Clustering-Based Threshold Model for Condition Assessment of Concrete Bridge Decks Using Infrared Thermography

Abstract

Bridge decks are deteriorating at an alarming rate due to corrosion of the reinforcing steel, requiring billions of dollars to repair and replace them. Nowadays, infrared thermography (IRT) diagnostics represent a mature high-technology field that combines achievements in the understanding of heat conduction, material science, and computer data processing. The high interest in the IRT inspection technique is related to its universal character, high testing productivity and in-service safety. However, the analysis of IRT data to evaluate the condition of concrete bridge decks is still rather qualitative, thus preventing the progressive competition of IRT with other inspection techniques. The goal of this research is to understand the relationship between IRT and deck deterioration, and develop a model to determine delamination quantities in concrete bridge decks using a relatively low cost microbolometer infrared camera. Infrared testing was conducted in-situ on a full-scale bridge deck. The thermal IR images were enhanced and stitched using specially developed Matlab codes to create a mosaicked thermogram of the entire bridge deck. Image analysis based on the K-means clustering technique was utilized to segment the mosaic and identify objective thresholds. Hence, a condition map classifying different categories of delamination severity was created and also validated through the results of other techniques obtained on the same bridge. The findings from this study demonstrate that IRT can provide transportation agencies both quantitative and qualitative indications of subsurface delamination defects, thus assisting efficient maintenance and repair decision-making, and focusing limited funding on the most deserving bridge decks.