The identification and categorization of subsurface damages in thermal images of concrete structures remain an ongoing challenge that demands expert knowledge. Consequently, creating a substantial number of annotated samples for training deep neural networks poses a significant issue. Artificial intelligence (AI) models particularly encounter the problem of false positives arising from thermal patterns on concrete surfaces that do not correspond to subsurface damages. Such false detections would be easily identifiable in visible images, underscoring the advantage of possessing additional information about the sample surface through visible imaging. In light of these challenges, this study proposes an approach that employs a few-shot learning method known as the Siamese Neural Network (SNN), to frame the problem of subsurface delamination detection in concrete structures as a multi-modal similarity region comparison problem. The proposed procedure is evaluated using a dataset comprising 500 registered pairs of infrared and visible images captured in various infrastructure scenarios. Our findings indicate that leveraging prior knowledge regarding the similarity between visible and thermal data can significantly reduce the rate of false positive detection by AI models in thermal images.
Subsurface delamination is one of the main damage mechanisms that affect the integrity of structural components. Its detection effectively and reliably is a crucial step for in-service assurance and avoiding further accidents. In that scenario, it is necessary to explore alternatives to current inspection practices so that effective non-destructive methods can be implemented in the field. This paper investigates the application of infrared thermography and ground penetrating radar to detect and evaluate subsurface delamination in concrete components. To this aim, laboratory specimens made of reinforced concrete with Teflon inserts to simulate internal delamination are inspected with the step-heating approach. The IR data collected during the heating and cooling process is then evaluated and processed with pulsed-phase thermography and principal component regression. The extension or severity of the delamination is then evaluated with ground penetrating radar. The results will be evaluated to determine the applicability of the methods at larger scales.
KEYWORDS: Inspection, Thermography, Image processing, Image registration, Signal to noise ratio, Signal processing, Data acquisition, Infrared radiation, Civil engineering
This study developed an end-to-end procedure to overcome common issues faced during the analysis of passive infrared thermography (IRT) sequences from outdoor concrete infrastructures. The processing pipeline includes the automatic pre-processing of raw thermograms, data cleaning and organization, image adjustment, and sequential image registration. One image registration method was implemented, and the results were evaluated using the Euclidean distance metric. Furthermore, the resulting sequences were processed using signal processing techniques to increase the detectability of the defects. The results from outdoor IRT surveys over two academic samples are presented, where one image per minute was taken for 24 hours on slabs and columns representative structures. By addressing the difficulties encountered during the analysis of passive IRT sequences, our contribution can broaden the spectrum of the application of IRT for the condition assessment of concrete infrastructure.
KEYWORDS: Signal to noise ratio, Inspection, Thermography, Signal processing, Data acquisition, Sun, Solar energy, Solar radiation, Interference (communication), Nondestructive evaluation
Infrared Thermography (IRT) is a Nondestructive Testing (NDT) method that can complement the concrete infrastructure condition assessment in a fast and contactless manner. When applied to large structures in outdoor areas, the heat source is usually the Sun, which is dynamic and varies through the days, months, and year. Solar irradiation is vulnerable to changes in environmental conditions, which affects the upcoming IRT measurements. Besides that, vertical elements have multiple locations and orientations, where the solar exposure varies according to the solar cycle. Consequently, column faces can experience reduced energy flow, where low or inexistent thermal contrast restrains the detection of existing subsurface damages. In this study, three signal processing techniques, named Principal Component Thermography (PCT), Pulsed Phase Thermography (PPT), and Partial Least Square Thermography (PLST), were applied to thermograms sequences acquired from a concrete element under varying solar exposure. One reinforced concrete column was constructed with ten simulated subsurface defects positioned in the Northeast, Southeast, Northwest, and Southwest faces. This column was inspected hourly through different days of summer and winter periods. It was demonstrated the difference between the signature contrast registered in thermograms acquired from faces exposed to small and large periods of solar irradiation. The preliminary results of using thermographic signal processing techniques verified the possibility of increasing the signal-to-noise ratio and thermal contrast in elements under unfavorable solar exposure. In addition, the research explored the use of different image sequence intervals on the performance of the signal processing techniques.
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