Pavement rutting poses a significant challenge in flexible pavements, necessitating costly asphalt resurfacing. To address this issue comprehensively, we propose an advanced Bayesian hierarchical framework of latent Gaussian models with spatial components. Our model provides a thorough diagnostic analysis, pinpointing areas exhibiting unexpectedly high rutting rates. Incorporating spatial and random components, and important explanatory variables like annual average daily traffic (traffic intensity), pavement type, rut depth and lane width, our proposed models account for and estimate the influence of these variables on rutting. This approach not only quantifies uncertainties and discerns locations at the highest risk of requiring maintenance, but also uncover spatial dependencies in rutting (millimetre/year). We apply our models to a data set spanning eleven years (2010-2020). Our findings emphasize the systematic unexpected spatial rutting effect, where more of the rutting variability is explained by including spatial components. Pavement type, in conjunction with traffic intensity, is also found to be the primary driver of rutting. Furthermore, the spatial dependencies uncovered reveal road sections experiencing more than 1 millimeter of rutting beyond annual expectations. This leads to a halving of the expected pavement lifespan in these areas. Our study offers valuable insights, presenting maps indicating expected rutting, and identifying locations with accelerated rutting rates, resulting in a reduction in pavement life expectancy of at least 10 years.

翻译：路面车辙是柔性路面面临的严峻挑战，需要昂贵的沥青罩面修复。为全面解决这一问题，我们提出了一种包含空间分量的潜高斯模型的高级贝叶斯分层框架。该模型可进行深入诊断分析，精确定位车辙速率异常偏高的区域。通过整合空间分量与随机分量，以及年平均日交通量（交通强度）、路面类型、车辙深度及车道宽度等重要解释变量，所提模型能够解释并估算这些变量对车辙的影响。该方法不仅量化了不确定性，识别出维护需求风险最高的位置，还揭示了车辙（毫米/年）的空间依赖性。我们将所提模型应用于跨越十一年（2010-2020年）的数据集。研究结果强调了系统性的空间车辙异常效应：纳入空间分量后可解释更多车辙变异。路面类型与交通强度共同被确认为车辙的主要驱动因素。此外，揭示的空间依赖性表明，某些路段的车辙深度超出年度预期值1毫米以上，导致这些区域的路面预期寿命减半。本研究提供了有价值的洞见，绘制了预期车辙分布图，并识别出车辙速率加速（导致路面预期寿命缩短至少10年）的定位区域。