Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

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ID: 119492
2020
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Abstract
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.
Reference Key
azimi2020sensorsdata-driven Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mohsen Azimi;Armin Dadras Eslamlou;Gokhan Pekcan;Azimi, Mohsen;Eslamlou, Armin Dadras;Pekcan, Gokhan;
Journal sensors
Year 2020
DOI
10.3390/s20102778
URL
https://www.mdpi.com/1424-8220/20/10/2778
Keywords
Deep learning Machine learning data science structural health monitoring damage detection computer vision crack detection

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