Infrared Thermography for Concrete Defect Detection: A Comprehensive Experimental and Numerical Analysis

Abstract

Infrared Thermography (IRT) has been widely utilized for detecting subsurface defects in concrete structures. However, there is a lack of quantitative assessment regarding the detectability of IRT under varying defect characteristics, such as thickness, depth, and size. This study aims to address this gap through a combination of experimental and numerical investigations. We constructed specimens with defects of different depths, sizes, and thicknesses to analyze the temperature differences between defect and sound areas. A finite element model was developed and validated by comparing it with experimental results. Using this model, we quantified the influence of defect properties and model parameters on thermal contrast, while also explored the heat transfer processes. In concrete structures, variations in material properties and surface irregularities significantly affect surface temperature distribution and, consequently, detectability. To examine these influences, we incorporated a Gaussian random field to introduce model noise. A thresholding algorithm was then employed to quantitatively evaluate detectability. The findings offer valuable insights into the effectiveness of IRT for concrete damage detection across various defect depths and thicknesses, thereby enhancing its practical application in structural health monitoring.

Keywords: Concrete delamination, Thermography, Numerical simulation, Defect segmentation, Structural health monitoring