基于极限学习机的钢桥面板腐蚀评估及预测

doi:10.11896/cldb.19050091

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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.

Key words： road materials conductive gussasphalt mixture corrosion of steel bridge deck prediction model extreme learning machine

Published: 14 July 2020

CLC number:	U416.21
	TB332

Fund:This work was financially supported by the Fundamental Research Funds for the Central Universities, CHD (300102219701, 300102219314) and Science and Technology Development Plan of Henan Province in 2018——Tackling Key Problems in Science and Technology (182102310028).

About author:: Qian Chen, Ph.D. candidate, is learning at School of Highway, Chang'an University. His research interest is new materials and technologies for green roads. He has published twelve academic papers, including ten retrieved from SCI/EI. And eight patents are authorized.
Chaohui Wangis currently professor, Ph.D. student advisor of School of Highway, Chang'an University, Young Talents in Transportation Science and Technology, Visiting Scholar of Oklahoma State University, USA, Expert Committee Member of International Slurry Mask Association. He received his Ph.D. degree in engineering from Chang'an University in July 2008. In the same year, he joined the Institute of Highway Research, Chang'an University, mainly devoting to development and application of new green road materials and technologies, preventive maintenance and decision-making optimization technology of roads and other fields. He has published more than ninety papers in the first author or newsletter author, including more than sixty retrieved from SCI/EI. And more than sixty invention patents are authorized.

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	CHEN Qian
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	CHEN Yuanzhao
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	GUO Tengteng
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Cite this article:

CHEN Qian,WANG Chaohui,CHEN Yuanzhao, et al. Corrosion Assessment and Prediction of Steel Bridge Deck Based on Extreme Learning Machine[J]. Materials Reports, 2020, 34(14): 14099-14104.

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http://www.mater-rep.com/EN/10.11896/cldb.19050091 OR http://www.mater-rep.com/EN/Y2020/V34/I14/14099

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