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), asphalt 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 emphasise the systematic unexplained spatial rutting effect, where some of the rutting variability is accounted for by spatial components, asphalt 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：路面车辙在柔性路面中构成重大挑战，需要昂贵的沥青重铺工程。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。该模型提供全面的诊断分析，精确定位表现出异常高车辙率的区域。通过纳入空间与随机分量，以及年平均日交通量（交通强度）、沥青类型、车辙深度和车道宽度等重要解释变量，我们提出的模型能够解释并估计这些变量对车辙形成的影响。该方法不仅量化了不确定性、识别出最可能需要维护的高风险路段，同时揭示了车辙（毫米/年）的空间依赖性。我们将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未解释空间车辙效应：部分车辙变异可由空间分量解释，而沥青类型与交通强度共同被确认为车辙形成的主要驱动因素。此外，揭示的空间依赖性显示某些路段的车辙发展超出年度预期值1毫米以上，导致这些区域的路面预期寿命减半。本研究提供了重要见解，通过绘制预期车辙分布图，识别出车辙加速发展区域——这些区域的路面使用寿命预计将缩短至少十年。