Article
Engineering, Industrial
Mehmet Kaptan, Ozkan Ugurlu, Jin Wang
Summary: This study investigated the impact of human factor errors associated with the use of bridge's electronic navigational devices on grounding and collision accidents, by qualitatively and quantitatively analyzing nonconformities obtained from actual accident reports. The accident network revealed how operating errors in using technological equipment lead to accidents, with recommendations for preventing such accidents given at the end.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Aerospace
Bin Meng, Na Lu
Summary: This study analyzed controlled flight into terrain (CFIT) accidents using the Human Factors Analysis and Classification System (HFACS) and Bayesian network (BN). The results showed that the precondition for unsafe acts had the greatest impact on CFIT accidents, and inadequate supervision, noncompliance with procedures, and navigation equipment failure were identified as the top significant contributing factors.
Review
Engineering, Industrial
Mehmet Kaptan, Songul Sarialioglu, Ozkan Ugurlu, Jin Wang
Summary: The research analyzed the structures of HFACS used in marine accidents, identifying revisions and different levels of classification methods to facilitate the application of the original framework.
INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS
(2021)
Article
Engineering, Marine
Emre Ozaydin, Remzi Fiskin, Ozkan Ugurlu, Jin Wang
Summary: In order to ensure maritime safety, it is necessary to study unreported maritime accidents. This study uses Bayesian network and Association Rule Mining methods to analyze data of unreported occupational accidents on Turkish fishing vessels. A network structure and accident occurrence rules are proposed to help analyze the latent factors and requirements for occupational accidents on fishing vessels.
Article
Engineering, Marine
Yuhao Cao, Xinjian Wang, Yihang Wang, Shiqi Fan, Huanxin Wang, Zaili Yang, Zhengjiang Liu, Jin Wang, Runjie Shi
Summary: This study utilizes a data-driven Bayesian network model to analyze the relationship between the severity of marine accidents and relevant Accident Influential Factors (AIFs). By classifying marine accident investigation reports involving 1,294 ships from 2000 to 2019, a database of factors affecting the severity of marine accidents is established. The Tree Augmented Naive Bayesian algorithm is used to establish a data-driven BN model and analyze the established database of AIFs through data training and machine learning.
Article
Engineering, Marine
Nermin Hasanspahic, Srdan Vujicic, Vlado Francic, Leo Campara
Summary: By analyzing 135 marine accident reports, the research revealed that the causes of marine accidents primarily depend on two human factor categories. By influencing these factors, the number of accidents could be reduced and shipping safety improved in general.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Industrial
Serdar Yildiz, Ozkan Ugurlu, Jin Wang, Sean Loughney
Summary: Shipping is a leading mode of transport in the world economy, with maritime safety closely related to effectiveness and efficiency of maritime trade. The complexity of human factors poses a challenge in fully analyzing accidents, and this research aims to demonstrate the feasibility of using the HFACS-PV system for various types of accidents. Results show that the HFACS-PV structure is adaptable for different maritime accidents, allowing for coherent analysis.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Review
Engineering, Industrial
Mona Ahmadi Rad, Lianne M. Lefsrud, Michael T. Hendry
Summary: Accident analysis methods are used to identify the factors and processes leading to an accident. This paper fills the research gap in systemic accident modelling in railways through a literature review and bibliometric analysis. The study identifies the popularity of HFACS, STAMP, and FRAM in railway accident studies, and highlights the need for qualitative and quantitative improvements of the methods.
Article
Green & Sustainable Science & Technology
Esra Yalcin, Gokcen Alev Ciftcioglu, Burcin Hulya Guzel
Summary: Organizational and operational human factors have a significant impact on accidents in the chemical industry. Chemical accidents occur in various operations due to multiple factors. Understanding the relationship between these factors and accidents is crucial for preventing recurring accidents and promoting sustainability. This study divided the chemical industry into five operations and used the HFACS method to analyze accident reports. The results showed that high-frequency accidents were caused by organizational processes and skill-based errors in different operations.
