用图模型分析桥梁在灾害中的多重作用，助力有限预算下科学维护决策。
Bridge-Centered Metapath Classification Using R-GCN-VGAE for Disaster-Resilient Maintenance Decisions
- 基于多路径结构构建城市异构图，融合道路、桥梁与建筑数据
- 通过R-GCN-VGAE识别桥梁的三类灾时角色：物流、医疗与居住保障
- 方法可推广至不同规模城市，支持灾后维护资源精准分配
日常基础设施管理对城市韧性至关重要。当桥梁能抵御灾害外力时，通过桥梁连接国道至医院、商店、住宅的多路径仍可维持，保障基本城市功能。然而，在预算有限情况下，需量化桥梁在灾害场景下的多维角色——现有单指标方法难以应对此挑战。本文聚焦从国道经桥梁到建筑（医院、商店、住宅）的多路径，构建包含道路、桥梁、建筑三层的异构图。采用关系中心的图卷积网络变分自编码器（R-GCN-VGAE）学习多路径特征表示，实现桥梁在灾备中的分类：供应链（商业物流）、医疗可达性（紧急医疗）、居民防护（防止孤立）。基于OSMnx与公开数据，在日本茨城县三个城市（水户市697座、鹿岛市258座、守谷市148座，共1,103座桥梁）验证方法有效性。异构图构建基于开放数据，重新定义桥梁灾时角色，支持维护预算决策。贡献包括：（1）基于开放数据构建城市异构图的方法；（2）基于多路径的桥梁灾时角色重定义；（3）维护预算决策支持方法建立；（4）跨不同城市规模验证k-NN调参策略；（5）实证显示UMAP在多角色桥梁可视化中优于t-SNE/PCA。
原文摘要 · Abstract (English)
Daily infrastructure management in preparation for disasters is critical for urban resilience. When bridges remain resilient against disaster-induced external forces, access to hospitals, shops, and residences via metapaths can be sustained, maintaining essential urban functions. However, prioritizing bridge maintenance under limited budgets requires quantifying the multi-dimensional roles that bridges play in disaster scenarios -- a challenge that existing single-indicator approaches fail to address. We focus on metapaths from national highways through bridges to buildings (hospitals, shops, residences), constructing a heterogeneous graph with road, bridge, and building layers. A Relation-centric Graph Convolutional Network Variational Autoencoder (R-GCN-VGAE) learns metapath-based feature representations, enabling classification of bridges into disaster-preparedness categories: Supply Chain (commercial logistics), Medical Access (emergency healthcare), and Residential Protection (preventing isolation). Using OSMnx and open data, we validate our methodology on three diverse cities in Ibaraki Prefecture, Japan: Mito (697 bridges), Chikusei (258 bridges), and Moriya (148 bridges), totaling 1,103 bridges. The heterogeneous graph construction from open data enables redefining bridge roles for disaster scenarios, supporting maintenance budget decision-making. We contributed that (1) Open-data methodology for constructing urban heterogeneous graphs. (2) Redefinition of bridge roles for disaster scenarios via metapath-based classification. (3) Establishment of maintenance budget decision support methodology. (4) k-NN tuning strategy validated across diverse city scales. (5) Empirical demonstration of UMAP superiority over t-SNE/PCA for multi-role bridge visualization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。