标题： 预防性维护后路面抗滑性和表面纹理的退化建模 显示英文标题

标题： Modeling the Deterioration of Pavement Skid Resistance and Surface Texture After Preventive Maintenance

摘要： 本研究调查了使用微铣削技术进行预防性维护后，抗滑性和表面宏观纹理的退化情况。 从德克萨斯州四个气候区的31段沥青路面收集了现场数据，涵盖了多种表面类型、铣削深度、作业速度和鼓形配置。 遵循标准化的数据收集协议，在铣削前、处理后立即以及处理后3、6、12和18个月进行测量。 使用抗滑值和平均剖面深度（MPD）来评估表面摩擦和纹理特性。 数据集被重新格式化为包含930个观测值的时间序列结构，包括气候区、处理参数和初始表面状况等上下文变量。 应用了比较建模框架，以预测抗滑性和宏观纹理随时间的退化趋势。 评估了八种回归模型，包括线性、树基和集成方法，以及一个序列到单个的变压器模型。 结果表明，变压器模型在抗滑性预测中取得了最高的准确性（R2=0.981），而随机森林在宏观纹理预测中表现最佳（R2 = 0.838）。 研究结果表明，预防性维护后表面特性的退化是非线性的，并受到环境和操作因素组合的影响。 本研究表明，数据驱动建模在支持交通机构进行路面性能预测和维护规划方面的有效性。 显示英文摘要

摘要： This study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas, encompassing a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, incorporating contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a sequence-to-one transformer model. Results show that the transformer model achieved the highest prediction accuracy for skid resistance (R2=0.981), while Random Forest performing best for macrotexture prediction (R2 = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is nonlinear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning.