Ultrasonic array tomography-oriented subsurface crack recognition and cross-section image reconstruction of reinforced concrete structure using deep neural networks

Abstract

Manual interpretation from technicians with prior expertise knowledge is still needed for ultrasonic array tomography when detecting subsurface damages in reinforced concrete (RC) structures. Ultrasonic scans were usually utilized in qualitative manners that have not been leveraged for quantification purposes in prior studies and practical engineering. This study presented an ultrasonic array tomography-oriented subsurface crack recognition and pixel-wise cross-section image reconstruction method for RC structures based on deep neural networks (DNN). B-scans were acquired by testing entity RC components with preset artificial cracks using shear-wave low-frequency ultrasonic array. A DNN with the basic encoder-decoder architecture was developed, which was improved by introducing skip connections and residual modules to adapt to semantic structure of ultrasonic B-scans. A supervised generative adversarial DNN was proposed to generate fake but convincing B-scans for data augmentation. Results reported that F-scores were higher than 78 %, and satisfactory agreements were observed between ground truth annotations and reconstructed cross-section images, substantiating the effectiveness of the proposed method. Furthermore, with the physical information-assisted registration, individual local reconstructed cross-section images were combined to global representations indicating crack distribution in the entire component to facilitate convincing structural assessments.