Investigation of the risk influential factors of maritime accidents: A novel topology and robustness analytical framework

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety Pub Date : 2024-11-06 DOI:10.1016/j.ress.2024.110636

Yuhao Cao , Manole Iulia , Arnab Majumdar , Yinwei Feng , Xuri Xin , Xinjian Wang , Huanxin Wang , Zaili Yang

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引用次数: 0

Abstract

This study aims to develop a novel and fully data-driven approach to analyse the maritime accidents risk influential factors (RIFs) by integrating Association Rule Mining (ARM) and Complex Network (CN) modelling. Firstly, a comprehensive dataset comprising 21,206 maritime accident records from Marine Accident Investigation Branch and Transportation Safety Board is collected and processed to serve as the foundational data source supporting the development of the new approach. Secondly, a novel Combined Association Rule Mining method is proposed to extract the interconnections among RIFs, with the mined results mapped into a CN framework. Finally, two importance ranking algorithms, namely the PageRank-Information-Entropy algorithm and edge betweenness centrality, are applied to identify the key RIFs and their information transmission paths. By simulating deliberate and random attacks within networks, a robustness analysis is conducted to further explore the evolution of RIFs. The findings reveal that ship-related factors demonstrate greater centrality and connectivity, exerting a more substantial influence on information propagation within the network structure. The robustness analysis illustrates that strategic node and edge removals are effective in preventing risk propagation. It therefore makes contributions to the development of a theoretical basis for stakeholders to develop cost-effective preventive measures against specific RIFs, ultimately enhancing maritime safety.

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海上事故风险影响因素调查：新型拓扑和稳健性分析框架

本研究旨在通过整合关联规则挖掘（ARM）和复杂网络（CN）建模，开发一种新颖的、完全由数据驱动的方法来分析海上事故风险影响因素（RIFs）。首先，从海上事故调查处和运输安全委员会收集并处理了 21,206 条海上事故记录，作为支持新方法开发的基础数据源。其次，提出了一种新颖的组合关联规则挖掘方法来提取 RIF 之间的相互联系，并将挖掘结果映射到 CN 框架中。最后，应用两种重要性排序算法（即 PageRank-Information-Entropy 算法和边缘间度中心性）来识别关键 RIF 及其信息传输路径。通过模拟网络中的蓄意攻击和随机攻击，进行了鲁棒性分析，以进一步探索 RIF 的演变。研究结果表明，与船舶相关的因素表现出更高的中心性和连通性，对网络结构内的信息传播产生了更大的影响。稳健性分析表明，战略性节点和边缘移除可有效防止风险传播。因此，该研究有助于为利益相关者提供理论依据，以针对特定的风险影响因素制定具有成本效益的预防措施，最终提高海事安全。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Reliability Engineering & System Safety 管理科学-工程：工业

CiteScore

15.20

自引率

39.50%

发文量

621

审稿时长

67 days

期刊介绍： Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.