Chang Congyi, Kong Linghui, Han Libin, Li Junmin, Pan Shuo, Wei Ya
Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing 100084, China.
Southwest United Graduate School, Kunming 650092, China.
Sensors (Basel). 2025 May 4;25(9):2898. doi: 10.3390/s25092898.
Monitoring the vibration response of Portland cement concrete (PCC) pavement under dynamic vehicle loading is critical for road maintenance and traffic analysis. This study embedded micro-electro-mechanical systems (MEMS) accelerometer sensors in PCC pavement to capture vibration signals induced by vehicles. A thresholding method is proposed to automate vehicle detection by analyzing acceleration time-domain data, achieving precision and recall rates exceeding 85%. The study also explored various sensor placement locations and different threshold values for acceleration time-domain signals. Sensor placement optimization revealed that positioning sensors at the front or rear ends of pavement slabs maximizes vibration response, enabling low-cost and efficient detection. Experimental results demonstrated that the proposed method balances simplicity and accuracy, eliminating the need for complex denoising processes. This approach provides a cost-effective solution for real-time vehicle detection and enhances pavement performance monitoring, supporting improved maintenance and traffic management strategies.
监测动态车辆荷载作用下的波特兰水泥混凝土(PCC)路面的振动响应对于道路养护和交通分析至关重要。本研究将微机电系统(MEMS)加速度计传感器嵌入PCC路面,以捕捉车辆引起的振动信号。提出了一种阈值方法,通过分析加速度时域数据来自动检测车辆,精度和召回率超过85%。该研究还探讨了各种传感器放置位置以及加速度时域信号的不同阈值。传感器放置优化表明,将传感器放置在路面板的前端或后端可使振动响应最大化,从而实现低成本和高效检测。实验结果表明,所提出的方法在简单性和准确性之间取得了平衡,无需复杂的去噪过程。这种方法为实时车辆检测提供了一种经济高效的解决方案,并增强了路面性能监测,支持改进的养护和交通管理策略。