The Risk Analysis of Cart Development Based on Dynamic Bayesian Networks DOI Creative Commons
Junjun Liu, Jun Yu

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

Abstract To address the issues of multiple uncertainties, complex structures, and unpredictability during development trolley, this paper proposes a risk analysis method for trolley based on dynamic Bayesian networks. First, extensive relevant literature applying rough set reduction theory optimization, factor checklist with 5 primary indicators 16 secondary is constructed. Next, network model established by introducing time dimension. Fuzzy expert scoring are used to quantify probabilities nodes, Leaky Noisy-or Gate expansion applied correct conditional probabilities. Finally, performed using bidirectional inference function network. The time-series variation curve obtained through case analysis. By reverse reasoning, key factors occurrence identified, corresponding response strategies proposed. research results provide new approach analyzing effectively controlling risks associated development.

Язык: Английский

Human and organizational factors analysis of collision accidents between merchant ships and fishing vessels based on HFACS-BN model DOI
Hong Wang, Ning Chen, Bing Wu

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 249, С. 110201 - 110201

Опубликована: Май 14, 2024

Язык: Английский

Процитировано

24
Incorporation of a global perspective into data-driven analysis of maritime collision accident risk DOI Creative Commons
Huanhuan Li, Cihad Çelik, Musa Bashir

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 249, С. 110187 - 110187

Опубликована: Май 15, 2024

Ship collision accidents are one of the most frequent accident types in global maritime transportation. Nevertheless, conducting an in-depth analysis prevention poses a formidable challenge due to constraints limited Risk Influential Factors (RIFs) and available datasets. This paper aims incorporate perspective into new data-driven risk model, scrutinize root causes accidents, advance measures for their mitigation. Additionally, it seeks analyze spatial distribution conduct comprehensive comparative study on characteristics both pre- post-COVID-19, utilizing real dataset collected from two reputable organizations: Global Integrated Shipping Information System (GISIS) Lloyd's Register Fairplay (LRF). The research findings implications encompass several crucial aspects: 1) constructed model demonstrates its reliability accuracy predicting as evident prediction performance various scenario analysis; 2) hazardous voyage segment is identified provide valuable guidance different stakeholders; 3) hierarchical significance ship context highlighted regarding probable occurrences; 4) During pandemic, rise probabilities, particularly involving older vessels bulk carriers, implies heightened operational challenges or maintenance issues these types; (5) prominence favorable adverse sea conditions reports underscores significant influence weather during pandemic. These help enhance safety protocols, ultimately reducing frequency domain.

Язык: Английский

Процитировано

23
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
Investigation of ship collision accident risk factors using BP-DEMATEL method based on HFACS-SCA DOI
Mingyang Guo, Miao Chen, Lihao Yuan

и другие.

Reliability Engineering & System Safety, Год журнала: 2025, Номер 257, С. 110875 - 110875

Опубликована: Янв. 31, 2025

Язык: Английский

Процитировано

5
Development of An Improved Bayesian Network Method for Maritime Accident Safety Assessment Based on Multiscale Scenario Analysis Theory DOI
Dewei Kong,

Zelong Lin,

Wei Li

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 251, С. 110344 - 110344

Опубликована: Июль 11, 2024

Язык: Английский

Процитировано

17
Research on scenario deduction and emergency decision-making evaluation for construction safety accidents DOI
Jianjun She, Zihao Guo, Zhijian Li

и другие.

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

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

15
Application of bayesian network in the maritime industry: Comprehensive literature review DOI
Isaac Animah

Ocean Engineering, Год журнала: 2024, Номер 302, С. 117610 - 117610

Опубликована: Март 28, 2024

Язык: Английский

Процитировано

13
A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports DOI
Di Zhang,

Xinyuan Li,

Chengpeng Wan

и другие.

Maritime Economics & Logistics, Год журнала: 2024, Номер 26(1), С. 44 - 73

Опубликована: Фев. 16, 2024

Язык: Английский

Процитировано

12
Structure model-based hazard identification method for autonomous ships DOI Creative Commons
Megumi Shiokari, Hiroko Itoh,

Tomohiro Yuzui

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 247, С. 110046 - 110046

Опубликована: Фев. 29, 2024

There is great interest in the practical use of autonomous ships. Although it important to identify and address serious hazards at initial design stage when constructing complicated large-scale systems, difficult capture entire structure such a system. To this issue, study, we propose model–based hazard identification (SMB-HAZID) method, which suitable for performing The primary feature method using task (ST) diagrams, checklist, keywords. includes modeling that can describe overall system together with each component's tasks, information necessary execution, interactions among components an ST diagram. In addition, paper presents example perform hypothetical ship early stage. Through trial application, confirmed SMB-HAZID analyzing within ship. our future research, will improve proposed by developing incorporating technique quantifying or semi-quantifying risks corresponding identified hazards.

Язык: Английский

Процитировано

12
Operational resilience modeling of cross-border freight railway systems: A study of strategies to improve proactive and reactive capabilities DOI
Haiwen Li, Lin Qi, Mudan Wang

и другие.

Reliability Engineering & System Safety, Год журнала: 2025, Номер unknown, С. 110856 - 110856

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2