Defects Detection and Identification in Adhesively Bonded Joints between CFRP Laminate and Reinforced Concrete Beam Using Acousto-Ultrasonic Technique

Abstract

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.