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.
|Original language||English (US)|
|Number of pages||9|
|Journal||IEEE Transactions on Automation Science and Engineering|
|State||Published - Dec 4 2019|