A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
LR-BCA: Label Ranking for Bridge Condition Assessment
2020
IEEE Access
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
doi:10.1109/access.2020.3048419
fatcat:fwuvehjyynbenntq3fauarw43q