Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

Data-driven methodologies are among the most effective tools for damage detection of complex existing buildings, such as heritage structures. Indeed, the historical evolution and actual behaviour of these assets are often unknown, no physical models are available, and the assessment must be performe...

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Autores principales:	Alberto Barontini, Maria Giovanna Masciotta, Paulo Amado-Mendes, Luís F. Ramos, Paulo B. Lourenço
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	structural health monitoring damage detection historical buildings negative selection algorithm Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/4aafa7908e6446418c67584c51d94c9e
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id	oai:doaj.org-article:4aafa7908e6446418c67584c51d94c9e
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spelling	oai:doaj.org-article:4aafa7908e6446418c67584c51d94c9e2021-11-11T19:09:03ZReducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques10.3390/s212171551424-8220https://doaj.org/article/4aafa7908e6446418c67584c51d94c9e2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7155https://doaj.org/toc/1424-8220Data-driven methodologies are among the most effective tools for damage detection of complex existing buildings, such as heritage structures. Indeed, the historical evolution and actual behaviour of these assets are often unknown, no physical models are available, and the assessment must be performed only based on the tracking of a set of damage-sensitive features. Selecting the most representative state indicators to monitor and sampling them with an adequate number of records are therefore essential tasks to guarantee the successful performance of the damage detection strategy. Despite their relevance, these aspects have been frequently taken for granted and little attention has been paid to them by the scientific community working in the field of Structural Health Monitoring. The present paper aims to fill this gap by proposing a multistep strategy to drive the selection of meaningful pairs of correlated features in order to support the damage detection as a one-class classification problem. Numerical methods to reduce the number of necessary acquisitions and estimate the performance of approximation techniques are also provided. The analyses carried out to test and validate the proposed strategy exploit a dense dataset collected during the long-term monitoring of an outstanding heritage structure, i.e., the Church of ‘Santa Maria de Belém’ in Lisbon.Alberto BarontiniMaria Giovanna MasciottaPaulo Amado-MendesLuís F. RamosPaulo B. LourençoMDPI AGarticlestructural health monitoringdamage detectionhistorical buildingsnegative selection algorithmChemical technologyTP1-1185ENSensors, Vol 21, Iss 7155, p 7155 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	structural health monitoring damage detection historical buildings negative selection algorithm Chemical technology TP1-1185
spellingShingle	structural health monitoring damage detection historical buildings negative selection algorithm Chemical technology TP1-1185 Alberto Barontini Maria Giovanna Masciotta Paulo Amado-Mendes Luís F. Ramos Paulo B. Lourenço Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques
description	Data-driven methodologies are among the most effective tools for damage detection of complex existing buildings, such as heritage structures. Indeed, the historical evolution and actual behaviour of these assets are often unknown, no physical models are available, and the assessment must be performed only based on the tracking of a set of damage-sensitive features. Selecting the most representative state indicators to monitor and sampling them with an adequate number of records are therefore essential tasks to guarantee the successful performance of the damage detection strategy. Despite their relevance, these aspects have been frequently taken for granted and little attention has been paid to them by the scientific community working in the field of Structural Health Monitoring. The present paper aims to fill this gap by proposing a multistep strategy to drive the selection of meaningful pairs of correlated features in order to support the damage detection as a one-class classification problem. Numerical methods to reduce the number of necessary acquisitions and estimate the performance of approximation techniques are also provided. The analyses carried out to test and validate the proposed strategy exploit a dense dataset collected during the long-term monitoring of an outstanding heritage structure, i.e., the Church of ‘Santa Maria de Belém’ in Lisbon.
format	article
author	Alberto Barontini Maria Giovanna Masciotta Paulo Amado-Mendes Luís F. Ramos Paulo B. Lourenço
author_facet	Alberto Barontini Maria Giovanna Masciotta Paulo Amado-Mendes Luís F. Ramos Paulo B. Lourenço
author_sort	Alberto Barontini
title	Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques
title_short	Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques
title_full	Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques
title_fullStr	Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques
title_full_unstemmed	Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques
title_sort	reducing the training samples for damage detection of existing buildings through self-space approximation techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4aafa7908e6446418c67584c51d94c9e
work_keys_str_mv	AT albertobarontini reducingthetrainingsamplesfordamagedetectionofexistingbuildingsthroughselfspaceapproximationtechniques AT mariagiovannamasciotta reducingthetrainingsamplesfordamagedetectionofexistingbuildingsthroughselfspaceapproximationtechniques AT pauloamadomendes reducingthetrainingsamplesfordamagedetectionofexistingbuildingsthroughselfspaceapproximationtechniques AT luisframos reducingthetrainingsamplesfordamagedetectionofexistingbuildingsthroughselfspaceapproximationtechniques AT pauloblourenco reducingthetrainingsamplesfordamagedetectionofexistingbuildingsthroughselfspaceapproximationtechniques
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Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

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