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Adhesively bonded composite reinforcements have been increasingly used in civil engineering since the 1980s. They depend on the effective transfer of forces throughout the adhesive joint that may be affected by defects or damages. It is therefore necessary to provide methods to detect and/or identify these defects present in the bonded joints without affecting their future use. This should be carried out through nondestructive methods (NDT) and should be able to discriminate the different types of defects that may be encountered. The acousto-ultrasonic technique shows good potential to answer to this challenge, as illustrated in recent studies led on small-scale model samples. In this paper, we assess the robustness of this methodology on larger scale samples using reinforced concrete beams (RC beam), that is a mandatory step prior to on-site applications. A mono-parametric analysis allows the detection of all types of defects using a simple criterion set. For the identification, it was necessary to conduct a data-driven strategy by means of a Principal Component Analysis (PCA) and a random forest (RF) method used from extracted parameters.

This research focuses on the application of an acousto-ultrasonics (AU) technique, a combination of ultrasonic characterization and acoustic emission, to nondestructively detect defects such as voids in bonded metal/composite assemblies. Computational methods are established to examine the effects of voids on the collected signal. The position of the receiver sensor with respect to the defect is also investigated. Given a specific structure and type of actuation signal, the sensor location and probability of detection can be enhanced by the model developed in this work. The defect detection is optimal provided the receiver sensor is located around the epicenter of the defect. Moreover, this work highlights the importance of the choice of reception sensor.

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