Improving the conversion accuracy between bridge element conditions and NBI ratings using deep convolutional neural networks

Graziano Fiorillo, Hani Nassif

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Interest in the analysis of bridge element conditions in the U.S. has increased lately. Since 2014, the Federal Highway Administration is publishing bridge element data to better predict the performance of bridges for improving the allocation of management resources. However, because bridge elements data are still limited, bridge engineers often rely on National Bridge Inventory (NBI) condition ratings to predict the performance of bridges, which have been assembled since the 1970s. Therefore, it is valuable to investigate the correlation that exists between NBI ratings and element conditions to improve our knowledge of the latter. The objective of this article is to perform the analysis of both bridge element condition data and NBI ratings to back-map NBI deterioration curves into element deterioration profiles using deep convolutional neural networks. The proposed approach better estimates NBI ratings from bridge element conditions by at least 24.8% when compared to other techniques. By using an error tolerance of ±1 on the NBI ratings, the proposed procedure can accurately predict more than 90.0% of the ratings, while element deterioration rates have a 60% probability of being predicted within the range of the empirical rates.

Original languageEnglish (US)
Pages (from-to)1669-1682
Number of pages14
JournalStructure and Infrastructure Engineering
Volume16
Issue number12
DOIs
StatePublished - Dec 1 2020

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Safety, Risk, Reliability and Quality
  • Geotechnical Engineering and Engineering Geology
  • Ocean Engineering
  • Mechanical Engineering

Keywords

  • Artificial neural network
  • National Bridge Inventory
  • bridge asset management
  • bridge element deterioration
  • convolutional deep learning
  • machine learning

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