Maritime Accidents Research Articles

Multi-Criteria Model for Identifying and Ranking Risky Types of Maritime Accidents Using Integrated Ordinal Priority Approach and Grey Relational Analysis Approach

Accidents in marine operations are occurring consistently despite government safety initiatives and efforts to lower the number of accidents and the ensuing human casualties. Since each accident type has a different frequency and casualty rate, identifying risky accident types is important to determine the priority for taking necessary risk reduction actions. Usually, a risk is calculated using two criteria, i.e., the frequency and fatality of an accident. However, the accident statistics show that for the last 5 years from 2018 to 2022, the injury rate is more than three times the death rate in maritime accidents in Korean waters. Considering the importance of injury, unlike other previous studies, we perform a risk analysis with three criteria, i.e., frequency, death, and injury to complement the conventional risk calculation methods, which can help decision-makers allocate the limited resources to the riskiest types of accidents in order of priority. In doing so, we employed an integrated ordinal priority approach (OPA) and grey relational analysis (GRA) method to assign proper weight to each criterion and rank eight accident types. We categorized the accidents types into three different groups where safety accidents and collisions were ranked as the most dangerous types. The combined OPA and GRA technique has been effectively applied to other risky industries, as well as the maritime industry. Additionally, the proposed method is suitable for multi-criteria models when each criterion has a different importance. Finally, the method can be integrated into the framework of the risk ranking process to enhance the analysis results.

A YOLOv7-Based Method for Ship Detection in Videos of Drones

With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7.

Development of an improved Bayesian network method for maritime accident safety assessment based on multiscale scenario analysis theory

The strait is infrastructure for connecting ports, ensuring the national economy and human life. Assessing maritime safety is crucial for strait operation and maintenance. Current methods over-rely on statistical analysis of historical data, which lacks scientific algorithms and cannot accurately assess safety timeliness. Therefore, this study proposes an improved Bayesian network (BN) model based on the multiscale scenario analysis theory to process to the maritime safety assessment process. Firstly, the maritime safety assessment model is constructed based on the knowledge element theory (KET) and scenario analysis theory (SAT). Secondly, a heuristic learning algorithm is used to construct the BN model of maritime accidents to define scenario elements and key parameters. Thirdly, a scenario simulation is conducted with the Bohai Strait as a case to assessment its emergency capability in catastrophic accidents. This paper successfully developed a maritime accident safety assessment framework using an improved BN algorithm within a scenario analysis perspective, significantly enhancing the accuracy and reliability of safety assessments. This methodology used in this study can be used a tool suitable for the safety assessment of major infrastructure accidents at the national level and the results obtained can serve as a reference for dynamically revising emergency plans.

A framework to analyse the probability of accidental hull girder failure considering advanced corrosion degradation for risk-based ship design

Ship’s hull girder failure could result from maritime accident that can cause human life loss, environmental disaster, and major economic impacts. In risk-based ship design paradigm, accounting for rare phenomena (e.g. ship-ship collision or grounding) is important to provide safe and durable structure. In-service corrosion-induced hull degradation should be considered at the design stage, as it can significantly affect structural strength. The current study presents a novel framework to estimate the probability of ship hull girder failure, accounting for novel corrosion modelling techniques and accidental damage. The associated uncertainties are considered using statistical sampling from evidence-based distributions. A state-of-the-art deterministic model for ultimate strength calculation is applied using Monte Carlo simulation approach, resulting in the probability of hull failure through a reliability assessment. Wave and still-water bending moments are considered random variables. Two case studies of tanker ships with varying sizes are executed to show the applicability of the proposed framework. The results indicate that proper consideration of corrosion is of high importance, as ageing can significantly increase the probability of failure if accidental damage happens. Therefore, whereas future research and model refinement are discussed, the presented framework can serve for risk-based ship design tool and assess existing structures’ safety.

