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.

翻译：易受气候影响的公路网络需要从被动的、故障后修复模式转向预测性的、决策就绪的维护模式。本文提出了ST-ResGAT，一种新颖的时空残差图注意力网络，它融合了残差图注意力编码与GRU时间聚合来预测路面性能退化。该框架专为资源受限的部署环境设计，能将连续的路面状况指数（PCI）预测结果直接转换为符合美国材料与试验协会（ASTM）标准的维护优先级。基于孟加拉国锡莱特地区750个路段在2021-2024年间的真实检测数据集，ST-ResGAT显著优于传统的非空间机器学习基线模型，取得了卓越的预测保真度（R2 = 0.93，RMSE = 2.72）。至关重要的是，消融实验证实了建模拓扑邻居效应的数学必要性，证明结构性能衰减表现为一种空间传染现象。我们创新性地集成了GNNExplainer来打开模型黑箱，证明其学习到的优先级与既有的物理工程理论完全一致。此外，我们量化了分类安全性：实现了85.5%的ASTM等级精确一致率和100%的相邻等级包含率，确保了有界的、对工程安全的预测。为了将模型输出与政策制定联系起来，我们生成了本地化的纵向维护剖面图，进行了气候压力测试，并推导出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一个实用、可解释且可持续的蓝图。