Towards a probabilistic approach for risk analysis of nuclear-powered icebreakers using FMEA and FRAM

Ocean Engineering, Год журнала: 2022, Номер 260, С. 112041 - 112041

Опубликована: Авг. 1, 2022

Язык: Английский

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Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study

Ocean & Coastal Management, Год журнала: 2022, Номер 230, С. 106377 - 106377

Опубликована: Окт. 3, 2022

Corona Virus Disease 2019 (COVID-19) outbreak leads to a significant downturn in the global economy and supply chain. In maritime sector, trade volume slumped by 3.8% 2020 compared with 2019. To explore impacts of COVID-19 on ship visiting behaviors, framework is proposed analyze impact port traffic using Automatic Identification System (AIS) data. Firstly, travel behavior-based model identify vessel anchoring berthing. Then, diversity berthing time are analyzed, reflecting COVID-19. The congestion caused quantified accounting for number ships their residence time. Finally, case study carried out vessels Beibu Gulf, China, operating from 2020. results show that average increase 62% 11% cargo 112% 63% oil tankers after before And density increases area Accordingly, relevant improvements countermeasures reduce adverse epidemic navigation system. paper has potential provide reference management improving efficiency post-pandemic era.

Язык: Английский

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Accident data-driven human fatigue analysis in maritime transport using machine learning

Reliability Engineering & System Safety, Год журнала: 2023, Номер 241, С. 109675 - 109675

Опубликована: Сен. 26, 2023

In maritime transport, fatigue conditions can impair seafarer performance, pose a high risk of incidents, and affect safety at sea. However, investigating human its impact on is challenging due to limited objective measures little interaction with other influential factors (RIFs). This study aims develop novel model enabling accident data-driven investigation RIF analysis using machine learning. It makes new methodological contributions, such as 1) the development identify significant RIFs leading based historical incident data; 2) combination Least Absolute Shrinkage Selection Operator (LASSO) Bayesian network (BN) formulate learning rationalise in accidents incidents; 3) provision insightful implications guide survey fatigue's contribution incidents without support psychological data. The results show importance their interdependencies for accidents. takes advantage available knowledge open direction management, which will benefit provide insights into high-risk sectors suffering from (e.g. nuclear offshore).

Язык: Английский

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A data mining method to extract traffic network for maritime transport management

Ocean & Coastal Management, Год журнала: 2023, Номер 239, С. 106622 - 106622

Опубликована: Май 1, 2023

Maritime traffic network is essential for navigation efficiency and safety of the maritime transport system. This study proposes a framework extracting based on Automatic Identification System (AIS) data. The consists pattern recognition, semantic routes extraction, route decomposition, generation. Firstly, data-driven method introduced to recognize ship behavior patterns extends single behaviors regional characteristics determine departure-arrival areas. Then, different combination areas, trajectories are classified groups. Subsequently, grid-system used rasterize each group, which realizes fusion trajectory data geographic location information. Finally, obtain main channels, extraction by establishing cumulative grid importance function. routes, together with compose network. applied AIS in Beibu Gulf, results show that contains 12 stop 4 entry/exit locations, 13 as well their corresponding channels. It therefore concluded proposed helps (1) provide theoretical analyze (2) enrich channel identification methods management.

Язык: Английский

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A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors

Reliability Engineering & System Safety, Год журнала: 2023, Номер 243, С. 109779 - 109779

Опубликована: Ноя. 20, 2023

Язык: Английский

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Risk identification and assessment methods of offshore platform equipment and operations

Process Safety and Environmental Protection, Год журнала: 2023, Номер 177, С. 1415 - 1430

Опубликована: Июль 28, 2023

Язык: Английский

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Environmental impacts of Arctic shipping activities: A review

Ocean & Coastal Management, Год журнала: 2023, Номер 247, С. 106936 - 106936

Опубликована: Ноя. 23, 2023

Язык: Английский

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A system dynamics-based approach for risk analysis of waterway transportation in a mixed traffic environment

Maritime Policy & Management, Год журнала: 2023, Номер 51(6), С. 1147 - 1169

Опубликована: Июнь 20, 2023

The rapid development of high techniques in recent years has made the operation intelligent ships a possible option foreseeable future. This will inevitably change navigation environment ships. Both conventional and sail same channels water areas. research proposed framework to meet needs future waterway transportation safety such mixed environment. can model assess risk inland traffic remote-control It defined four stages scenario rivers, including perception, cognition, decision, control stage. On this basis, we explored factors under scenario, construct system dynamics accordingly. A case study modelling assessment systems continuous bridge areas was conducted validate method. results showed that identify main key affecting provides theoretical basis for supervision remotely controlled ship operations

Язык: Английский

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Research on scenario deduction and emergency decision-making evaluation for construction safety accidents

Reliability Engineering & System Safety, Год журнала: 2024, Номер 251, С. 110317 - 110317

Опубликована: Ноя. 1, 2024

Язык: Английский

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A big data analytics method for the evaluation of maritime traffic safety using automatic identification system data

Ocean & Coastal Management, Год журнала: 2024, Номер 251, С. 107077 - 107077

Опубликована: Март 6, 2024

The complex traffic situations are among the factors influencing maritime safety. They can be quantitatively estimated through analysis of data. This paper explores impact on safety, focusing inland waterway traffic. It presents a big data analytics method, utilizing from Automatic Identification System (AIS) and historical accident records. methodology involves AIS preprocessing spatial autocorrelation models, including Moran's index, to extract evaluate dynamic characteristics characteristic includes thorough investigation into spatial-temporal distribution ship average speed trajectory density. then introduces an effective model that evaluates relationship between patterns accidents. study, specifically targeting Nanjing section Yangtze River, reveals variations in density over time. identifies several hotspots with significant local correlation these factors. Moreover, substantial is found locations accidents areas increased speed. These results may provide insights for safety management highlight strategies preventing

Язык: Английский

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