Analyzing ship collision accidents in China: A framework based on the N-K model and Bayesian networks

Maritime accidents are frequent in China, with collisions constituting a significant portion, leading to substantial casualties, financial losses, and environmental harm. Analyzing collision risk is crucial for safe shipping operations, as such accidents arise when multiple risk factors interact beyond certain thresholds. Thus, understanding the coupled relationships among these factors is essential for accident prevention. This study presents a quantitative coupling risk assessment approach for collisions using the N–K model and Bayesian networks (BNs). Through an analysis of 304 accident reports from the China Maritime Safety Administration (CMSA), four primary risk factors (human, ship, environment, and management) are identified. The N–K model quantifies the coupling degree between these factors, while a BN model further elucidates and quantifies the collision risk based on the calculations of N–K model. Validation of the BN model is followed by sensitivity analysis to assess the impact of risk factors on accident occurrence. Subsequently, the method is applied to a case study on collision risk assessment in Chinese waters. Findings indicate that multifactor coupling has a higher occurrence probability than two-factor coupling, with human-ship-environment-management coupling presenting the highest likelihood. Human and management factors emerge as pivotal in collision incidents, while the impact of ship and environmental factors on coupling becomes more pronounced as their probabilities vary. Leveraging the capability of BNs to handle uncertainties and logical relationships, combined with the data-driven N–K model, mitigates subjectivity. The results propose the formulation of risk management strategies and measures for practitioners to enhance maritime safety.

A two-way coupled fluid–structure interaction method for predicting the slamming loads and structural responses on a stiffened wedge

Ships navigating through rough seas are subjected to slamming loads from waves, which can lead to structural damage and maritime accidents. The wedge model is commonly employed to investigate slamming loads and structural responses. While a stiffened wedge closely resembles a real ship hull plate, incorporating it into fluid–structure interaction simulations presents challenges. This paper proposes a two-way coupled fluid–structure interaction method to examine slamming loads and structural responses of a free fall non-prismatic stiffened steel wedge. Hydrodynamic loads are determined through Reynolds-averaged Navier–Stokes computations using OpenFOAM, while structural responses are predicted using the finite element analysis (FEA) software Calculix. To achieve two-way coupling between computational fluid dynamics and FEA simulations, a coupling library for partitioned multi-physics simulations, preCICE, is introduced. The computed impact pressure and stress align well with available experimental data. Various free fall heights are investigated in the numerical simulations. The results indicate that elastic deformation mitigates impact pressure, while the presence of transverse ribs enhances the rigidity of the flexible plate. The duration of pressure and the peak slamming pressure exhibit an inverse correlation. Greater free fall heights result in shorter pressure duration times, and smaller free fall heights may reduce rise time. Three-dimensional effects cause pressure to decrease along the midpoint of the plate toward both sides. Additionally, structural stress in the central area exceeds that in the areas on both sides at the same height. In conclusion, the proposed two-way coupled model proves suitable for accurately and efficiently computing hydroelastic slamming on flexible wedges.

Main directions for improvement of navigation safety through prevention of ship collisions

The article is devoted to the study of the main directions of improving the safety of navigation by preventing ship collisions. The modern development of global shipping is characterized by an increase in the intensity of maritime transportation, which significantly increases the risks of emergencies, in particular ship collisions. These incidents are one of the main causes of maritime accidents, resulting in significant economic losses, environmental damage and risk to human life. The article analyzes various aspects of improving safety of navigation, including the use of modern technologies, organizational measures, crew training and efficient ship maintenance. The focus is on the following areas: technical support, organizational measures, education and training for the crew, communication systems and international coordination, as well as ensuring the technical serviceability of ships. Radar systems allowing to detect and track ships at different distances, which is critical in difficult weather conditions. Another important aspect is crew training. Regular professional education, training, and simulation training help crews adapt to new technologies and respond effectively to dangerous situations. An effective communication system and international coordination are critical for rapid response to emergencies. Emergency communication systems ensure rapid exchange of information between ships and coastal services, and international cooperation contributes to increased preparedness and coordination. Ensuring the technical serviceability of ships through regular inspections and technical surveys is the basis for safe navigation, allowing to identify and eliminate potential problems. The article emphasizes the importance of a comprehensive approach to improving shipping safety, including the integration of advanced technologies, organizational processes, training and maintenance. This approach helps to reduce the risk of ship collisions and improve overall safety at sea

