•In this study, the feasibility of the modified Human Factor Analysis and Classification System for Passenger Vessel collisions (HFACS-PV) is demonstrated for other types of accidents.•The HFACS-PV ...structure makes it possible to evaluate the occurrence of marine accidents and to analyse the contributions of human error.•The results of this study show that the HFACS-PV structure is compatible with contact, grounding and sinking accidents as well as collisions.•The main feature that distinguishes HFACS-PV from other HFACS structures is that it examines the internal and external environmental factors as a separate level under the name “Operational Conditions”
Shipping is one of the leading modes of transport that has dominated the world economy from past to present. The effectiveness and efficiency of maritime trade is closely related to maritime safety. Providing quality maritime safety is a fundamental requirement for environmentally friendly, sustainable, safe and efficient global trade. Therefore, maritime safety and human factors are frequently studied topics in literature. However, the fact that the human element has a complex socio-technical structure makes it difficult to fully analyse human factors in accidents. That is one of the biggest challenges in preventing and mitigating accidents. This research aims to demonstrate the feasibility of the modified Human Factor Analysis and Classification System for Passenger Vessel collisions (HFACS-PV) for other types of accidents. 51 grounding accidents that occurred in passenger vessels between 1991 and 2017 were analysed by using the HFACS-PV structure. The results show that the HFACS-PV structure created for passenger vessel accidents is compatible with contact, grounding and sinking accidents as well as collisions. Thus, the HFACS-PV structure allows for coherent analysis of marine accidents. Owing to the flexibility of HFACS, it is also possible to combine it with other analytical methods to conduct both qualitative and quantitative analysis.
•The implementation of HFACS and statistical methods in collision and grounding accident analysis.•Identification of HFACS categorical differences in collision and grounding accidents by accident ...causes.•Comparison of unsafe actions and preconditions for unsafe actions by bridge crew structure.•Presentation of industrial recommendations to prevent marine accidents.
It is important to establish all the causes of marine accidents, but this is sometimes quite difficult. Therefore, analyzing the causes by examining as many accidents as possible using a common classification system and submission of proposals is extremely essential. Overall, the study is an examination of collision and grounding accidents using the Human Factors Analysis and Classification System (HFACS). In the first phase of the study, the frequency and distribution of the causes of collision and grounding accidents were examined by HFACS categories. In the second phase, unsafe acts, which have been identified as the most important categories, and preconditions for unsafe acts are evaluated by bridge crew structure. The Chi-Square Test of Compliance and Independence and Simple Correspondence Analysis are used as statistical methods. As a result of study, the most important causes are identified as human factor differences between collision and grounding accidents, decision errors, resource management deficiencies, violations, skill-based errors and miscommunication.
•A study investigating the nonconformities encountered in the use of technology.•This study presents an accident network for marine collision-contact and grounding analysis.•The HFACS-PV method was ...used to categorize the causes of accidents.•The BN method was implemented to show the relationships between accident causes.
Technology and its innovative applications make life easier and reduce the workload on seafarers. Today's ship bridges have much more modern and integrated navigation systems than before, and the ship's handling and management have become much easier. However, nonconformities encountered in the use of technological devices may cause accidents. In this study, the effect of human factor related errors associated with the use of the bridge's electronic navigational devices on grounding and collision-contact accidents was investigated. Nonconformities obtained from 175 collision-contact and 115 grounding accident reports were qualitatively analysed by means of human factor analysis and a classification system. Afterwards, relationships between nonconformities and their probabilities were evaluated quantitatively via a Bayesian network method. As a result of the study, the accident network was revealed. This accident network summarizes how operating errors in the use of technological equipment cause accidents. Recommendations on the prevention of accidents caused by operating errors associated with the use of new technologies are finally given.
