Continuous structural health monitoring of civil infrastructure can be achieved by deploying an Internet of Things network of distributed acceleration sensors in buildings to capture floor movement. Postdisaster damage levels can be computed based on the peak relative floor displacement as specified in government standards. This article uses machine learning approaches to identify the status of buildings postevent based on accelerometer traces. Prior work in the field assumed the use of high-quality accelerometers for displacement estimation. In this article, we focus on using lower quality and cheaper accelerometers, while accounting for noise effects by incorporating noisy data sets in machine learning approaches for classification. A labeled acceleration data set of buildings response to earthquakes was created, where each sample is labeled with its corresponding damage severity. Sensor noise is included in the data set to model nonideal sensors. Classification performance of machine learning algorithms, such as support vector machine, K-nearest neighbor, and convolutional neural network, is presented. Techniques for addressing noise levels are proposed, and the results are compared with regular noise cancellation techniques that adopt high-pass filtering. Note to Practitioners —This article presents a methodology for automatic estimation of buildings status in the aftermath of a natural disaster, such as an earthquake. It focuses on using low-cost inertial sensors, such as accelerometers, to sense buildings’ vibrations and then applying machine learning algorithms to detect damage. Utilizing the convolutional network approach, the proposed methods detect the building damage state with high accuracy. Since this article focuses on using cheap sensors, the cost of deploying a sensor network to monitor buildings is reduced significantly. Deploying this network enables rescue and reconnaissance teams to have a clear view of the most vulnerable structures.

中文翻译：

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使用噪声数据集进行结构健康监测的机器学习方法

可以通过在建筑物中部署分布式加速度传感器的物联网网络来捕获地板运动来对民用基础设施进行连续的结构健康监控。灾后破坏程度可以根据政府标准规定的相对地面峰值位移来计算。本文使用机器学习方法，基于加速度计跟踪来识别建筑物事后状况。该领域的先前工作假定使用高质量的加速度计进行位移估计。在本文中，我们着重于使用质量较低且价格较便宜的加速度计，同时通过将噪声数据集纳入机器学习方法进行分类来解决噪声影响。创建了标记的建筑物对地震反应的加速度数据集，每个样品都标有相应的损坏严重程度。数据集中包含传感器噪声以对非理想传感器进行建模。给出了机器学习算法的分类性能，例如支持向量机，K近邻算法和卷积神经网络。提出了解决噪声水平的技术，并将结果与​​采用高通滤波的常规噪声消除技术进行了比较。执业者注意 —本文介绍了在自然灾害（例如地震）发生后自动估算建筑物状态的方法。它专注于使用低成本的惯性传感器（例如加速度计）来感应建筑物的振动，然后应用机器学习算法来检测损坏。利用卷积网络方法，所提出的方法可以高精度地检测建筑物的破坏状态。由于本文着重于使用廉价的传感器，因此可以大大降低部署传感器网络以监视建筑物的成本。部署此网络可使救援和侦察团队清晰了解最脆弱的建筑物。