

The rescue records of 454 victims of the sunken ship were analyzed retrospectively. Their demographic data, rescue effects, accident inducement, and injury disposition were reviewed. A thorough analysis from the point of view of maritime traffic safety was also performed.

RESULTS

Of the 454 victims, 442 (97.36%) were killed and only 12 (2.64%) survived. The survivors were classified based on their gender, rescue type, and rescue spot as follows: male (91.67%), female (8.33%); tourists (50.00%), and ship staff (50.00%), after the breakdown of the rescue spot in Jianli, Hubei province, China. The survivors were saved only during the initial 17 h after the disaster. The survivors suffering from somato- and psychotrauma were urgently treated for limb injuries, infections of the upper respiratory tract and lungs, fluid and electrolyte imbalance, and acute traumatic stress. This incident was the most severe maritime disaster since the establishment of the People's Republic of China on October 1, 1949, due to the large number of elderly victims, fast overturning speed, and severe weather.

CONCLUSIONS

Emergency rescue requires more automated and intelligent systems for maritime safety. An increased focus must be placed on public welfare and ethics, with the goal of influencing more prosocial behavior rather than the pursuit of profit.