Screening for anxiety, depression and poor psychological well-being in Spanish seafarers: An empirical study of the cut-off points on three measures of psychological functioning

The present study examines three variables that are associated with psychological issues in merchant seafarers: anxiety, depression and well-being. Furthermore, cut-off points have been established for three psychological scales: the Generalized Anxiety Disorder (GAD-7) questionnaire, the Patient Health Questionnaire (PHQ-9) and the World Health Organization's Well-Being Index (WHO-5). Two cross-sectional surveys were employed in this study, with a total sample size of 170 Spanish seafarers working on international merchant vessels. The participants were recruited at a branch of the Social Marine Institute situated in Gijón, northern Spain. The PHQ demonstrated the greatest discriminatory ability at each of the cut-off points, followed by the GAD and the WHO. The analysis revealed that while the PHQ exhibited the highest sensitivity, the GAD demonstrated the highest specificity, making it the most balanced questionnaire overall. Holiday duration was the only variable significantly related to any of the psychological scales, specifically the WHO. The findings of this study provide a foundation for further research in maritime mental health, with a particular focus on the prevention of maritime accidents.

A two-stage approach for ship detection in restricted visibility based on dehazing and SE-YOLO algorithms

ABSTRACT The rapid and automatic detection of ships in restricted visibility is crucial for protecting the safety of maritime navigation. The frequent presence of fog in maritime environments causes restricted visibility, contributing to a higher occurrence of maritime accidents. Aiming to address ship target detection's low accuracy in restricted visibility weather, this paper proposes an improved dehazing model based on a two-stage ship detection algorithm. First, during the dehazing stage, a specialized model for maritime environments addresses the problem of unclear ship features in foggy images. The model is trained by a combination of synthetic images training and real images fine-tuning. Then, during the detection stage, a modified YOLO network for small ship detection called SE-YOLO is introduced to solve the issue of small ship targets' low detection accuracy. On the one hand, the modified Spatial Pyramid Pooling – Fast(SPPF) module is designed to reduce information loss during feature extraction; on the other hand, the attention mechanisms are integrated to enhance the network's sensitivity to the details of small ship targets and improve the model's overall detection performance for ships. Moreover, to simulate the real sea scene and test the effectiveness of our method, a maritime-haze dataset containing different concentrations of fog and various brightness is made for this research. Finally, The experimental results indicate that, compared to the traditional YOLOv5 method, our method performs better on detecting ships in restricted visibility environments, with the mean average precision (mAP) increased from 62.64% to 77.23%.

A risk prediction model for Maritime accidents

This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence.

A data-driven Bayesian network model for pattern recognition of maritime accidents: A case study of Liaoning Sea area

A data-driven Bayesian network (BN) model is developed to capture the individual and collective impacts of accident influential factors (AIFs) on different attributes of maritime accidents, including accident type, severity, and loss. To develop the data-driven BN model, a dataset comprising 980 accidents from 2000 to 2023 in the Liaoning Sea area is initially established. Subsequently, the data-driven BN model is constructed using the Tree Augmented Network (TAN) learning algorithm and expectation-maximization (EM) algorithm. Finally, comprehensive BN analysis encompassing strength of influence assessment, sensitivity analysis, scenario simulation, and model verification is conducted based on the developed TAN-BN model. The results demonstrate that distinct types of maritime accidents exhibit varying sensitivities to seasonal variations and time of day; contact and groundings are primarily encountered by general cargo ships while fire and explosions are more prevalent among bulk carriers. Fishing boats tend to be vulnerable to collisions whereas sinking accidents are most commonly associated with sand carriers.

