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.
Recently, basalt-carbon hybrid composite structures have attracted increasing attention due to their better damage tolerance, if compared with carbon fiber-reinforced polymer composites (CFRP). Low-velocity is considered as one of the most severe threats to composite materials as it is usually invisible and it occurs frequently in service. With this regard, nondestructive testing (NDT) techniques, especially emerging modalities, are expected to be an effective damage detection method. Eddy current-pulsed thermography (ECPT), as an emerging NDT technique, was used to evaluate the damage induced by low-velocity impact loading in a CFRP laminate, as well as in two different-structured basalt-carbon hybrid composite laminates. In addition, ultrasonic C-scan and x-ray computed tomography were performed to validate the thermographic results. Pulsed phase thermography, principal component thermography, and partial least squares thermography were used to process the thermal data and to retrieve the damage imagery. Then, a further analysis was performed on the imagery and temperature profile. As a result, it is concluded that ECPT is an effective technique for hybrid composite evaluation. The impact energy tends to create an interlaminar damage in a sandwich-like structure, while it tends to create an intralaminar damage in an intercalated stacking structure.
In this paper, eddy current pulsed thermography in transmission mode was used to detect the damages caused by low-velocity impacts in carbon fiber-reinforced polymer and basalt-carbon hybrid fiber-reinforced polymer laminates. In particular, different hybrid structures including intercalated stacking and sandwich-like structures were used. The impact energy of 12.5 was used for the evaluation of the impact damage level. Ultrasonic phased-array C-scan was performed for comparative purposes. In addition, the advantages and disadvantages of the two structures were identified and discussed.
The three dimensional (3D) X-ray computed tomography (3D-CT) has proven its successful application as an inspection method in nondestructive testing. The generated 3D volume uses high efficiency reconstruction algorithms containing all required information on the inner structures of the inspected part. Segmentation of this volume reveals suspicious regions that need to be classified as defective or false alarms. This paper deals with the classification step using data fusion theory, which was successfully applied on 2D X-ray data in previous work along with a support vector machine (SVM). For this study we chose a 3D-CT dataset of aluminium castings that needs to be fully inspected via X-ray CT to ensure their quality. We achieved a true classification rate of 97% on a validation dataset, which proves the effectiveness of the data fusion theory as a method to build a better classifier. Comparison with SVMs shows the importance of selecting the most pertinent features to improve the classifier performance and attaining 98% of true classification rate.
The three dimensional X-ray computed tomography (3D-CT) has proved its successful usage as
inspection method in non destructive testing. The generated 3D volume using high efficiency
reconstruction algorithms contains all the inner structures of the inspected part. Segmentation of this
volume reveals suspicious regions which need to be classified into defects or false alarms. This paper
deals with the classification step using data fusion theory and support vector machine. Results
achieved are very promising and prove the effectiveness of the data fusion theory as a method to build
stronger classifier.
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