Human factor plays an important role in sustainable maritime transportation. Human errors are the leading cause of death or injuries during maritime pilot transfer operations despite international ...regulations. This paper proposes a hybrid human factors analysis model, comprised of qualitative and quantitative approaches by combining Success Likelihood Index Method (SLIM) and Human Factor Analysis and Classification System (HFACS) methods, to obtain the seafarers’ errors in the pilot boarding and disembarking. Human errors in the maritime pilot transfer process are determined by examining accident reports, literature reviews and expert judgments. Accidents and near misses are often poorly reported in the maritime industry. Therefore, determining human error probability (HEP) is of paramount importance in maritime transportation. This paper conducts an empirical human error prediction for a maritime pilot transfer operation to enhance operational safety and minimize human errors, providing a methodological extension through the integration of the HFACS technique into the SLIM. Utilizing HFACS and SLIM constitutes the unique contribution of this paper by predicting the possibility of human error since it presents the first application of the maritime industry. Thus, possible loss of life and injury will be prevented, the sustainability of maritime safety will be contributed, and pilots will be provided to work in a safe environment.
•Human errors are the leading cause of death or injuries during maritime pilot transfer operations.•Empirical human error prediction for a maritime pilot transfer operation to enhance operational safety and minimize human errors.•Utilizing HFACS and SLIM constitutes the unique contribution of this paper by predicting the possibility of human error since it presents the first application of the maritime industry.
Commercial fishing is an important industry that generates income directly or indirectly to many people in the world. It is impossible to carry out a fishing activity on this scale without a vessel. ...Therefore, fishing vessels are the most important element of modern fishing industry. Fishing vessels play a key role in fishing, transporting and storing fish. Thousands of people die every year as a result of fishing vessel accidents. In order to carry out sustainable fishing operations, fishing vessel accidents should be investigated and measures should be taken to prevent them. Therefore, in this study for analysing of accidents occurred between 2008 and 2018 in fishing vessels, with full lengths of 7 m and above, Bayesian network, chi-square methods were used. As a result, recommendations were made to prevent accidents. Also, Accident (Bayes) Network, which summarizes the occurrence of accidents on fishing vessels, is presented. These networks allow to understand the occurrence of accidents in fishing vessels and to estimate the occurrence of accidents in variable conditions. It was also found that there was a significant relationship between accident category and vessel length, vessel age, loss of life and loss of vessel.
•In this study, an accident network is presented which summarizes the occurrence of fishing vessel accidents.•The accident (Bayes) network makes it possible to evaluate the occurrence of fishing vessel accidents.•How influencing factors may individually or in combination lead to the occurrence of an accident is demonstrated.•General findings of the study show that it is necessary to review the safety measures applied in fishing vessels.•Chi-square test results show that there is a significant relationship between the accident type and ship characteristics.
In this study, an analysis of fire-explosion accidents in ship engine rooms was conducted. For analysis, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and ...fuzzy fault tree analysis (FFTA) was used. Using the HFACS method, the factors in the formation of engine-room fires were classified according to a hierarchical structure. The possible accident scenarios and probabilities were calculated using the FFTA method. In this study, it was observed that fire-explosion accidents were concentrated in ships over 20 years old and that mechanical fatigue affected accident formation. In particular, when the increased hot surfaces due to the operation of a ship's engines while it is in motion are combined with oil/fuel leakage, fire-related accidents become inevitable. Failure to provide proper insulation also triggers the occurrence of accidents. It has been observed that some of such accidents occur because the materials used in maintenance and repair work are not original to the ship. During this study, the causes of accidents were examined to prevent fire-related accidents from occurring in engine rooms, and suggestions were made to prevent similar accidents from happening in the future.
•The HFACS-PV structure used in this study presents information about the human errors that cause engine room fires.•The most likely accident scenarios and probabilities were calculated by using the FFTA method in this study.•This study suggests a hybrid model for analysis of ship engine room fires.
