Automated Construction of Bridge Condition Inventory Using Natural Language Processing and Historical Inspection Reports

摘要

The aging bridge infrastructure network is in critical need of maintenance, rehabilitation or replacement (MR&R) as nearlyhalf of this inventory is approaching the end their design service lives. Agencies responsible for managing this networkhave limited resources that are insufficient for the scale of the problem, highlighting the need for smart, system-leveldecision-making strategies that can be integrated with current practice. A large amount of rich information on elementlevelcondition descriptions are buried in bridge inspection reports, but this local information is seldom used holisticallyto infer system performance. Current decision-making strategies are constrained by limitations in bridge deteriorationprediction models, which lack comprehensive and well-structured databases needed for automation of processes associatedwith high resolution forecasting. How to draw meaningful information from the details of these localized reports to assistsystem-level bridge condition comparison and maintenance prioritization still remains unclear and warrants further study.To bridge this gap, this paper proposes a Natural Language Processing framework to extract information from the rawtextual data in bridge inspection reports. This raw data provides a source for capturing the experience-driven metricinherent to the bridge inspection process. The proposed framework constructs an innovative bi-directional Long-shortTerm Memory neural network that automatically reads inspection reports into different condition categories and achieves96.2% accuracy when examined on inspection reports collected by Virginia Department of Transportation. The extractedinformation forms a well-structured bridge condition inventory that contains rich historical and local condition information,and hence enables smart, system-level bridge MR&R decision-making.