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

By Mohsen Azimi, Armin Dadras Eslamlou, Gokhan Pekcan. Italian National Conference on Sensors, 2020

648 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/53ebde5a62fbb66c364246b1995baf291e626c25

How researchers see this paper

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In Civil and Structural Engineering

1. Arman Malekloo
2. Mark A. Girolami
3. Ekin Ozer
4. Mohammad AlHamaydeh

Citations in the corpus

• Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “38 The authors in their paper discussed in great length the usage of DL and machine vision and the new methods of monitoring damages, that is, mobile sensors and UAVs in SHM application.”