Li Jilu, Ma Hua, Shi Wei, Tan Yiqiu, Xu Huining, Zheng Bin, Liu Jie
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China.
Xingtai Pavement & Bridge Construction Group Co., Ltd., Xingtai 054000, China.
Materials (Basel). 2023 Oct 3;16(19):6539. doi: 10.3390/ma16196539.
Monitoring and warning of ice on pavement surfaces are effective means to improve traffic safety in winter. In this study, a high-precision piezoelectric sensor was developed to monitor pavement surface conditions. The effects of the pavement surface temperature, water depth, and wind speed on pavement icing time were investigated. Then, on the basis of these effects, an early warning model of pavement icing was proposed using an artificial neural network. The results showed that the sensor could detect ice or water on the pavement surface. The measurement accuracy and reliability of the sensor were verified under long-term vehicle load, temperature load, and harsh natural environment using test data. Moreover, pavement temperature, water depth, and wind speed had a significant nonlinear effect on the pavement icing time. The effect of the pavement surface temperature on icing conditions was maximal, followed by the effect of the water depth. The effect of the wind speed was moderate. The model with a learning rate of 0.7 and five hidden units had the best prediction effect on pavement icing. The prediction accuracy of the early warning model exceeded 90%, permitting nondestructive and rapid detection of pavement icing based on meteorological information.
监测和预警路面结冰情况是提高冬季交通安全的有效手段。在本研究中，开发了一种高精度压电传感器来监测路面状况。研究了路面表面温度、水深和风速对路面结冰时间的影响。然后，基于这些影响，利用人工神经网络提出了路面结冰预警模型。结果表明，该传感器能够检测到路面表面的冰或水。利用测试数据在长期车辆荷载、温度荷载和恶劣自然环境下验证了该传感器的测量精度和可靠性。此外，路面温度、水深和风速对路面结冰时间有显著的非线性影响。路面表面温度对结冰情况的影响最大，其次是水深的影响。风速的影响适中。学习率为0.7且有五个隐藏单元的模型对路面结冰具有最佳预测效果。预警模型的预测准确率超过90%，能够基于气象信息对路面结冰进行无损快速检测。