This study examines and analyzes marine accidents that have occurred over the past 20 years in the Black Sea. Geographic information system, human factor analysis and classification system (HFACS), ...and Bayesian network models are used to analyze the marine accidents. The most important feature distinguishing this study from other studies is that this is the first study to analyze accidents that have occurred across the whole Black Sea. Another important feature is the application of a new HFACS structure to reveal accident formation patterns. The results of this study indicate that accidents occurred in high concentrations in coastal regions of the Black Sea, especially in the Kerch Strait, Novorossiysk, Kilyos, Constanta, Riva, and Batumi regions. The formation of grounding and sinking accidents has been found to be similar in nature; the use of inland and old vessels has been highlighted as important factors in sinking and grounding incidents. However, the sequence of events leading to collision‐contact accidents differs from the sequence of events resulting in grounding and sinking accidents. This study aims to provide information to the maritime industry regarding the occurrence of maritime incidents in the Black Sea, in order to assist with reduction and prevention of the marine accidents.
In order to ensure sustainable maritime safety, studies based on unreported maritime accidents in maritime transport are necessary. Such studies allow the causes of accidents that have not come to ...light, to be identified and addressed. In this study, the data of unreported occupational accidents on Turkish fishing vessels with a full length of 12 m and above was analysed using both Bayesian network (BN) and Association Rule Mining (ARM) methods. A network structure that summarizes the occurrence of occupational accidents on fishing vessels with the BN method was put forward. The network structure makes it possible to analyse the latent factors, active failures and operational conditions that cause the accident qualitatively and quantitatively. The Predictive Apriori algorithm was used to establish rules for the occurrence of occupational accidents on fishing vessels, taking variables such as day condition, length, sea condition, and ship type into account. These rules provide an understanding of how occupational accidents occur on fishing vessels. In other words, these rules define the minimum requirements for the occurrence of accidents on fishing boats. The developed hybrid model can be used for analysing unreported occupational accidents on fishing vessels.
•A BN model created in this study summarizes the occurrence of occupational accidents on fishing vessels.•Researchers can predict the occurrence of occupational accidents under changeable conditions with the help of the BN.•Association Rule Mining (ARM) was used to reveal the most probable combinations of conditions leading to an accident.•ARM creates accident occurrence rules by considering the influencing factors affecting an accident.
The importance of accident investigations carried out in every field where operators play a vital role is increasingly recognised. Many researchers argue that understanding accident formation is the ...most important way to prevent future disasters. In this research, an analysis of the modified Human Factor Analysis and Classification System (HFACS) structures developed for use in the analysis of marine accidents was conducted. These structures include HFACS-PV (Passenger Vessels), HFACS-MA (Maritime Accidents), HFACS-Coll (Collisions), HFACS-SIBCI (ship collision accidents between assisted ships and icebreakers in ice-covered waters) and HFACS-Ground (Groundings). In this study, revisions in HFACS structures were examined. It was found that the accident factors were classified at different levels to facilitate the application of the original HFACS framework. The first of the remarkable differences among the basically developed methods is the level of external factors (first level), where the accident factors arising from national and international rules are classified. The second is the level of operational conditions (last level). It has been observed that the precondition for the unsafe acts level has been revised in all methods examined. This study will guide researchers in choosing an HFACS structure suitable for the area they will study, as well as revealing different aspects of the modified methods examined in marine accident analysis.
•In this study, a number of modified Human Factor Analysis and Classification System (HFACS) methods developed to analyse marine accidents were examined.•In the study, different and similar aspects of the classifications made in HFACS were revealed, and different perspectives in a number of applications were reviewed.•This study was conducted to provide researchers with the selection of a specific HFACS structure with the associated content in marine accident analysis.
In today’s world, wherein more than 80% of world trade is carried out by maritime routes, the safety and security of the seas where this trade takes place is of vast importance for nations and the ...international community. For this reason, ensuring the sustainable safety and security of the seas has become an integral part of the mission of all maritime-related entities. Using big data extracted from the seas and maritime activities into meaningful information with artificial intelligence applications and developing applications that can be used in maritime surveillance will be of great importance for augmenting maritime safety and security. In this article, data sources which can be used by a maritime surveillance system based on big data and artificial intelligence technologies and which can be established around sensitive sea areas and critical coastal facilities, are identified and a model proposal using this maritime big data is put forward.
PDF
