A dynamic topology analysis method for multi-ship encounters based on multi time-space network trees DOI
Zhichen Liu,

Ying Li,

Zhaoyi Zhang

и другие.

Ocean Engineering, Год журнала: 2024, Номер 307, С. 118052 - 118052

Опубликована: Май 20, 2024

Язык: Английский

Analysis of factors affecting the severity of marine accidents using a data-driven Bayesian network DOI
Yuhao Cao, Xinjian Wang, Yihang Wang

и другие.

Ocean Engineering, Год журнала: 2023, Номер 269, С. 113563 - 113563

Опубликована: Янв. 3, 2023

Язык: Английский

Процитировано

98

Risk evolution and prevention and control strategies of maritime accidents in China's coastal areas based on complex network models DOI
Jian Deng, Shaoyong Liu, Yaqing Shu

и другие.

Ocean & Coastal Management, Год журнала: 2023, Номер 237, С. 106527 - 106527

Опубликована: Фев. 17, 2023

Язык: Английский

Процитировано

54

A data-driven risk model for maritime casualty analysis: A global perspective DOI Creative Commons
Kaiwen Zhou, Wenbin Xing, Jingbo Wang

и другие.

Reliability Engineering & System Safety, Год журнала: 2023, Номер 244, С. 109925 - 109925

Опубликована: Дек. 30, 2023

Maritime casualty analysis needs to be addressed given the increasing safety demand in field due accidents' low-frequency and high-consequence features. This paper aims delve deeper into factors that affect maritime accident casualties by establishing a new database conducting an evolution analysis. Based on refined dataset, pure data-driven Bayesian network (BN) model is developed conduct of accidents occurred under different ship operational conditions. Methodologically, it introduces risk improve accuracy through enriched updated database. Furthermore, categorised five datasets based temporal development trends better analyse casualty. Five models are individually constructed timeframes illustrate dynamics compared seven evaluation indexes demonstrate effectiveness proposed BN model. It, for first time, investigates changing roles with time. The insights gained from this invaluable, contributing improved prediction strategies acknowledging patterns accidents.

Язык: Английский

Процитировано

44

Risk-informed multi-objective decision-making of emergency schemes optimization DOI
Xuan Liu, Cheng Wang,

Zhiming Yin

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 245, С. 109979 - 109979

Опубликована: Фев. 3, 2024

Язык: Английский

Процитировано

25

Navigating uncertainty: A dynamic Bayesian network-based risk assessment framework for maritime trade routes DOI
Hanwen Fan,

Haiying Jia,

Xuzhuo He

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 250, С. 110311 - 110311

Опубликована: Июль 1, 2024

Язык: Английский

Процитировано

18

Coupling and causation analysis of risk influencing factors for navigational accidents in ice-covered waters DOI
Shanshan Fu, Mingyan Wu, Yue Zhang

и другие.

Ocean Engineering, Год журнала: 2025, Номер 320, С. 120280 - 120280

Опубликована: Янв. 8, 2025

Язык: Английский

Процитировано

3

An integrated resilience assessment methodology for emergency response systems based on multi-stage STAMP and dynamic Bayesian networks DOI
Xu An,

Zhiming Yin,

Qi Tong

и другие.

Reliability Engineering & System Safety, Год журнала: 2023, Номер 238, С. 109445 - 109445

Опубликована: Июнь 16, 2023

Язык: Английский

Процитировано

42

A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system DOI

Yang Liu,

Xiaoxue Ma, Weiliang Qiao

и другие.

Reliability Engineering & System Safety, Год журнала: 2023, Номер 241, С. 109620 - 109620

Опубликована: Сен. 3, 2023

Язык: Английский

Процитировано

27

Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks DOI
Bilal Zerouali,

Yacine Sahraoui,

Mourad Nahal

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 249, С. 110236 - 110236

Опубликована: Май 22, 2024

Язык: Английский

Процитировано

13

Quantitive HAZOP and D-S evidence theory-fault tree analysis approach to predict fire and explosion risk in inert gas system on-board tanker ship DOI
Ozcan Durukan, Emre Akyüz, Orhan Destanoğlu

и другие.

Ocean Engineering, Год журнала: 2024, Номер 308, С. 118274 - 118274

Опубликована: Май 27, 2024

Язык: Английский

Процитировано

13