A data-driven approach for regional bridge condition assessment using inspection reports

Bridges play an important role in the highway transportation network. There is an increasing concern that highway bridges have been suffering from structural degradation and deficiency due to severe environment, overloading, initial structural defects, and other factors. Ensuring the safety of a large number of regional bridges at the network level becomes a major challenge. This study proposes an entire data-driven condition assessment framework for network-level bridges, including data integration, condition assessment, and maintenance management. The periodic bridge inspection reports in China could provide quite a few condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The proposed framework is applied to a real highway bridge network located in Hebei province, China. Inspection data are obtained from thousands of bridges over quite a few years. The established regional deterioration model could effectively predict the bridge future condition based on the extracted hidden features. The regional maintenance strategies are optimized to satisfy the economic and performance constraints, which allows bridge managers to select the most appropriate one for implementation. It is shown that the proposed data-driven approach can provide a guideline to bridge managers to estimate the future condition and allocate the maintenance resources.

Automated summary

This study proposes a comprehensive data-driven framework for network-level bridge condition assessment. By analyzing periodic bridge inspection reports in China, the future condition of bridges can be predicted, and maintenance strategies can be optimized to meet economic and performance constraints.