东南大学学报（英文版）2025，Vol.41Issue(4)：412-421,10.DOI:10.3969/j.issn.1003-7985.2025.04.002
基于深度学习的钢筋混凝土梁内部裂缝阵列超声全聚焦成像方法
Deep learning-based method for array ultrasonic total focus imaging of internal cracks in RC beams
摘要
Abstract
Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these challenges,an efficient total focus imaging method based on deep learning is proposed.This method deals with array ultrasonic time-domain signals from cracked RC beams.A deep neural network(DNN)employing a feature extraction+multilevel feature fusion+matrix construction architec-ture was developed;this architecture enabled the DNN to learn the underlying physical principles of the TFM.The ar-chitecture effectively transformed ultrasonic time-domain signals into a B-scan matrix.Training,validation,and test-ing data were collected by measuring eight RC beams with preset artificial cracks using a low-frequency shear wave ar-ray ultrasonic system.The results demonstrated that the re-constructed B-scan matrices had a peak signal-to-noise ratio of 26.94 dB and a structural similarity index of 0.978.Fur-thermore,the proposed method required 42%fewer floating-point operations compared with physics-based cal-culations,achieving total focus imaging with lower compu-tational cost.The study facilitates the advancement of ultra-sonic total focus imaging of RC structures from physics-based methods to data-driven methods without re-quiring prior physical knowledge,thereby providing robust support for further nondestructive evaluation and quantita-tive analysis.关键词
全聚焦方法/阵列超声成像/钢筋混凝土梁/时域信号/深度学习Key words
total focus method/array ultrasound imaging/reinforced concrete beam/time-domain signal/deep learn-ing分类
建筑与水利引用本文复制引用
舒江鹏,李斯涵,杨涵,许逸飞..基于深度学习的钢筋混凝土梁内部裂缝阵列超声全聚焦成像方法[J].东南大学学报（英文版）,2025,41(4):412-421,10.基金项目
Science&Technology Specific Project of Jiangsu Province(No.BZ2024047),Key R&D Program of Ningbo(No.2024H013),the National Natural Science Foundation of China(No.W2412092). （No.BZ2024047）
