Guided wave-based cross-scene interfacial debonding detection in reinforced concrete structures

Abstract

The cross-scene problem within the field of guided wave-based interfacial debonding detection in steel-reinforced concrete structures is a significant challenge. This issue arises when the methods initially designed for laboratory-configured structures or numerical models are adapted to real-world scenarios. In response to this challenge, driven by the concept of domain adaptation, a novel method—the discriminative wavelet adversarial adaptation network (DWAAN)—is proposed to assess the health of analogous structures using diagnostic insights from a reference structure. The proposed method utilizes continuous wavelet transform to segregate signal components. A feature extraction network, designed with the convolutional neural network architecture, extracts domain confusion and damage-sensitive features through guided adversarial adaptive training in the wavelet domain. Both the adversarial and statistical alignment are employed during training for comprehensive optimization of data distribution discrepancies. The effectiveness of the proposed method is assessed in both the homogeneous and heterogeneous cross-scene interfacial debonding detection scenarios, revealing an average performance enhancement of 32.5% in distinct homogeneous applications and 26.6% in heterogeneous scenarios, when compared to the traditional supervised learning approach. In addition, the present method demonstrates robust convergence stability, evidenced by the minimized training fluctuations. The proposed method holds promising potential for practical applications in interfacial debonding detection in reinforced concrete structures while addressing the cross-scene challenge.