Khurshid Hifsa, Mohammed Bashar S, Bheel Naraindas, Cahyadi Willy Anugrah, Mukhtar Husneni
Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Tronoh, 32610, Perak, Malaysia.
Dept. of Electrical Engineering, School of Electrical Engineering, Telkom University, Telkom University Landmark Building, 19th floor, Terusan Buah Batu, Bandung, Jalan Telekomunikasi, 40257, Indonesia.
Heliyon. 2024 Aug 3;10(15):e35772. doi: 10.1016/j.heliyon.2024.e35772. eCollection 2024 Aug 15.
Currently, the field of structural health monitoring (SHM) is focused on investigating non-destructive evaluation techniques for the identification of damages in concrete structures. Magnetic sensing has particularly gained attention among the innovative non-destructive evaluation techniques. Recently, the embedded magnetic shape memory alloy (MSMA) wire has been introduced for the evaluation of cracks in concrete components through magnetic sensing techniques while providing reinforcement as well. However, the available research in this regard is very scarce. This study has focused on the analyses of parameters affecting the magnetic sensing capability of embedded MSMA wire for crack detection in concrete beams. The response surface methodology (RSM) and artificial neural network (ANN) models have been used to analyse the magnetic sensing parameters for the first time. The models were trained using the experimental data obtained through literature. The models aimed to predict the alteration in magnetic flux created by a concrete beam that has a 1 mm wide embedded MSMA wire after experiencing a fracture or crack. The results showed that the change in magnetic flux was affected by the position of the wire and the position of the crack with respect to the position of the magnet in the concrete beam. RSM optimisation results showed that maximum change in magnetic flux was obtained when the wire was placed at a depth of 17.5 mm from the top surface of the concrete beam, and a crack was present at an axial distance of 8.50 mm from the permanent magnet. The change in magnetic flux was 9.50 % considering the aforementioned parameters. However, the ANN prediction results showed that the optimal wire and crack position were 10 mm and 1.1 mm, respectively. The results suggested that a larger beam requires a larger diameter of MSMA wire or multiple sensors and magnets for crack detection in concrete beams.
目前，结构健康监测（SHM）领域专注于研究用于识别混凝土结构损伤的无损评估技术。在创新的无损评估技术中，磁传感尤其受到关注。最近，嵌入式磁形状记忆合金（MSMA）线被引入，用于通过磁传感技术评估混凝土构件中的裂缝，同时还能提供加固作用。然而，这方面现有的研究非常稀少。本研究聚焦于分析影响嵌入式MSMA线磁传感能力的参数，以用于混凝土梁裂缝检测。首次使用响应面法（RSM）和人工神经网络（ANN）模型来分析磁传感参数。这些模型使用通过文献获得的实验数据进行训练。这些模型旨在预测在一根嵌入1毫米宽MSMA线的混凝土梁发生断裂或裂缝后产生的磁通量变化。结果表明，磁通量的变化受线的位置以及裂缝相对于混凝土梁中磁铁位置的影响。RSM优化结果表明，当线放置在距混凝土梁顶面17.5毫米深处，且在距永久磁铁轴向距离8.50毫米处存在裂缝时，可获得最大磁通量变化。考虑上述参数时，磁通量变化为9.50%。然而，ANN预测结果表明，最佳的线和裂缝位置分别为10毫米和1.1毫米。结果表明，对于更大的梁，需要更大直径的MSMA线或多个传感器和磁铁来进行混凝土梁裂缝检测。
