Knowledge graph construction based on ship collision accident reports to improve maritime traffic safety

Ocean & Coastal Management, Год журнала: 2023, Номер 240, С. 106660 - 106660

Опубликована: Май 19, 2023

Язык: Английский

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A machine learning method for the prediction of ship motion trajectories in real operational conditions

Ocean Engineering, Год журнала: 2023, Номер 283, С. 114905 - 114905

Опубликована: Июнь 16, 2023

This paper presents a big data analytics method for the proactive mitigation of grounding risk. The model encompasses dynamics ship motion trajectories while accounting kinematic uncertainties in real operational conditions. approach combines K-means and DB-SCAN (Density-Based Spatial Clustering Applications with Noise) clustering methods Principal Component Analysis (PCA) to group environmental factors. A Multiple-Output Gaussian Process Regression (MOGPR) is consequently used predict selected dynamics. Ship sway defined as deviation between her trajectory centreline. Surge accelerations are idealise time-varying manoeuvring ships various routes. Operational conditions simulated by Automatic Identification System (AIS), General Bathymetric Chart Oceans (GEBCO), nowcast hydro-meteorological records. Dynamic Time Warping (DTW) adopted identify centre-line along paths. machine learning algorithm applied predictions Ro-Pax operating two ports Gulf Finland. visualised ship's route using Progress (GPR) flow method. Results indicate that present methodology may assist predicting probabilistic distribution (speed, distance, drift angle, surge accelerations) risk

Язык: Английский

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A Bayesian network-based model for risk modeling and scenario deduction of collision accidents of inland intelligent ships

Reliability Engineering & System Safety, Год журнала: 2023, Номер 243, С. 109816 - 109816

Опубликована: Ноя. 14, 2023

Язык: Английский

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An object-oriented Bayesian network model for the quantitative risk assessment of navigational accidents in ice-covered Arctic waters

Reliability Engineering & System Safety, Год журнала: 2023, Номер 238, С. 109459 - 109459

Опубликована: Июнь 19, 2023

Merchant ship operations in the ice-covered Arctic waters may encounter traditional navigational accident risks (i.e., grounding, collision, etc.) and from sea ice, such as besetting ice. However, describing, modeling, quantifying multiple ice navigation are challenges maritime risk assessment perspective. This paper proposes an object-oriented Bayesian network (OOBN) model for quantitative of accidents waters. The OOBN makes use database Lloyd's intelligence investigation reports. proposed decomposes into five levels based on causation theory: environment, unsafe condition, act, probability accident, consequence accident. Consequently, ship–ice collision selected cases to interpret factors identification, analysis, evaluation. results demonstrate that (1) is highest grounding accidents, followed by waters; (2) speed condition critical mutual these four scenarios; (3) influencing specific identified propose corresponding control options. can be used

Язык: Английский

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Dynamic evolution of maritime accidents: Comparative analysis through data-driven Bayesian Networks

Ocean Engineering, Год журнала: 2024, Номер 303, С. 117736 - 117736

Опубликована: Апрель 10, 2024

Maritime accident research has primarily focused on characteristics and risk analysis, which often overlooks the evolution of associated patterns over time. This study aims to investigate dynamic changes in maritime accidents from 2012 2021 by employing a data-driven Bayesian Network (BN) model conducting systematic pattern comparison. It presents two-stage models for two databases five against different timeframes capture evolving global accidents. Furthermore, within context investigation, this pioneers analysis effectiveness network structures, namely layered BN Tree-Augmented Naive (TAN) network, terms accuracy predicting severity. The key findings regarding past decade include: (1) significant rise risks linked large ships (30.8%), port areas (11.67%), anchoring (11.82%), manoeuvering operations (3.8%); (2) connection between poor practices fishing boats 'overboard' accidents, inadequate equipment tankers or chemical 'fire/explosion' accidents; (3) TAN model's superior performance forecasting severity compared model; (4) probability 'very serious' ship-related factors is 74.7%, significantly lower than network's 99.4%. reveals shifts time underscores importance continuous monitoring effective safety management.

Язык: Английский

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Systems driven intelligent decision support methods for ship collision and grounding prevention: Present status, possible solutions, and challenges

Reliability Engineering & System Safety, Год журнала: 2024, Номер unknown, С. 110489 - 110489

Опубликована: Сен. 1, 2024

Язык: Английский

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Human and organizational factors analysis of collision accidents between merchant ships and fishing vessels based on HFACS-BN model

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

Опубликована: Май 14, 2024

Язык: Английский

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

Reliability Engineering & System Safety, Год журнала: 2024, Номер 249, С. 110202 - 110202

Опубликована: Май 17, 2024

Язык: Английский

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

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

Опубликована: Июль 1, 2024

Язык: Английский

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Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN

Reliability Engineering & System Safety, Год журнала: 2022, Номер 229, С. 108850 - 108850

Опубликована: Сен. 19, 2022

The risks involved in ship pilotage operations are characterized by random, uncertain and complex features. To reveal the spatiotemporal evolution of collision process, a risk analysis model is developed this paper combination Functional Resonance Analysis Method (FRAM) Dynamic Bayesian Network (DBN). First, based on results functional resonance mechanism system, relevant influencing factors (RIFs) their coupling relationships identified. Second, DBN quantified employment various uncertainty treatment methods including Dempster-Shafer evidence theory for configuration prior probabilities Markov dynamic factors' transition probability calculation. Finally, using temporal observation data, inference conducted to law process. findings show that significantly sensitive regional locations, resulting "U" curve shaped action resonance. "Inadequate human look-out" among most influential factors, hence targeted control strategies should be formulated ensure safety operations.

Язык: Английский

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