Risk factors extraction and analysis of Chinese ship collision accidents based on knowledge graph

IF 4.6 2区工程技术 Q1 ENGINEERING, CIVIL

Ocean Engineering Pub Date : 2025-02-04 DOI:10.1016/j.oceaneng.2025.120536

Jihong Chen , Chenglin Zhuang , Jia Shi , Houqiang Jiang , Jinyu Xu , Jutong Liu

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引用次数: 0

Abstract

Shipping is a crucial mode of transportation. The high density of ship activities in Chinese waters increases the likelihood and severity of shipping accidents, which can significantly impact the global supply chain and shipping network operations. Among various maritime accidents, collisions are the most prevalent. Knowledge graphs, using triples (entity-relation-entity) as basic units, describe real-world concepts and relationships through text information, which aid in the causal analysis of accidents. This paper analyzes text data from Chinese ship collision accident reports and employs joint triple extraction algorithms based on deep learning and CART (Classification and Regression Tree) algorithm to construct a knowledge graph of these accidents, visualized using Gephi software. Utilizing complex network theory, a series of safety-related topological indicators are defined to perform quantitative risk assessment, identify key risk factors, and propose preventive measures, offering significant reference value for preventing ship collisions and other maritime accidents in Chinese waters.

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来源期刊

Ocean Engineering 工程技术-工程：大洋

CiteScore

7.30

自引率

34.00%

发文量

2379

审稿时长

8.1 months

期刊介绍： Ocean Engineering provides a medium for the publication of original research and development work in the field of ocean engineering. Ocean Engineering seeks papers in the following topics.