NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING

K. Mosalam, S. Muin, Yuqing Gao
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引用次数: 8

Abstract

This paper presents two on-going efforts of the Pacific Earthquake Engineering Research (PEER) center in the area of structural health monitoring. The first is data-driven damage assessment, which focuses on using data from instrumented buildings to compute the values of damage features. Using machine learning algorithms, these damage features are used for rapid identification of the level and location of damage after earthquakes. One of the damage features identified to be highly efficient is the cumulative absolute velocity. The second is vision-based automated damage identification and assessment from images. Deep learning techniques are used to conduct several identification tasks from images, examples of which are the structural component type, and level and type of damage. The objective is to use crowdsourcing, allowing the general public to take photographs of damage and upload them to a server where damage is automatically identified using deep learning algorithms. The paper also introduces PEER.s effort and preliminary results in engaging the engineering and computer science communities in such developments through the PEER Hub Image-Net (F-Net) challenge.
结构健康监测的新方向
本文介绍了太平洋地震工程研究中心(PEER)在结构健康监测领域正在进行的两项工作。第一种是数据驱动的损伤评估,其重点是使用仪器建筑物的数据来计算损伤特征值。使用机器学习算法,这些损伤特征用于快速识别地震后的损伤水平和位置。被确定为高效的损伤特征之一是累积绝对速度。二是基于视觉的图像自动损伤识别与评估。深度学习技术用于从图像中执行几个识别任务,其中的示例是结构组件类型,以及损坏的级别和类型。目标是使用众包,允许公众拍摄损坏照片并将其上传到服务器,然后使用深度学习算法自动识别损坏。本文还介绍了PEER。通过PEER Hub Image-Net (F-Net)挑战,使工程和计算机科学社区参与到此类发展中来,这方面的努力和初步成果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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