Mukhti Julfikhsan Ahmad, Robles Kevin Paolo V, Lee Keon-Ho, Kee Seong-Hoon
Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49304, Republic of Korea.
Department of Architectural Engineering, Dong-A University, Busan 49304, Republic of Korea.
Materials (Basel). 2023 May 1;16(9):3502. doi: 10.3390/ma16093502.
The objective of this study is to explore the feasibility of using ultrasonic pulse wave measurements as an early detection method for corrosion-induced concrete damages. A series of experiments are conducted using concrete cube specimens, at a size of 200 mm, with a reinforcing steel bar (rebar) embedded in the center. The main variables include the water-to-cement ratio of the concrete (0.4, 0.5, and 0.6), the diameter of the rebar (10 mm, 13 mm, 19 mm, and 22 mm), and the corrosion level (ranging from 0% to 20% depending on rebar diameter). The impressed current technique is used to accelerate corrosion of rebars in concrete immersed in a 3% NaCl solution. Ultrasonic pulse waves are collected from the concrete specimens using a pair of 50 kHz P-wave transducers in the through-transmission configuration before and after the accelerated corrosion test. Deep learning techniques, specifically three recurrent neural network (RNN) models (long short-term memory, gated recurrent unit, and bidirectional long short-term memory), are utilized to develop a classification model for early detection of concrete damage due to rebar corrosion. The performance of the RNN models is compared to conventional ultrasonic testing parameters, namely ultrasonic pulse velocity and signal consistency. The results demonstrate that the RNN method outperforms the other two methods. Among the RNN methods, the bidirectional long short-term memory RNN model had the best performance, achieving an accuracy of 74% and a Cohen's kappa coefficient of 0.48. This study establishes the potentiality of utilizing deep learning of ultrasonic pulse waves with RNN models for early detection of concrete damage associated with steel corrosion.
本研究的目的是探讨使用超声脉冲波测量作为腐蚀引起的混凝土损伤早期检测方法的可行性。使用尺寸为200mm的混凝土立方体试件进行了一系列实验，试件中心埋有一根钢筋。主要变量包括混凝土的水灰比（0.4、0.5和0.6）、钢筋直径（10mm、13mm、19mm和22mm）以及腐蚀程度（根据钢筋直径从0%到20%不等）。采用外加电流技术加速浸泡在3%氯化钠溶液中的混凝土中钢筋的腐蚀。在加速腐蚀试验前后，使用一对50kHz的纵波换能器以穿透传输配置从混凝土试件中采集超声脉冲波。利用深度学习技术，特别是三种循环神经网络（RNN）模型（长短期记忆、门控循环单元和双向长短期记忆），开发了一种用于早期检测钢筋腐蚀引起的混凝土损伤的分类模型。将RNN模型的性能与传统超声检测参数，即超声脉冲速度和信号一致性进行了比较。结果表明，RNN方法优于其他两种方法。在RNN方法中，双向长短期记忆RNN模型性能最佳，准确率达到74%，科恩卡帕系数为0.48。本研究确立了利用RNN模型对超声脉冲波进行深度学习以早期检测与钢筋腐蚀相关的混凝土损伤的潜力。
