Hazard recognition is critical for construction safety, especially for accident prevention. Traditional methods often fail to capture the dynamic and interdependent nature of construction hazards. To address this issue, this paper proposes a network-based framework that conceptualizes construction hazards as dynamic interactions between objects with hazardous attributes. A link prediction model using Graph Neural Networks (GNNs) is integrated in this framework to automatically explore latent interactions between hazard objects that are ignored by the existing dataset. By analyzing 4470 construction accident reports, this paper constructed a hazard network and revealed key structural properties, including hazard object centrality, cliques, and communities. The experimental results of link prediction showed that the GNN-based model demonstrated superior performance compared to traditional methods, with 81 % of GNN-predicted links validated by actual construction accident cases. This framework provides a practical solution for intelligent hazard recognition and proactive risk management in the construction industry.

中文翻译：

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网络分析和基于图神经网络 （GNN） 的施工危险链路预测

危险识别对于施工安全至关重要，尤其是对于事故预防。传统方法通常无法捕捉到建筑危险的动态和相互依存的性质。为了解决这个问题，本文提出了一个基于网络的框架，该框架将建筑危险概念化为具有危险属性的物体之间的动态相互作用。该框架中集成了使用图神经网络 （GNN） 的链接预测模型，以自动探索现有数据集忽略的危险对象之间的潜在交互。通过分析 4470 份建筑事故报告，本文构建了灾害网络，揭示了灾害对象中心性、派系和群落等关键结构特性。链路预测的实验结果表明，与传统方法相比，基于 GNN 的模型表现出更好的性能，81% 的 GNN 预测链路被实际施工事故案例验证。该框架为建筑行业的智能危险识别和主动风险管理提供了实用的解决方案。