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基于智能手机传感器和SVM的道路表面粗度评估和分析的研究

B Yiliguoqi¹, B Siriguleng², Arong³

¹Department of Transport and Municipal Engineering, Inner Mongolia Technical University of Construction, Hohhot Inner Mongolia, 010000, China.

Scientific reports

|January 22, 2026

概括

这项研究使用智能手机传感器来评估道路粗度,以80-100%的准确度将质量归类为好或差. 这种具有成本效益的方法有助于道路维护规划.

科学领域:

土木工程土木工程是指土木工程.
运输工程运输工程
地理空间分析的研究.

背景情况:

道路表面质量对车辆性能,安全和基础设施寿命有重大影响.
传统的道路粗度评估方法可能昂贵且耗时.
开发可访问和具有成本效益的道路质量评估工具对于高效的基础设施管理至关重要.

研究的目的:

通过智能手机惯性测量单元 (IMU) 数据引入一种低成本的方法来评估路面质量和粗度.
根据加速度计和陀螺仪的读数,评估分类道路粗度水平的可行性.
为了证明支持向量机 (SVM) 分类器对此任务的有效性.

主要方法:

使用加速仪和陀螺仪数据,从低成本智能手机以10 Hz的频率采集样本.
从垂直加速和滚动运动中提取标准偏差和四分位数间范围等统计特征.
培训支持矢量机 (SVM) 分类器,以区分"好"和"差"的道路粗度水平,参考国际粗度指数 (IRI) 值.

主要成果:

拟议的方法在区分道路粗度和质量水平方面实现了80-100%的分类准确性.
实验验证是使用四辆车在多个50米路段的数据进行的.
结果表明,即使是来自智能手机的低频IMU信号,也足以进行有效的道路粗性选.

结论:

基于智能手机的IMU数据为道路表面质量和粗度的评估提供了可行且具有成本效益的解决方案.
在识别不同的道路粗度类别时,SVM分类器表现出强大的性能.
这种方法对积极的道路维护规划和资源分配有重大影响.

关键词:

维护维护维护维护粗性粗性粗性简单的诊断简单的诊断标准偏差的标准偏差是什么支持矢量机器 (SVM) 是一个支持矢量机器.

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