Citation

Kong X, Li Z, Wallis JR, Zhang Y. Data Sci. Transp. 2023; 5(1): e1.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s42421-023-00064-z

PMID

unavailable

Abstract

Good deck condition is critical for a bridge to transport vehicles across physical obstacles on the landscape. This study uses the National Bridge Inventory data provided by LTBP InfoBridge to assess the influence of weather, structure, and traffic factors on deck conditions of concrete and steel bridges in the ten states with the highest percentage of bridges with poor deck conditions. For this analysis, an interpretable machine learning framework takes advantage of the high prediction accuracy while maintaining interpretability. A model is built for each structure type producing results that reveal the impacts of the factors on deck conditions for both types of bridges. The effects of various factors on the deck conditions of steel and concrete bridges are analyzed by comparing the similarities and differences among these factors. The results show that the concrete cast-in-place is the only deck structure associated with good deck conditions for steel bridges, while multiple other structure types associate with good deck conditions for concrete bridges. For the wearing surface, the bituminous wearing surface is highly associated with poor deck conditions of steel bridges. The number of snow days is negatively associated with the deck condition of steel bridges, while the concrete bridge could maintain a good performance with extremely long snow days. The findings also confirmed that increasing the truck percentage could deteriorate the deck conditions faster.

Language: en

Keywords

Bridge; Concrete; Deck condition; Interpretable machine learning; SHAP; Steel