An improved drift model of dropped containers under wind and current effects

In the face of the increasing risk of Maritime accidents caused by global climate change, it is necessary to improve the accuracy of drift model of the dropped containers at seas for Maritime Search and Rescue (SAR). Sensitivity test on the classic drift model illustrates that immersion ratio (Ir) of container plays a dominate role in the drift trajectory of container compared to the drag coefficients. Inspired from it, a new scheme of variable Ir is proposed to improve the model by considering the water entering process of container in the study. Based on the stability analysis of four floating attitudes of intact empty container under strong wind condition, the variable Ir is estimated according to the orifice inflow theory and the Archimedes' principle of buoyancy. The new scheme is compared with the fixed Ir scheme from the simulation of the drift trajectory of the dropped container at Xiamen Bay of China. The result shows that error arisen from the fixed Ir would be accumulated as the time integral, but the new scheme of variable Ir can obviously improve the model accuracy for the long-time prediction of SAR. In addition, the drag coefficients of wind and water in the drift model of the dropped container are suggested to be 0.8 and 1.2, respectively.

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

Framework for process risk analysis of maritime accidents based on resilience theory: A case study of grounding accidents in Arctic waters

The complexity and non-linear interactions involved in maritime accidents make it challenging to apply traditional models of risk analysis to them. Resilience is adopting a perspective focuses on holistic aspects of safety across the pre-accident, mid-accident, and post-accident phases. This paper proposes a framework for process risk analysis of maritime accidents based on resilience theory by applying the function resonance analysis method (FRAM). We first identify a three-layer maritime accident based on maritime accident investigation reports and then analyze the potential variability of all relevant functions. Subsequently, a FRAM model is constructed to analyze the accident's functional resonance of accidents by aggregating variations in the functions. Finally, we apply Monte Carlo simulations to compute the values of coupling of all interconnected functions. The ship grounding accidents in Arctic waters is chosen as the case to demonstrate the proposed framework. The relationships of influence between the functions were analyzed to obtain critical coupling pathways of coupling between them, and a time-weighted PageRank algorithm was used to identify key functions. The results show that the proposed approach can reveal vulnerabilities and instabilities in the system, and enables the proposal of targeted, long-term measures of risk control to enhance its resilience and safety.

A data-driven conceptual framework for understanding the nature of hazards in railway accidents

Hazards threaten railway safety by their potential to trigger railway accidents, resulting in significant costs and impacting the public's willingness to use railways. Whilst many prior works investigate railway hazards, few offer a holistic view of hazards across jurisdictions and time because the large number of primary sources make synthesising such learnings time consuming and potentially incomplete. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic modelling for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. The results allow the different aspects of each hazard to be listed along with the potential combinations of hazards that could trigger railway accidents. Better understanding of the aspects of individual hazards and the relationships between hazards and previous accidents can inform more effective hazard mitigation policies including technical or regulatory interventions. A case study of the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability to better inform policymaking for managing risks. The primary contributions of the framework proposed are to enable a large amount of knowledge accumulated to be summarised for an intuitive policymaking process, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could apply the technique to road, aviation or maritime accidents.

A semantic reasoning-based emergency rescue assistant decision method for maritime accidents involving chemicals

Maritime accidents involving chemicals (MAICs) constitute a unique category of marine emergency characterized by low frequency, high risk, and complex disposal challenges. These accidents often entail fires, explosions, and pollutant leakage, demanding effective emergency responses. The success of MAICs rescue operations relies on multiple factors, encompassing the structural features of the involved vessel, personnel, cargo, marine environment, and the availability of resources and response procedures. Proficiency in various professional fields, including ship transportation, chemistry, firefighting, and environmental science, is essential for informed decision-making. This paper addresses the need for a standardized description of knowledge in the MAICs emergency rescue decision-making process, aiming to enhance practical application frequency and decision-making efficiency. Employing ontology theory, the approach involves structurally modeling the knowledge of MAICs emergency rescue, constructing an ontology model, and establishing a decision rule base. Validating the proposed method, the emergency response to the “Longqing 1″ accident is examined as a case study, illustrating its potential for supporting MAICs emergency rescue decision-making. The application of ontology theory in this context holds both theoretical and practical significance. It facilitates the sharing, reuse, and expansion of knowledge in the emergency rescue field, providing crucial technical support for intelligent decision-making in MAICs emergencies.

