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随着城市交通的快速发展，路面性能直接关系到交通运输的安全性和环境保护。为了对沥青路面车辙深度有效建模，构建FGM(1,1 sin)模型，旨在提高对沥青路面车辙深度演化过程的预测精度。由于RIOHTrack实验中的车辙深度长期演化存在季节性回弹、数据样本少和采样周期长等问题，因此，在传统灰色模型的基础上引入正弦算子，以适应沥青路面车辙深度数据的特点。通过引入分数阶累加算子，采用时间权重，增强了模型对不同路面车辙深度数据的挖掘能力，并能更准确地描述车辙发展规律。此外，在RIOHTrack实验场的19种不同沥青路面上对该模型进行了测试，相较于其他现有算法，展现了更高的预测精度。研究对沥青路面的结构设计、材料使用和养护决策提供了有力的数据支持。
Abstract：With the rapid development of urban transportation, pavement performance is directly related to the safety of transportation and environmental protection. To effectively model the rutting depth of asphalt pavements this study is based on the FGM(1, 1│sin) model, which aims to improve the prediction accuracy of the rutting disease evolution process of asphalt pavements. The model combines the fractional-order accumulation operator and the sinusoidal operator to accommodate the characteristics of asphalt pavement rutting depth data, such as long sampling periods, small data samples, and the presence of oscillations. By introducing time weights, the model enhances the ability to mine the rut depth data of different pavements and describes the rut development law more accurately. In addition, the model was tested on 19 different asphalt pavements at the RIOHTrack experimental site and demonstrated higher prediction accuracy compared to other existing algorithms, providing strong data support for structural design, material use, and maintenance decisions of asphalt pavements.
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基本信息:
DOI：10.12194/j.ntu.20240523001
中图分类号:U418.68;U416.217
引用信息:
[1]陈一帆,李卓轩,曹进德.基于FGM(1,1 sin)模型的沥青路面车辙深度预测[J].南通大学学报(自然科学版),2024,23(04):17-22+44.DOI:10.12194/j.ntu.20240523001.
基金信息:
国家重点研发计划项目（2020YFA0714300）; 综合交通运输理论交通运输行业重点实验室（南京现代综合交通实验室）开放课题资助项目（MTF2023004）