ABSTRACT

Accelerometers in smart devices have been used to successfully provide valuable information such as early warnings of earthquake activity and health monitoring of buildings. The next important step of using the acceleration measurements from smart devices is to assess building seismic damage, which is a more challenging application. A main challenge is related to the sliding motions of smart devices, which prevents acceleration measurements from directly representing the movement of underlying building floors. To detect and remove sliding motions in acceleration measurements, this paper presents an accurate and robust accelerometer-based stick-slip motion classification framework based on machine learning techniques. Three types of machine learning algorithms are introduced, and their classification performance are compared; support vector machine (SVM), multilayer perception (MLP), and recurrent neural networks (RNN). For the SVM and MLP, three classification conditions are considered: feature selection, non-linear discriminating analysis and classifier comparison. For the RNN, three hyperparameters are considered to find the best performing classification algorithm. Each algorithm is trained and validated with experimental acceleration data from a shaking table test.

摘要

智能设备中的加速度计已成功用于提供有价值的信息，例如地震活动的早期预警和建筑物的健康监测。使用来自智能设备的加速度测量的下一个重要步骤是评估建筑物的地震损坏，这是一个更具挑战性的应用。一个主要挑战与智能设备的滑动运动有关，这会阻止加速度测量值直接代表底层建筑楼层的运动。为了检测和消除加速度测量中的滑动运动，本文提出了一种基于机器学习技术的准确且稳健的基于加速度计的粘滑运动分类框架。介绍了三种机器学习算法，并比较了它们的分类性能；支持向量机 (SVM)、多层感知 (MLP) 和循环神经网络 (RNN)。对于 SVM 和 MLP，考虑了三个分类条件：特征选择、非线性判别分析和分类器比较。对于 RNN，考虑三个超参数以找到性能最佳的分类算法。每个算法都使用振动台测试的实验加速度数据进行训练和验证。