International Division, Civil Engineering Department, Hazama Ando Corporation, Akasaka 107-8658, Japan.
Department of Civil and Environmental Engineering, Saitama University, Saitama City 338-8570, Japan.
Sensors (Basel). 2020 May 9;20(9):2710. doi: 10.3390/s20092710.
In this study, a simple and customizable convolution neural network framework was used to train a vibration classification model that can be integrated into the measurement application in order to realize accurate and real-time bridge vibration status on mobile platforms. The inputs for the network model are basically the multichannel time-series signals acquired from the built-in accelerometer sensor of smartphones, while the outputs are the predefined vibration categories. To verify the effectiveness of the proposed framework, data collected from long-term monitoring of bridge were used for training a model, and its classification performance was evaluated on the test set constituting the data collected from the same bridge but not used previously for training. An iOS application program was developed on the smartphone for incorporating the trained model with predefined classification labels so that it can classify vibration datasets measured on any other bridges in real-time. The results justify the practical feasibility of using a low-latency, high-accuracy smartphone-based system amid which bottlenecks of processing large amounts of data will be eliminated, and stable observation of structural conditions can be promoted.
在这项研究中，我们使用了一个简单且可定制的卷积神经网络框架来训练一个振动分类模型，可以将其集成到测量应用中，以实现移动平台上准确和实时的桥梁振动状态。网络模型的输入基本上是从智能手机内置加速度计传感器获取的多通道时序列信号，而输出则是预定义的振动类别。为了验证所提出框架的有效性，我们使用从桥梁长期监测中收集的数据来训练模型，并在由来自同一座桥梁但以前未用于训练的数据组成的测试集上评估其分类性能。我们在智能手机上开发了一个 iOS 应用程序，将训练有素的模型与预定义的分类标签结合起来，以便可以实时对任何其他桥梁上测量的振动数据集进行分类。研究结果证明了在存在处理大量数据瓶颈的情况下，使用低延迟、高精度基于智能手机的系统的实际可行性，并可以促进结构状况的稳定观察。
