The Application of Two-Dimensional Continuous Wavelet Transform Based on Active Infrared Thermography for Subsurface Defect Detection in Concrete Structures

Abstract

The early condition-based assessment of civil infrastructures plays an essential role in extending their service life, preventing undesirable sudden failures, and reducing maintenance and rehabilitation costs. One of the most commonly used and fastest nondestructive testing (NDT) techniques is infrared thermography (IRT), which has emerged as a powerful method for assessing general concrete quality and detecting subsurface damage in structural members. Nevertheless, the accurate detection and classification of localized defects is still a challenging task to achieve. The contribution made by enhancing defect detection using two-dimensional (2D) wavelet transformation (WT) as a post-processing method, however, has received little attention within the field of active IR thermography. In this study, we explored the use of continuous wavelet transform (CWT) to visualize how the wavelet function at different frequencies could enhance the damage features of thermal images. A concrete slab under an applied heat flux was tested experimentally by an IR camera with well-controlled excitation sources. The qualitative visualization of thermograms was translated into quantitative results by extracting, processing, and post-processing the values assigned to the pixels in the thermal images. With the assumption of there being no oriented damage features, an isotropic (non-directional) Mexican hat wavelet was utilized as the mother wavelet. The experimental results showed that the 2D-CWT method achieved strong detection performance in extracting discriminatory features (defective areas) from the acquired thermal images. Compared with raw thermograms, the resultant CWT-transformed images were less affected by the non-uniform heating effect, and the boundaries of the defects contrasted more strongly. The 2D-CWT method demonstrates good sensitivity when an appropriate wavelet type and scale factor are chosen. Due to the desire to detect localized defects, adjusting the scale factor of the wavelet is important to improve the efficiency of detection as lower scale factors provide the finer details of thermal images, whereas higher scale factors provide the general outline of internal defects. The findings of this study represent a further step toward improving thermographic data for more precise defect-detection imaging, and principally for large concrete structures, that can be verified easily using other NDT surveys.

Quantification of subsurface defects in reinforced concrete of bridges by unsupervised segmentation of IR images

“…The progress of the rebar corrosion causes accumulation of iron oxide at the interface between the rebar and concrete which has higher volume than the steel which subsequently weakens their adhesion and can cause cracking of the concrete [3]. Therefore, it is required to monitor the condition of structure to recognise the onset of corrosion propagation and detect damages at earliest possible stage, preferably by non-destructive techniques and before they progress severely, incur costly rehabilitation and maintenance, endanger lives and cause traffic disruption [1,4,5].…”

“…Despite the advantages, there are still difficulties and barriers for practical implementation of IRT for concrete bridges. The challenges facing implementation of IRT are related to favourable data collection time, selection of proper mechanism and waveform for thermal excitation, geometry of the sub-surface defect, varying thermal properties of concrete as a low diffusivity construction material and objective interpretation of the IR results [5,7].…”