用可解释AI分析洪水如何加速路面老化，为防灾提供数据支持。
Evaluating Pavement Deterioration Rates Due to Flooding Events Using Explainable AI
- 结合20年路况数据与洪水信息，量化洪水对路面粗糙度的影响。
- 洪水路段的平整度恶化速度比非洪水路段快30%以上。
- 通过SHAP和LIME技术揭示洪水影响的关键因素，适合交通部门决策参考。
洪水会显著破坏路面基础设施，引发即时与长期的结构及功能问题。本研究聚焦于洪水事件对路面劣化的影响，通过德克萨斯州公路部（TxDOT）的路面管理系统（PMIS）数据库中20年的路面状况数据，结合洪水事件的持续时间和空间范围进行分析。采用统计方法对比洪水前后国际平整度指数（IRI）的变化，计算出受洪水影响的路面劣化速率。同时，应用可解释人工智能技术（如SHAP、LIME），评估洪水对路面性能的具体影响。结果表明，受洪水影响的路段平整度恶化速度显著高于未受洪水影响路段。研究强调需采取主动减灾措施，包括改善排水系统、使用抗洪材料和预防性养护，以提升易受灾区域的路面韧性。
原文摘要 · Abstract (English)
Flooding can damage pavement infrastructure significantly, causing both immediate and long-term structural and functional issues. This research investigates how flooding events affect pavement deterioration, specifically focusing on measuring pavement roughness by the International Roughness Index (IRI). To quantify these effects, we utilized 20 years of pavement condition data from TxDOT's PMIS database, which is integrated with flood event data, including duration and spatial extent. Statistical analyses were performed to compare IRI values before and after flooding and to calculate the deterioration rates influenced by flood exposure. Moreover, we applied Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to assess the impact of flooding on pavement performance. The results demonstrate that flood-affected pavements experience a more rapid increase in roughness compared to non-flooded sections. These findings emphasize the need for proactive flood mitigation strategies, including improved drainage systems, flood-resistant materials, and preventative maintenance, to enhance pavement resilience in vulnerable regions.
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