Share Email Print

Proceedings Paper

Automated construction of bridge condition inventory using natural language processing and historical inspection reports
Author(s): Tianshu Li; Devin Harris
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The aging bridge infrastructure network is in critical need of maintenance, rehabilitation or replacement (MR and R) as nearly half of this inventory is approaching the end their design service lives. Agencies responsible for managing this network have limited resources that are insufficient for the scale of the problem, highlighting the need for smart, system-level decision-making strategies that can be integrated with current practice. A large amount of rich information on element level condition descriptions are buried in bridge inspection reports, but this local information is seldom used holistically to infer system performance. Current decision-making strategies are constrained by limitations in bridge deterioration prediction models, which lack comprehensive and well-structured databases needed for automation of processes associated with high resolution forecasting. How to draw meaningful information from the details of these localized reports to assist system-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 raw textual data in bridge inspection reports. This raw data provides a source for capturing the experience-driven metric inherent to the bridge inspection process. The proposed framework constructs an innovative bi-directional Long-short Term Memory neural network that automatically reads inspection reports into different condition categories and achieves 96.2% accuracy when examined on inspection reports collected by Virginia Department of Transportation. The extracted information forms a well-structured bridge condition inventory that contains rich historical and local condition information, and hence enables smart, system-level bridge MR and R decision-making.

Paper Details

Date Published: 1 April 2019
PDF: 8 pages
Proc. SPIE 10971, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII, 109710T (1 April 2019); doi: 10.1117/12.2514006
Show Author Affiliations
Tianshu Li, Univ. of Virginia (United States)
Devin Harris, Univ. of Virginia (United States)

Published in SPIE Proceedings Vol. 10971:
Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII
Andrew L. Gyekenyesi, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?