Evaluation and prediction for effect of conductive gussasphalt mixture on corrosion of steel bridge deck

被引:18
作者
Chen, Qian [1 ]
Wang, Chaohui [1 ]
Sun, Xiaolong [2 ]
Cao, Yangsen [1 ]
Guo, Tengteng [3 ]
Chen, Jiao [4 ]
机构
[1] Changan Univ, Sch Highway, Xian 710064, Shaanxi, Peoples R China
[2] Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Guangdong, Peoples R China
[3] North China Univ Water Resources & Elect Power, Sch Civil Engn & Commun, Zhengzhou 450045, Henan, Peoples R China
[4] Zhongjiaotongli Construct Co Ltd, Xian 710065, Shaanxi, Peoples R China
关键词
Road engineering; Snow melting; Conductive gussasphalt mixture; Steel deck corrosion; Prediction model; Extreme learning machine; PERFORMANCE EVALUATION; ASPHALT CONCRETE; COEFFICIENT; SURFACE; IMPACT; CYCLE;
D O I
10.1016/j.conbuildmat.2019.116837
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Conductive gussasphalt mixture can melt snow on the bridge deck, but it may corrode steel bridge deck and have an impact on traffic environment and safety when the power is on. To solve this problem, five conductive gussasphalt mixtures were prepared, and the effects of mixture type, working conditions and environmental factors of conductive gussasphalt mixture on corrosion of steel bridge deck was studied systemically. Based on the extreme learning machine optimized by genetic algorithm, the corrosion degree prediction model of steel bridge deck was established. The results indicated that number of times on power, mixture type and temperature had significant effects on the corrosion of steel deck, and their contribution rates were 58.47%, 24.62% and 15.40%, respectively. After optimization by genetic algorithm, the error of extreme learning machine model was 0.40-9.25%. Compared with the traditional extreme learning machine model, they decreased by 5.31-10.63%. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数:12
相关论文
共 40 条
  • [1] [Anonymous], 2004, GBT 50344 2004
  • [2] [Anonymous], E202011 JTG
  • [3] [Anonymous], 2005, JTG E42-2005
  • [4] Optimization of gear blank preforms based on a new R-GPLVM model utilizing GA-ELM
    Cao, Zhiyong
    Xia, Juchen
    Zhang, Mao
    Jin, Junsong
    Deng, Lei
    Wang, Xinyun
    Qu, June
    [J]. KNOWLEDGE-BASED SYSTEMS, 2015, 83 : 66 - 80
  • [5] Review of ice-pavement adhesion study and development of hydrophobic surface in pavement deicing
    Chen, Huaxin
    Wu, Yongchang
    Xia, Huiyun
    Jing, Bingyin
    Zhang, Qingjiang
    [J]. JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING-ENGLISH EDITION, 2018, 5 (03) : 224 - 238
  • [6] Chen Q., 2018, ROAD MACH CONSTR MEC, V35, P56
  • [7] [陈谦 Chen Qian], 2019, [材料导报, Materials Review], V33, P1659
  • [8] Performance evaluation of tourmaline modified asphalt mixture based on grey target decision method
    Chen, Qian
    Wang, Chaohui
    Wen, Penghui
    Sun, Xiaolong
    Guo, Tengteng
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2019, 205 : 137 - 147
  • [9] Durability evaluation of road cooling coating
    Chen, Qian
    Wang, Chaohui
    Fu, Hao
    Zhang, Lian
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2018, 190 : 13 - 23
  • [10] Comprehensive performance evaluation of low-carbon modified asphalt based on efficacy coefficient method
    Chen, Qian
    Wang, Chaohui
    Wen, Penghui
    Wang, Menghao
    Zhao, Jianxiong
    [J]. JOURNAL OF CLEANER PRODUCTION, 2018, 203 : 633 - 644