Article
Engineering, Chemical
Jian-Feng Yang, Peng-Chao Wang, Xin-Yong Liu, Ming-Cheng Bian, Liang-Chao Chen, Si-Yun Lv, Jin-Fu Tao, Guan-Yu Suo, Shen-Qing Xuan, Ru Li, Jian-Wen Zhang, Chi-Min Shu, Zhan Dou
Summary: This study made loss-prevention recommendations for the chemical industry after conducting a review of accident reports and creating a complex network model. It was shown that most accidents were directly or indirectly caused by human action, and human factors played a decisive role in occurrence, evolution, and resolution. Risk abatement strategies were proposed for the crucial factors.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2023)
Article
Engineering, Chemical
Lixia Niu, Jin Zhao, Jinhui Yang
Summary: With the improvement of intelligent coal mine construction, China's coal production safety has made significant progress, but coal mine gas explosion accidents still occur due to the unsafe acts of miners. This study collected 100 cases of coal mine gas explosion in China and identified the causes of miners' unsafe acts using an improved human factors analysis and classification system (HFACS). The study also analyzed the correlation among the impacting factors using interpretive structural model (ISM) and tested the relationship between contributing factors quantitatively. Based on these findings, a Bayesian network (BN) was constructed to evaluate the probability of miners' unsafe acts in coal mine gas explosion accidents, with a probability of 20% and 52% observed. The government's insufficient crackdown on illegal activities was found to have the greatest impact on miners' violations.
Article
Engineering, Industrial
Zaili Yang, Zhisen Yang, John Smith, Bostock Adam Peter Robert
Summary: Cycling has benefits but also risks. This study develops a new risk analysis approach using Bayesian network to predict the severity of cycling accidents.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Marine
Cenk Sakar, Mustafa Sokukcu
Summary: This study combines fault tree analysis with Bayesian network analysis using a fuzzy technique to analyze accidents during pilot transfer operations. The study finds that poor condition of pilot ladder, lack of safety culture, adverse sea/swell, and securing failure of pilot ladder on deck are the major contributors to PT accidents.
Article
Chemistry, Multidisciplinary
Weiliang Qiao, Hongtongyang Guo, Enze Huang, Wanyi Deng, Chuanping Lian, Haiquan Chen
Summary: A comprehensive analysis model combining a Bayesian network (BN) and a complex network (CN) is proposed to investigate the human-related factors associated with suffocation on ships during docking repair. The model integrates the advantages of BN and CN to evaluate the human-related risk involved in the suffocation accidents on ships.
APPLIED SCIENCES-BASEL
(2022)
Article
Oceanography
Kezhong Liu, Qing Yu, Zhitao Yuan, Zhisen Yang, Yaqing Shu
Summary: The study identified the importance of different risk factors and critical scenarios in China's coastal waters, with small general cargo ships posing the highest risk. Bad weather conditions often lead to catastrophic accidents, while minor accidents tend to occur in areas with lower traffic density. The research findings can offer valuable guidance for risk prevention and improve the maritime safety management system in coastal waters.
OCEAN & COASTAL MANAGEMENT
(2021)
Article
Transportation
Baode Li, Jing Lu, Han Lu, Jing Li
Summary: This paper proposes a novel machine learning-based methodology for predicting and analyzing accident scenarios in maritime accidents to assist emergency response decision-making. The study utilizes accident data from investigation reports and develops scenario prediction models using classification and regression tree (CART) and random forest (RF) algorithms. The optimized models show that the number of people involved in an accident is the most important factor affecting the final accident scenario.
MARITIME POLICY & MANAGEMENT
(2023)
Article
Transportation
Andrew Rawson, Mario Brito
Summary: Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. This comprehensive review of academic literature in the maritime domain compares the purpose, methods, datasets, and features of studies on accident prediction, severity, ship detentions, and ship collision risk. The review also identifies challenges and highlights novel applications and key areas for further research in order to strengthen the methodological approach.
Article
Transportation
Ahmet Lutfi Tuncel, Emre Akyuz, Ozcan Arslan
Summary: This paper presents a comprehensive risk analysis for the operational transfer processes of maritime pilots using fuzzy extended fault tree analysis (FFTA). The study finds that the risk of encountering an accident during the transfer processes of maritime pilots is relatively high, highlighting the need for measures to improve safety.
MARITIME POLICY & MANAGEMENT
(2023)
Article
Transportation
Daozheng Huang, Sean Loughney, Jin Wang
Summary: With the progress of the Belt and Road initiative, China's reliance on maritime and onshore transportation has increased, leading to the need for unimpeded transportation for trade and energy resources. This study proposes a framework for the quantitative assessment of key Strategic Transport Passages (STPs) in the context of the Belt and Road, and identifies and ranks China's STPs based on their strategic values.
MARITIME POLICY & MANAGEMENT
(2023)
Article
Transportation
Ozkan Ugurlu, Saban Emre Kartal, Orcun Gundogan, Muhammet Aydin, Jin Wang
Summary: Mobbing is a fundamental problem that disrupts the organization's structure and negatively affects its employees' safe work environment. This study utilizes a dynamic Bayesian network model to analyze seafarers' mobbing behavior and recommends measures to be taken in the maritime industry.
