Using multiple linear regression and BP neural network to predict critical meteorological conditions of expressway bridge pavement icing.

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.