LR-BCA: Label Ranking for Bridge Condition Assessment

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.