Li Pengpeng, Wang Linbing, Yang Songli, Ye Zhoujing
National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China.
School of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.
Sensors (Basel). 2025 Jun 30;25(13):4066. doi: 10.3390/s25134066.
The structural integrity of cement concrete pavements, paramount for ensuring traffic safety and operational efficiency, faces mounting challenges from the escalating burden of heavy-duty vehicular traffic. Precise characterisation of pavement dynamic responses under such conditions proves indispensable for implementing effective structural health monitoring and early warning system deployment. This investigation examines the triaxial dynamic response characteristics of cement concrete pavement subjected to low-speed, heavy-duty vehicular excitations, employing data acquired through in situ field measurements. A monitoring system incorporating embedded triaxial MEMS accelerometers was developed to capture vibration signals directly within the pavement structure. Raw data underwent preprocessing utilising a smoothing wavelet transform technique to attenuate noise, followed by empirical mode decomposition (EMD) and short-time energy (STE) analysis to scrutinise the time-frequency and energetic properties of triaxial vibration signals. The findings demonstrate that heavy, slow-moving vehicles generate substantial triaxial vibrations, with the vertical (-axis) response exhibiting the greatest amplitude and encompassing higher dominant frequency components compared to the horizontal (X and Y) axes. EMD successfully decomposed the complex signals into discrete intrinsic mode functions (IMFs), identifying high-frequency components (IMF1-IMF3) associated with transient vehicular impacts, mid-frequency components (IMF4-IMF6) presumably linked to structural and vehicle dynamics, and low-frequency components (IMF7-IMF9) representing system trends or ambient noise. The STE analysis of the selected IMFs elucidated the transient nature of axle loading, revealing pronounced, localised energy peaks. These findings furnish a comprehensive understanding of the dynamic behaviour of cement concrete pavements under heavy vehicle loads and establish a robust methodological framework for pavement performance assessment and refined axle load identification.
水泥混凝土路面的结构完整性对于确保交通安全和运营效率至关重要，但面临着来自重型车辆交通负担不断加重的日益严峻的挑战。准确表征在这种情况下路面的动态响应对于实施有效的结构健康监测和部署早期预警系统而言必不可少。本研究通过现场实测获取的数据，考察了水泥混凝土路面在低速、重型车辆激励下的三轴动态响应特性。开发了一种包含嵌入式三轴MEMS加速度计的监测系统，以直接在路面结构内捕获振动信号。原始数据利用平滑小波变换技术进行预处理以衰减噪声，随后进行经验模态分解（EMD）和短时能量（STE）分析，以仔细研究三轴振动信号的时频和能量特性。研究结果表明，重型、缓慢行驶的车辆会产生大量的三轴振动，与水平（X和Y）轴相比，垂直（Z轴）响应的振幅最大且包含更高的主导频率成分。EMD成功地将复杂信号分解为离散的固有模态函数（IMF），识别出与车辆瞬态冲击相关的高频成分（IMF1-IMF3）、可能与结构和车辆动力学相关的中频成分（IMF4-IMF6）以及代表系统趋势或环境噪声的低频成分（IMF7-IMF9）。对选定IMF的STE分析阐明了轴载的瞬态特性，揭示了明显的局部能量峰值。这些发现提供了对重型车辆荷载作用下水泥混凝土路面动态行为的全面理解，并为路面性能评估和精确轴载识别建立了一个强大的方法框架。
