Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images
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 proble...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/13/4964 |
| _version_ | 1797408167032782848 |
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| author | Alireza Entezami Carlo De Michele Ali Nadir Arslan Bahareh Behkamal |
| author_facet | Alireza Entezami Carlo De Michele Ali Nadir Arslan Bahareh Behkamal |
| author_sort | Alireza Entezami |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T03:54:17Z |
| format | Article |
| id | doaj.art-4922427d078341bd94b29c42f6f400c5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:54:17Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4922427d078341bd94b29c42f6f400c52023-12-03T14:22:27ZengMDPI AGSensors1424-82202022-06-012213496410.3390/s22134964Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite ImagesAlireza Entezami0Carlo De Michele1Ali Nadir Arslan2Bahareh Behkamal3Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, ItalyDepartment of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, ItalyFinnish Meteorological Institute (FMI), Erik Palménin Aukio 1, FI-00560 Helsinki, FinlandDepartment of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, ItalyThe 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.https://www.mdpi.com/1424-8220/22/13/4964structural health monitoringcollapsedisplacement analysismachine learningsynthetic aperture radarbridges |
| spellingShingle | Alireza Entezami Carlo De Michele Ali Nadir Arslan Bahareh Behkamal Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images Sensors structural health monitoring collapse displacement analysis machine learning synthetic aperture radar bridges |
| title | Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images |
| title_full | Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images |
| title_fullStr | Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images |
| title_full_unstemmed | Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images |
| title_short | Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images |
| title_sort | detection of partially structural collapse using long term small displacement data from satellite images |
| topic | structural health monitoring collapse displacement analysis machine learning synthetic aperture radar bridges |
| url | https://www.mdpi.com/1424-8220/22/13/4964 |
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