MARITIME POLICY & MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Chao Zhao, Xiaokun Chang, Tian Xie, Hamido Fujita, Jian Wu
Summary: In this paper, an enhanced Autoencoder model is proposed to identify elevated road traffic accident (RTA) risk based on traffic anomaly detection in an unsupervised manner. By introducing an attention mechanism and an enhanced loss, the model can effectively extract traffic condition features and optimize anomaly detection performance. Experimental results demonstrate the effectiveness of the model.
APPLIED INTELLIGENCE
(2023)
Review
Engineering, Marine
Cagatay Kandemir, Metin Celik
Summary: This paper conducts a systematic literature review on human reliability studies in the maritime and offshore industries, focusing on the concepts, methodologies, and statistical data found in previous studies. It discusses the impact of emerging industrial developments on human reliability investigations and introduces a new R&D concept called Maritime & Offshore Human Reliability Research 2.0 (MOHR 2.0). The paper also provides an application protocol for extending the studies to offshore operations. Overall, this study contributes to our understanding of the potential and challenges of human reliability studies in maritime and offshore operations.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT
(2023)
Article
Engineering, Marine
Idris Turna
Summary: Voyage plans are crucial for safe navigation, and inaccuracies in these plans can lead to high rates of detention and maritime accidents. This research introduces a novel model using Fuzzy Bayesian Networks to determine the importance weights of factors in the appraisal section of voyage plans. The findings offer valuable data for developing strategies to reduce the risk of accidents and detentions.
SHIPS AND OFFSHORE STRUCTURES
(2023)
Article
Engineering, Industrial
Xuri Xin, Kezhong Liu, Sean Loughney, Jin Wang, Zaili Yang
Summary: This paper develops a new maritime traffic clustering approach for enhancing traffic pattern interpretability and discovering high-risk multi-ship encounter scenarios. The method effectively captures high-risk traffic clusters and is robust in various traffic scenarios, supporting collision risk management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Interdisciplinary Applications
Zifei Xu, Musa Bashir, Qinsong Liu, Zifan Miao, Xinyu Wang, Jin Wang, Nduka Ekere
Summary: The research aims to develop an intelligent model that can predict the remaining useful life of bearings without human intervention. The intelligent health indicator model is constructed based on an unsupervised neural network and can extract multi-scale coded features from raw vibration signals. The model is fitted with an ensemble health indicator to create a reliable indicator for RUL prediction, and three neural network-based prognostic models are developed to examine the reliability of the proposed health indicator.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Oceanography
Shiqi Fan, Eduardo Blanco-Davis, Stephen Fairclough, Jinfen Zhang, Xinping Yan, Jin Wang, Zaili Yang
Summary: Psychological factors are a critical cause of human errors in various sectors, but little research has been done in the maritime industry, despite the significant contribution of human errors to shipping accidents. This study introduces a conceptual framework for assessing seafarer psychological factors using neurophysiological analysis. The framework enables quantitative assessment of psychological factors, and can be used to test, verify, and train seafarers' behaviors for ship safety.
OCEAN & COASTAL MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Xuri Xin, Kezhong Liu, Sean Loughney, Jin Wang, Huanhuan Li, Zaili Yang
Summary: This study proposes a new traffic partitioning methodology to achieve optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Management
Hani Alyami, Zaili Yang, Ramin Riahi, Stephen Bonsall, Jin Wang, Chengpeng Wan, Zhuohua Qu
Summary: This paper proposes a fuzzy decision-making approach to evaluate risk control options and aid in the selection of operational safety strategies in container terminals. A hybrid model based on fuzzy set theory is developed to optimize operational safety performance under uncertainty. The results suggest that automation and operator training are the most effective measures.
INTERNATIONAL JOURNAL OF SHIPPING AND TRANSPORT LOGISTICS
(2023)
Article
Engineering, Civil
Ye Xiao, Xingchen Li, Wen Yao, Jin Chen, Yupeng Hu
Summary: This article introduces a bidirectional data-driven trajectory prediction method based on AIS data, which can improve the accuracy of ship trajectory prediction and reduce the risk of accidents. By studying comprehensive historical trajectories, the average prediction error is reduced by 60.28%. The method analyzes ocean and port trajectory data before and after the COVID-19 epidemic, which is of great significance for maritime safety and trade analysis.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
