Assessing the structural integrity of buildings after an earthquake is necessary for citizens to be able to use these facilities safely after the event. The currently available structural health monitoring (SHM) systems use a dense network of sensors installed in buildings to monitor their behavior during earthquakes. Such a network is impractical with respect to cost and deployment time for the vast majority of buildings; therefore, most structures remain uninstrumented. However, a massive network of citizen-owned smart devices, such as tablets and smartphones that contain cameras and vibration sensors, has already been deployed. This paper develops a framework that can crowdsource readings from distributed citizen-owned smart devices and convert these readings into actionable information. Although prior community-based seismic research focused on using smartphones to provide early disaster warnings, the proposed system focuses specifically on using video captured on a smartphone to directly assess the structural health of buildings post-earthquake, thus providing citizens and emergency personnel with immediate relevant information regarding the health state of buildings. This paper presents a novel self-calibration technique for a smartphone camera using its internal accelerometer readings. Shake table experiments show that the proposed technique can achieve sub-millimeter accuracy, demonstrating its suitability for SHM applications.

中文翻译：

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基于社区的多感官结构健康监测系统：智能手机加速度计和摄像头融合方法


为了让公民能够在地震发生后安全地使用这些设施，有必要在地震后评估建筑物的结构完整性。目前可用的结构健康监测（SHM）系统使用安装在建筑物中的密集传感器网络来监测其在地震期间的行为。对于绝大多数建筑物来说，就成本和部署时间而言，这种网络是不切实际的；因此，大多数建筑物仍未配备仪器。然而，一个由公民拥有的智能设备组成的庞大网络已经部署，例如包含摄像头和振动传感器的平板电脑和智能手机。本文开发了一个框架，可以众包来自分布式公民拥有的智能设备的读数，并将这些读数转换为可操作的信息。尽管之前基于社区的地震研究侧重于使用智能手机提供早期灾害预警，但拟议的系统特别侧重于使用智能手机上捕获的视频来直接评估地震后建筑物的结构健康状况，从而为公民和应急人员提供直接相关的信息有关建筑物健康状况的信息。本文提出了一种利用内部加速度计读数的智能手机摄像头自校准技术。振动台实验表明，所提出的技术可以实现亚毫米精度，证明其适用于 SHM 应用。