Jiang Shengchuan, Wang Hui, Fan Wenruo, Chi Min, Zhang Xun, Ma Jinlong
Department of Traffic Engineering, Business School, University of Shanghai for Science and Technology, Shanghai 200090, China.
School of Civil Engineering, Chongqing University, Chongqing 400045, China.
Sensors (Basel). 2025 Mar 10;25(6):1721. doi: 10.3390/s25061721.
This study proposes a non-contact framework for evaluating the skid resistance of shared roadside pavements to improve cyclist and pedestrian safety. By integrating a friction tester and a laser scanner, we synchronize high-resolution three-dimensional (3D) surface texture characterization with friction coefficient measurements under dry and wet conditions. Key metrics-including fractal dimension (), macro/micro-texture depth density ( and ), mean texture depth (), and joint dimensions-were derived from 3D laser scans. A hierarchical regression analysis was employed to prioritize the influence of texture and joint parameters on skid resistance across environmental conditions. Combined with material types (brick, tile, and stone) and drainage performance, these metrics are systematically analyzed to quantify their correlations with skid resistance. Results indicate that raised macro-textures and high (>2.5) significantly enhance dry-condition skid resistance, whereas recessed textures degrade performance. The hierarchical model further reveals that and dominate dry friction ( = 0.61 and -0.53, respectively), while micro-texture density () and seam depth are critical predictors of wet skid resistance ( = -0.76 and 0.31). In wet environments, skid resistance is dominated by micro-texture density ( < 3500) and macro-texture-driven water displacement, with higher values indicating denser micro-textures that impede drainage. The study validates that non-contact laser scanning enables efficient mapping of critical texture data (e.g., pore connectivity, joint depth ≥0.25 mm) and friction properties, supporting rapid large-scale pavement assessments. These findings establish a data-driven linkage between measurable surface indicators (texture, morphometry, drainage) and skid resistance, offering a practical foundation for proactive sidewalk safety management, especially in high-risk areas. Future work should focus on refining predictive models through multi-sensor fusion and standardized design guidelines.
本研究提出了一种用于评估共享路边路面防滑性能的非接触式框架，以提高骑自行车者和行人的安全性。通过集成摩擦测试仪和激光扫描仪，我们在干燥和潮湿条件下将高分辨率三维（3D）表面纹理表征与摩擦系数测量同步进行。关键指标——包括分形维数（）、宏观/微观纹理深度密度（和）、平均纹理深度（）和接缝尺寸——来自3D激光扫描。采用分层回归分析来确定纹理和接缝参数在不同环境条件下对防滑性能影响的优先级。结合材料类型（砖、瓦和石材）和排水性能，对这些指标进行系统分析，以量化它们与防滑性能的相关性。结果表明，凸起的宏观纹理和高分形维数（>2.5）显著提高干燥条件下的防滑性能，而凹陷纹理则会降低性能。分层模型进一步表明，和主导干燥摩擦（分别为 = 0.61和 -0.53），而微观纹理密度（）和接缝深度是潮湿条件下防滑性能的关键预测指标（分别为 = -0.76和0.31）。在潮湿环境中，防滑性能由微观纹理密度（<3500）和宏观纹理驱动的排水主导，较高的值表明微观纹理更密集，阻碍排水。该研究验证了非接触式激光扫描能够有效地绘制关键纹理数据（如孔隙连通性、接缝深度≥0.25毫米）和摩擦特性，支持快速的大规模路面评估。这些发现建立了可测量的表面指标（纹理、形态测量、排水）与防滑性能之间的数据驱动联系，为积极的人行道安全管理提供了实际基础，特别是在高风险地区。未来的工作应侧重于通过多传感器融合和标准化设计指南来完善预测模型。
