文献类型：期刊文献

英文题名：LR-BCA: Label Ranking for Bridge Condition Assessment

作者：Wang, Kai[1];Ruan, Tong[1];Xie, Faxiang[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 20037, Peoples R China;[2]Hohai Univ, Coll Civil & Transportat Engn, Nanjing 210098, Peoples R China

年份：2021

卷号：9

起止页码：4038

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20211210121134);WOS:【SCI-EXPANDED(收录号:WOS:000607658700001)】；

基金：This work was supported by the National Natural Science Foundation of China for Open Link Data (LOD) Quality of Use Evaluation Theory and Method Research under Grant 61772201.

语种：英文

外文关键词：Bridge condition assessment; data cleaning; machine learning; label ranking

摘要：Bridge condition assessment (BCA) plays an important role in modern bridge management. Existing assessment methods are time-consuming, labor-intensive and error-prone. The use of machine learning for BCA can effectively solve the above problems. However, the large amount of label noise in the dataset severely affected the performance of the BCA model. In this paper, we present an effective label ranking approach for BCA (LR-BCA). Our proposed LR-BCA method considers the natural order relationship between bridge condition ratings. Moreover, a heuristic data cleaning (HDC) approach is proposed for cleaning bridge condition dataset. The HDC method firstly identifies all the label conflict examples, then iteratively filters out the noise. Experimental results on real-world dataset confirm the effectiveness of the HDC method and demonstrate that our proposed LR-BCA method achieves 99% Top-2 accuracy, which is highly competitive compared to baseline methods.