College of Transportation Engineering, Chang'an University, Xi'an, China.
School of Highway, Chang'an University, Xi'an, China.
PLoS One. 2022 Feb 4;17(2):e0263539. doi: 10.1371/journal.pone.0263539. eCollection 2022.
Icy bridge deck in winter has tremendous consequences for expressway traffic safety, which is closely related to the bridge pavement temperature. In this paper, the critical meteorological conditions of icy bridge deck were predicted by multiple linear regression and BP neural network respectively. Firstly, the main parameters affecting the bridge pavement temperature were determined by Pearson partial correlation analysis based on the three-year winter meteorological data of the traffic meteorological monitoring station on the bridge in Shandong province. Secondly, the bridge pavement temperature is selected as the dependent variable, while air temperature, wind speed, relative humidity, dew point temperature, wet bulb temperature and wind cold temperature were selected as independent variables, and the bridge pavement temperature prediction models of linear regression and 5-layer hidden layer classical BP neural network regression were established respectively based on whether the variables are linear or not. Finally, the prediction accuracy of the above models was compared by using the measured data. The results show that the linear regression model could be established only with air temperature, relative humidity and wind speed, owing to collinearity problem. Compared with multiple linear regression model, the predicted value of the BP neural network has a higher degree of fitting with the measured data, and the coefficient of determination reaches 0.7929. Using multiple linear regression and BP neural network, the critical meteorological conditions of bridge deck icing in winter can be effectively predicted even when the sample size is insufficient.
冬季冰雪桥面会对高速公路交通安全产生巨大影响，而桥面温度与冰雪的形成密切相关。本文分别采用多元线性回归和 BP 神经网络对冬季桥面结冰的临界气象条件进行了预测。首先，基于山东省某桥上交通气象监测站三年的冬季气象数据，采用 Pearson 偏相关分析方法确定了影响桥面温度的主要参数。其次，以桥面温度为因变量，以气温、风速、相对湿度、露点温度、湿球温度和风寒温度为自变量，在自变量是否线性的基础上分别建立了线性回归和 5 层隐层经典 BP 神经网络回归的桥面温度预测模型。最后，通过实测数据对各模型的预测精度进行了对比。结果表明，由于存在共线性问题，仅能用气温、相对湿度和风速建立线性回归模型。与多元线性回归模型相比，BP 神经网络的预测值与实测数据拟合程度更高，决定系数达到 0.7929。利用多元线性回归和 BP 神经网络，即使样本量不足，也能有效预测冬季桥面结冰的临界气象条件。
