The DOI matches its Crossref record; 234 of the 234 references it cites were found in a registry or an index.
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review
Open accessAbstract
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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![Figure 5 Illustration of (a) crack and non-crack image classification, (b) object localization, and (c) pixel-level segmentation (open-access images from Yang et al. [101]).](/paper-figures/72f0eaf6fbd04ef394503c7289d8e5af/data-driven-structural-health-monitoring-and-damage-detection-through-deep-figure-5.jpg)
![Figure 7 Four transfer learning approaches based on target domain size and similarity to the source domain [57].](/paper-figures/8884ec66d13b441d9d903048b0ac4127/data-driven-structural-health-monitoring-and-damage-detection-through-deep-figure-7.jpg)
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“In particular, the choice of this specific size is based on the fact that the AlexNet 34 (and also similar CNN architectures) has been shown to offer satisfactory performances with an input matrix of that size or very similar, albeit for different types of problems with respect to the one presented in this work (e.g., image classification).”
From Vibration-based structural health monitoring exploiting a combination of convolutional neural networks and autoencoders for temperature effects neutralization (2022) · cites this paper as [48]
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Cited by
Sorted by citations
Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022) · Structural Health Monitoring · doi:10.1177/14759217211036880 · Cited by 607
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Metadata
- 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
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-05-13
- Volume
- 20
- Issue
- 10
- Pages
- 2778
- ISSN
- 1424-8220 (ISSN-L)
- 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
- 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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