Candelaria Ma Doreen Esplana, Chua Nhoja Marie Miranda, Kee Seong-Hoon
Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Korea.
Institute of Civil Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines.
Materials (Basel). 2022 Nov 9;15(22):7914. doi: 10.3390/ma15227914.
This study investigated the applicability of using ultrasonic wave signals in detecting early fire damage in concrete. This study analyzed the reliability of using the linear (wave velocity) and nonlinear (coherence) parameters from ultrasonic pulse measurements and the applicability of machine learning in assessing the thermal damage of concrete cylinders. While machine learning has been used in some damage detections for concrete, its feasibility has not been fully investigated in classifying thermal damage. Data was collected from laboratory experiments using concrete specimens with three different water-to-binder ratios (0.54, 0.46, and 0.35). The specimens were subjected to different target temperatures (100 °C, 200 °C, 300 °C, 400 °C, and 600 °C) and another set of cylinders was subjected to room temperature (20 °C) to represent the normal temperature condition. It was observed that P-wave velocities increased by 0.1% to 10.44% when the concretes were heated to 100 °C, and then decreased continuously until 600 °C by 48.46% to 65.80%. Conversely, coherence showed a significant decrease after exposure to 100 °C but had fluctuating values in the range of 0.110 to 0.223 thereafter. In terms of classifying the thermal damage of concrete, machine learning yielded an accuracy of 76.0% while the use of P-wave velocity and coherence yielded accuracies of 30.26% and 32.31%, respectively.
本研究调查了利用超声波信号检测混凝土早期火灾损伤的适用性。本研究分析了超声波脉冲测量中线性（波速）和非线性（相干性）参数的可靠性，以及机器学习在评估混凝土圆柱体热损伤方面的适用性。虽然机器学习已用于混凝土的一些损伤检测，但在热损伤分类方面其可行性尚未得到充分研究。通过使用三种不同水胶比（0.54、0.46和0.35）的混凝土试件进行实验室实验来收集数据。对试件施加不同的目标温度（100℃、200℃、300℃、400℃和600℃），另一组圆柱体在室温（20℃）下进行试验以代表常温条件。观察到当混凝土加热到100℃时，纵波波速增加了0.1%至10.44%，然后持续下降，直到600℃时下降了48.46%至65.80%。相反，相干性在暴露于100℃后显著下降，但此后在0.110至0.223范围内波动。在混凝土热损伤分类方面，机器学习的准确率为76.0%，而使用纵波波速和相干性的准确率分别为30.26%和32.31%。
