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Effectiveness of infrared thermography for delamination detection in reinforced concrete bridge decks
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.
Highlights The performance of the model for delamination detection was benchmarked with validated ground truth. Although the model's accuracy is 69%, it is not the most suitable metric especially when the result is biased towards TPR or TNR. Infrared thermography data were validated by conventional and reliable field techniques.
Effectiveness of infrared thermography for delamination detection in reinforced concrete bridge decks
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.
Highlights The performance of the model for delamination detection was benchmarked with validated ground truth. Although the model's accuracy is 69%, it is not the most suitable metric especially when the result is biased towards TPR or TNR. Infrared thermography data were validated by conventional and reliable field techniques.
Effectiveness of infrared thermography for delamination detection in reinforced concrete bridge decks
Ichi, Eberechi (author) / Dorafshan, Sattar (author)
2022-08-01
Article (Journal)
Electronic Resource
English
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