Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images
Open accessAbstract
The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher–student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.
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AANN is typically used to simulate a nonlinear PCA process and solve problems related to feature extraction, pattern recognition, and dimensionality reduction.
“The program is typically used to simulate a nonlinear PCA process and solve problems related to feature extraction, pattern recognition, and dimensionality reduction [261,262].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [262]
Section 2.2.1 (page not provided) describes the auto-associative neural network as mapping feature samples through a nonlinear function into a lower-dimensional space, supporting the dimensionality-reduction aspect. The paper also uses it for feature normalization, but does not describe pattern recognition as an AANN application or state that AANN is typically used to simulate nonlinear PCA; nonlinear PCA appears in a cited reference’s title. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAANN is an ANN structure with the same input and output layer.
“AANN is an ANN structure with the same input and output layer.”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [262]
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- Authors
- Entezami, A., De Michele, C., Arslan, A. N., & Behkamal, B.
- Affiliations
- Politecnico di Milano (IT)
- Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
- Finnish Meteorological Institute (FI)
- Corresponding author
- Entezami, Alireza
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-06-30
- Volume
- 22
- Issue
- 13
- Pages
- 4964
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9269802
- OpenAlex
- W4283760109
- Cited by
- 19
- References
- 41
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 6 · 2022: 1
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Space Agency (ESA) (4000132658/20/NL/MH/ac)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Synthetic Aperture Radar (SAR) Applications and Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- structural health monitoring, InSAR, long-term monitoring, small data, unsupervised learning, teacher-student learning, feature normalization, artificial neural networks, hyperparameter optimization, Mahalanobis distance, TerraSAR-X, civil engineering structures
- MeSH terms
- Algorithms, Environmental Monitoring, Humans, Interferometry, Radar, Neural Networks, Computer
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