用图注意力网络预测道路退化，输出符合标准的养护优先级。
ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenance
- 融合残差图注意力与GRU，建模道路空间时间退化规律。
- 在750段道路数据上达到R²=0.93、RMSE=2.72的高预测精度。
- 模型可解释性强，结果与工程理论一致，适合低资源地区应用。
气候脆弱的道路网络亟需从被动维修转向预测性决策维护。本文提出ST-ResGAT，一种新型时空残差图注意力网络，通过融合残差图注意力编码与GRU时序聚合，实现路面退化预测。该框架专为资源受限场景设计，将连续的路面状况指数（PCI）预测直接转化为符合美国材料与试验协会（ASTM）标准的养护优先级。基于孟加拉国锡尔赫特市2021–2024年共750个路段的真实检测数据，ST-ResGAT显著优于传统非空间机器学习基线，预测精度达R² = 0.93，RMSE = 2.72。消融实验证实拓扑邻域效应的数学必要性，表明结构退化具有空间传播特性。引入GNNExplainer揭示模型可解释性，其生成的优先级与物理工程理论完全吻合。此外，分类安全性量化结果显示：85.5%精确匹配ASTM等级，100%相邻等级包含，确保预测边界可控、工程师可信赖。进一步生成局部纵向养护规划，进行气候压力测试，并推导帕累托可持续性前沿。因此，ST-ResGAT为高风险、低资源地质环境下的智能基础设施管理提供了实用、可解释且可持续的解决方案。
原文摘要 · Abstract (English)
Climate-vulnerable road networks require a paradigm shift from reactive, fix-on-failure repairs to predictive, decision-ready maintenance. This paper introduces ST-ResGAT, a novel Spatio-Temporal Residual Graph Attention Network that fuses residual graph-attention encoding with GRU temporal aggregation to forecast pavement deterioration. Engineered for resource-constrained deployment, the framework translates continuous Pavement Condition Index (PCI) forecasts directly into the American Society for Testing and Materials (ASTM)-compliant maintenance priorities. Using a real-world inspection dataset of 750 segments in Sylhet, Bangladesh (2021-2024), ST-ResGAT significantly outperforms traditional non-spatial machine learning baselines, achieving exceptional predictive fidelity (R2 = 0.93, RMSE = 2.72). Crucially, ablation testing confirmed the mathematical necessity of modeling topological neighbor effects, proving that structural decay acts as a spatial contagion. Uniquely, we integrate GNNExplainer to unbox the model, demonstrating that its learned priorities align perfectly with established physical engineering theory. Furthermore, we quantify classification safety: achieving 85.5% exact ASTM class agreement and 100% adjacent-class containment, ensuring bounded, engineer-safe predictions. To connect model outputs to policy, we generate localized longitudinal maintenance profiles, perform climate stress-testing, and derive Pareto sustainability frontiers. ST-ResGAT therefore offers a practical, explainable, and sustainable blueprint for intelligent infrastructure management in high-risk, low-resource geological settings.
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