{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:52:59Z","timestamp":1769554379129,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency (ESA)","doi-asserted-by":"publisher","award":["4000132658\/20\/NL\/MH\/ac"],"award-info":[{"award-number":["4000132658\/20\/NL\/MH\/ac"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013student 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.<\/jats:p>","DOI":"10.3390\/s22134964","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T01:40:36Z","timestamp":1656639636000},"page":"4964","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4864-2120","authenticated-orcid":false,"given":"Alireza","family":"Entezami","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7098-4725","authenticated-orcid":false,"given":"Carlo","family":"De Michele","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1256-0163","authenticated-orcid":false,"given":"Ali Nadir","family":"Arslan","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute (FMI), Erik Palm\u00e9nin Aukio 1, FI-00560 Helsinki, Finland"}]},{"given":"Bahareh","family":"Behkamal","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rizzo, P., and Enshaeian, A. 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