Deciphering spatial heterogeneity of maritime accidents considering impact scale variations

ABSTRACT Ensuring maritime safety has ascended as a preeminent concern within the global maritime sector. Understanding how factors affect maritime accidents’ consequences in different water areas would be of great benefit to preventing the occurrence or reducing the consequences. This study thus employed a multi-scale geographically weighted regression (MGWR) model on the accident dataset from Fujian waters in the East China Sea, to quantify the influences of different factors as well as the spatial heterogeneity in the effects of key factors on maritime accident consequence. The performances of MGWR are compared with multiple linear regression (MLR) and GWR. As expected, MGWR outperforms the other two models in terms of its ability to clearly capture the unobserved spatial heterogeneity in the effects of factors. Results reveal notably distinct influences of some factors on maritime accident consequences in different locations. An intuitive indication by MGWR is that approximately 50% of the accidents present positive coefficients of good visibility while other locations are negative, which are failed to recognize by MLR. The outcomes provide insights for making appropriate safety countermeasures and policies customized for different water areas.

Numerical simulation of freak wave generation and evolution process based on SPH method

With the occurrence of maritime accidents all over the world, the study of extreme waves has been promoted. These extreme waves are often called freak waves in the scientific community. At present, a variety of mechanisms are proposed to explain freak waves in the marine environment, such as directional focusing, dispersive focusing, and nonlinear modulation instability. At first, a hydrodynamic model is established based on the Smoothed Particle Hydrodynamics (SPH) method in the C# environment, and the regular wave generated by the piston wave generator is numerically simulated. Secondly, the simulation results were analyzed and visualized by using Paraview software, compared with physical experimental datas to verify the reliability of this model. Then, the generation and evolution of freak waves were numerically simulated through the movement of piston wave generators on both sides of a 2D numerical wave flume, and the influence of frequency and amplitude was analyzed by using directional focusing. The results show that if the frequency is smaller and the amplitude is larger, it is more likely to generate freak waves and wave-breaking phenomena. This study lays a foundation for the research of freak waves in the future.

Analysis of the Relationships between Factors Contributing to Marine Casualties

This paper presents a model for assessing the impact of various factors on maritime accidents. This paper discusses the issue of maritime transport and its risks. The taxonomy of causes and consequences used in maritime transport is explained. Two mathematical principles were used, i.e., multi-criteria decision analysis (MCDA) and principal component analysis (PCA). The analyses were carried out using the Promethee/Gaia method. The connection between causes and accidents constituted the decision problem. The evaluation criteria, a set of actions and preferences, were identified based on the method principles. The results of this research include rankings of causal chains. They were analysed in multi-criteria and single-criteria dimensions. The outcomes are presented numerically and graphically. Such research contributes to improving safety at sea. It allows us to understand how a particular transport system works. Conclusions can be drawn and measures can be initiated to change the situation in the future.

Impact of Ship Noise on Seafarers’ Sleep Disturbances and Daily Activities: An Analysis of Fatigue Increase and Maritime Accident Risk through a Survey

This study delves into the impact of ship noise on seafarer well-being, emphasizing fatigue—a significant contributor to maritime accidents due to human error. The investigation, centered around the hypothesis that IMO ship construction standards may not adequately minimize noise levels in seafarer cabins, seeks to establish whether these levels are sufficient to ensure seafarer security and prevent sleep disturbances. According to current IMO regulations, noise levels are set at 55 dB for vessels under 10,000 gross tonnage and 60 dB for those over 10,000, yet WHO guidelines recommend a maximum of 40 dB in bedrooms to avoid sleep disruption. A comprehensive survey involving 221 cadets demonstrates that 79.6% of participants experience sleep disturbances, work disruptions, and stress due to noise, indicating that the present noise standards are insufficient. This paper argues that reducing noise levels in individual cabins to below 40 dB is critical for enhancing seafarer health and safety and could significantly reduce human error-related maritime accidents. The findings advocate for more stringent noise control measures and regulatory reforms to bridge the knowledge gaps and improve labor protection in the maritime industry.