Corrosion Assessment and Prediction of Steel Bridge Deck Based on Extreme Learning Machine
CHEN Qian1, WANG Chaohui1, CHEN Yuanzhao2, LI Zhenxia2, GUO Tengteng2, CHEN Haijun2
1 School of Highway, Chang'an University, Xi'an 710064, China 2 School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Abstract: Conductive gussasphalt mixture can melt snow and ice timely and efficiently, but it is easy to accelerate the corrosion of steel deck when it is powered on. It will affect the service performance and traffic safety of bridges. In order to solve the above problems, five kinds of conductive gussasphalt mixtures were designed and prepared. When conductive gussasphalt mixtures were powered on, the current density flowing through the steel plate was tested. The effects of types, working conditions and environmental factors of conductive gussasphalt mixtures on steel plate corrosion were studied. And the prediction model of steel plate corrosion based on the extreme learning machine neural network was established. It lays a solid foundation for the further popularization and application of snow melting technology of conductive gussasphalt mixture in the field of steel bridge deck pavement. The results show that the influence degree order of different factors on steel plate corrosion was ranked as follows: number of time on power>temperature>humidity>electrified time. The influence of number of time on power and temperature on steel plate corrosion were more significant at the level of 0.05. Compared with the traditional neural network prediction model, the extreme learning machine prediction model had better accuracy and efficiency. And its mean absolute error, mean absolute percent error and root mean squared error were 50.48%, 45.89% and 49.30% lower than the former, respectively.
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2020, Vol. 34 
