Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review
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DOI：
10.3390/s20102778
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发表时间：
2020-05-01
期刊：
影响因子：
3.9
通讯作者：
Pekcan, Gokhan
中科院分区：
文献类型：
--
作者：
Azimi, Mohsen;Eslamlou, Armin Dadras;Pekcan, Gokhan
由于传感器的最新技术进步以及高速互联网和基于云的计算，结构健康监测（SHM）中的数据驱动方法越来越受欢迎。自从在土木工程中引入深度学习（DL）以来，特别是在SHM中，这种新兴且有前途的工具引起了研究人员的极大关注。本文的主要目标是回顾最新出版物在SHM使用新兴的DL为基础的方法，并为读者提供各种SHM应用的整体了解。在简要介绍之后，将概述各种DL方法（例如，深度神经网络、迁移学习等）给出讨论了基于振动、基于视觉的监测的过程和应用，沿着了一些用于SHM的最新技术，如传感器、无人机（UAV）等。该评论的结论是基于DL的方法在SHM应用中的前景和潜在局限性。
Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.