Document Type

Article

Publication Date

10-1-2022

Publication Title

Automation in Construction

Volume

142

Abstract

This paper presents findings of delamination detection using infrared thermography (IRT) in five in-service bridges using an unmanned aerial vehicle system. The authors have used semantically segmented IRT images to evaluate IRT’s effectiveness in detection of deck delamination for the first time. Using an adaptive image processing-based model, sub-surface delaminations were detected by optimizing all user-defined parameters in the model, including threshold values to convert the enhanced IRT images to a binary image. The optimization process has been done selecting iterating the user-defined parameters and their effect on the interaction of a set of sigmoid curves representing the model’s performance metrics. The 2- clustered (Park river median and Park river south bound bridges) and 3-clustered (Park river north-bound, Forest river north-bound, Forest river south-bound) threshold values ranged from 0.365 to 0.380 and 0.459 to 0.486, respectively, and yielded to an average accuracy of 69% for delamination detection. The effect of different parameters on the value of the performance metrics were investigated and analyzed including the ambient wind speed and depth of delamination during data collection. The optimized delamination detection model was shown to be superior to a delamination detection using the conventional unsupervised K-nearest neighbor clustering technique.

First Page

104523

DOI

10.1016/j.autcon.2022.104523

ISSN

0926-5805

Comments

The Version of Record for this accepted manuscript has been published at:

Ichi, Eberechi, and Sattar Dorafshan. "Effectiveness of Infrared Thermography for Delamination Detection in Reinforced Concrete Bridge Decks." Automation in Construction, vol. 142, 2022, article 104523, https://doi.org/10.1016/j.autcon.2022.104523.

Download

Share

COinS