Phased array ultrasonic and deep learning based internal defect detection in underwater concrete bridge structures

Abstract

Underwater concrete structures, which are critical load-bearing components in water-crossing bridges, are often prone to internal defects that undermine their long-term integrity. Traditional inspection methods, which mainly focus on surface defects, fall short in detecting internal structural issues, creating a significant gap in maintenance and safety assessments. This study proposes a phased array ultrasonic testing (PAUT) and deep learning based internal defect detection method in underwater concrete bridges. First, a new underwater PAUT device is developed, incorporating non-local means filtering to reduce noise and enhance ultrasonic imaging quality. Second, a small-scale dataset of ultrasonic images depicting internal defects in underwater concrete is created, and StyleGAN2-ADA was employed for effective data augmentation, addressing the challenge of limited data availability. To further improve defect detection, an enhanced SAM-based method is introduced, utilizing both serial and parallel depth-wise adapter fine-tuning strategies, which significantly boost the model's adaptability to complex underwater ultrasonic imaging tasks. Additionally, a pyramid-module-based self-generating prompt encoder is developed to optimize feature extraction and reduce manual intervention. Experimental results on both laboratory specimens and real-world underwater bridge piers demonstrate the superior performance of this method. The detection model achieves an Intersection over Union (IoU) score of 0.772—an improvement of 4.49 % over the baseline SAM model—and outperforms other approaches in precision, recall, and F1-score. This research presents a robust, efficient, and scalable solution for internal defect detection in underwater concrete bridge structures, with strong potential for real-world applications in structural health monitoring.