车辆荷载激励下的水泥混凝土路面振动信号时频能量解析及车速估算
Time-frequency Energy Analysis and Vehicle Speed Estimation of Cement Pavement Vibration Signals Under Vehicle Load
杨松立 1魏亚 2叶周景 1杨海露 1杨碧宇 3李鹏鹏 1汪林兵4
作者信息
- 1. 北京科技大学国家材料服役安全科学中心,北京 100083
- 2. 清华大学土木工程系,北京 100084
- 3. 长安大学公路学院,陕西西安 710064
- 4. 佐治亚大学环境、土木、农业和机械工程学院,佐治亚雅典GA 30602
- 折叠
摘要
道路作为重要交通基础设施,正朝着路面状态精准感知、交通运行协同管控和用户交互智能服务转型升级.路面动力响应监测通过埋设在路面中的传感器,避免了人为和环境的干扰,实时、连续地收集关于交通载荷和路面状况的数据,是实现智慧公路的重要途径.然而,传感器的植入式安装易对路面造成损伤,且传感器耐久性不足,海量的原位监测数据解析存在困难.这些问题制约了植入式传感器在公路中的推广和应用.基于此,提出一种基于水泥混凝土路面滑模摊铺施工的传感器植入工艺,实现传感器安装和路面一体化摊铺成型.通过实地测试,采用改进的模态分析方法和连续小波变换分析技术,从振动信号时频能量的角度分析了不同车速下振动信号的时频特征,提出了基于振动信号时频能量变化率的车速计算方法,适用于刚性路面振动信号的交通信息解析.该方法计算所得的车速误差在8％以内,可有效提升基于振动信号的刚性路面车速估算的准确性.通过解决植入传感器与路面集成以及信号处理方面的挑战,研究成果有助于推动更高效、响应更快的智能高速公路技术发展.
Abstract
As critical transportation infrastructures,roads are being developed to include precise pavement condition sensing,coordinated traffic management,and intelligent user-interactive services.Pavement dynamic response monitoring through embedded sensors offers real-time continuous data collection on traffic loads and pavement conditions,effectively minimizing interference from human and environmental factors.This is essential for the development of intelligent highways.However,challenges remain in embedded sensor installation techniques,such as durability and efficient processing of vast amounts of in situ monitoring data.This study introduces an innovative sensor-embedding method integrated with slipform paving for cement pavements,thereby enabling seamless sensor installation during pavement paving.Field tests were conducted using the advanced empirical mode decomposition method and continuous wavelet transform technique to analyze the time-frequency characteristics of the vibration signals at various vehicle speeds from an energy perspective.Our approach demonstrated high accuracy,with speed estimation errors consistently below 8％.This significant improvement in vibration-based speed estimation enhances the traffic monitoring capabilities of rigid pavements.By addressing the challenges of embedded sensor integration and signal processing,this study contributes to the advancement of more efficient and responsive smart highway technologies.
关键词
路面工程/车速估算/经验模态分解/水泥混凝土路面/植入式监测Key words
pavement engineering/vehicle speed estimation/empirical mode decomposition/ce-ment concrete pavement/embedded monitoring引用本文复制引用
出版年
2024
NETL
NSTL
万方数据