Cited by 798Structural Health Monitoring TechniquesInfrastructure Maintenance and MonitoringStructural Engineering and Materials Analysis

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

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Azimi, Mohsen, Eslamlou, Armin, Pekcan, Gokhan·2020·Sensors

DOI 10.3390/s20102778PMC PMC7294417

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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.

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Authors: Azimi, M., Eslamlou, A., & Pekcan, G.
Affiliations: University of Nevada, Reno (US)
Iran University of Science and Technology (IR)
Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA
Corresponding author: Azimi, Mohsen
ORCID: Azimi, Mohsen
Eslamlou, Armin
Pekcan, Gokhan
Venue: Sensors
Publisher: MDPI AG
Published: 2020-05-13
Volume: 20
Issue: 10
Pages: 2778
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s20102778
PMC: PMC7294417
OpenAlex: W3024912007
Cited by: 769
References: 232
Citations by year: 2026: 151 · 2025: 181 · 2024: 133 · 2023: 138 · 2022: 96 · 2021: 56 · 2020: 14
FWCI: 44.44
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sensors (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Science Foundation (1646420)
Topics: Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
Keywords: structural health monitoring, damage detection, deep learning, vibration-based condition monitoring, vision-based monitoring, transfer learning, deep neural network, unmanned aerial vehicles, structural sensors, civil infrastructure

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