Epp Tyler, Svecova Dagmar, Cha Young-Jin
Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
Sensors (Basel). 2018 Mar 29;18(4):1018. doi: 10.3390/s18041018.
Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures.
结构健康监测（SHM）已转向数据密集型系统，利用多种传感器类型更定期地监测桥梁和大坝等基础设施。这项工作面临的问题之一是被检查结构的规模以及进行测试所需的时间。安装能够及时提供测量数据的自动化系统是克服这些障碍的一种方法。本研究提出了一种人工神经网络（ANN）应用程序，该程序使用实验室条件下钢筋混凝土梁的空气耦合麦克风以及从停车场现场实验收集的数据，从小规模的冲击回波数据训练样本中确定完好和受损位置。现场的冲击回波测试以半自动方式进行，以加快现场损伤检测测试的前端工作。当使用最小二乘距离方法时，人工神经网络的使用消除了对完好和受损位置分类使用用户定义截止值的需求。据推测，这可能会在监测大型民用钢筋混凝土（RC）结构时显著缩短测试时间。
