Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete

[EN] Gradient damage in cementitious materials is typically driven by chemical and/or physical agents that penetrate from the exterior. Depending on the mechanism, it may lead to reduced porosity, cracking, or steel corrosion (e.g., carbonation), or to increased porosity, microcracking, expansion, a...

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Autores: Gallardo-Llopis, Carles, Vazquez-Martinez, Santiago, Font-Pérez, Alba, Payà, Jordi, Gosálbez Castillo, Jorge|||0000-0001-6520-9014, Morell-Monzó, Sergio|||0000-0001-8883-2618
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/229988
Acceso en línea:https://riunet.upv.es/handle/10251/229988
Access Level:acceso abierto
Palabra clave:Ultrasound tomography
Reconstruction algorithm
Neural network
NDT
Concrete damage
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spelling Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in ConcreteGallardo-Llopis, CarlesVazquez-Martinez, SantiagoFont-Pérez, AlbaPayà, JordiGosálbez Castillo, Jorge|||0000-0001-6520-9014Morell-Monzó, Sergio|||0000-0001-8883-2618Ultrasound tomographyReconstruction algorithmNeural networkNDTConcrete damage[EN] Gradient damage in cementitious materials is typically driven by chemical and/or physical agents that penetrate from the exterior. Depending on the mechanism, it may lead to reduced porosity, cracking, or steel corrosion (e.g., carbonation), or to increased porosity, microcracking, expansion, and spalling (e.g., external sulphate or acid attack, as well as thermal damage). Delineating the boundary of this damage is therefore essential for concrete quality assessment. This work pursues two goals. First, we use neural networks (NNs) to perform ultrasonic tomographic reconstruction of concrete specimens in order to estimate the advancing damage front. Unlike X-ray tomography, ultrasonic tomography is strongly affected by diffraction and related wave phenomena; NNs can learn to compensate for these effects but require large training datasets. Second, we demonstrate a cost-effective training strategy based on simulated measurements, avoiding the effort of assembling extensive experimental databases for cement-based materials. The trained reconstruction network was evaluated on cylindrical specimens composed of an outer, externally exposed ring and an inner, unaffected core. Reconstruction accuracy, quantified by the Structural Similarity Index (SSIM), reached 0.95 for simulated signals and up to 0.82 for real signals. These results indicate that NN-based ultrasonic tomography, trained purely on simulations, can reliably delineate gradient damage fronts and provide a practical tool for non-destructive evaluation of concrete.This research was funded by MCIN/AEI/10.13039/501100011033 (grant number PID2020-120262GB-I00).MDPI AGEscuela Técnica Superior de Ingeniería de TelecomunicaciónDepartamento de ComunicacionesInstituto Universitario de Telecomunicación y Aplicaciones MultimediaInstituto de Investigación para la Gestión Integrada de Zonas CosterasAgencia Estatal de InvestigaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-04-12journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/229988reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-120262GB-I00 MONITORIZACION INTELIGENTE DE MATERIALES COMPUESTOS A TRAVES DE ONDAS MECANICAS Y ALGORITMOS DE PROCESADO NO LINEAL DE LA SEÑALopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2299882026-06-13T07:49:27Z
dc.title.none.fl_str_mv Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete
title Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete
spellingShingle Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete
Gallardo-Llopis, Carles
Ultrasound tomography
Reconstruction algorithm
Neural network
NDT
Concrete damage
title_short Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete
title_full Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete
title_fullStr Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete
title_full_unstemmed Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete
title_sort Ultrasound Reconstruction Tomography Using Neural Networks Trained with Simulated Data: A Case of Theoretical Gradient Damage in Concrete
dc.creator.none.fl_str_mv Gallardo-Llopis, Carles
Vazquez-Martinez, Santiago
Font-Pérez, Alba
Payà, Jordi
Gosálbez Castillo, Jorge|||0000-0001-6520-9014
Morell-Monzó, Sergio|||0000-0001-8883-2618
author Gallardo-Llopis, Carles
author_facet Gallardo-Llopis, Carles
Vazquez-Martinez, Santiago
Font-Pérez, Alba
Payà, Jordi
Gosálbez Castillo, Jorge|||0000-0001-6520-9014
Morell-Monzó, Sergio|||0000-0001-8883-2618
author_role author
author2 Vazquez-Martinez, Santiago
Font-Pérez, Alba
Payà, Jordi
Gosálbez Castillo, Jorge|||0000-0001-6520-9014
Morell-Monzó, Sergio|||0000-0001-8883-2618
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Escuela Técnica Superior de Ingeniería de Telecomunicación
Departamento de Comunicaciones
Instituto Universitario de Telecomunicación y Aplicaciones Multimedia
Instituto de Investigación para la Gestión Integrada de Zonas Costeras
Agencia Estatal de Investigación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Ultrasound tomography
Reconstruction algorithm
Neural network
NDT
Concrete damage
topic Ultrasound tomography
Reconstruction algorithm
Neural network
NDT
Concrete damage
description [EN] Gradient damage in cementitious materials is typically driven by chemical and/or physical agents that penetrate from the exterior. Depending on the mechanism, it may lead to reduced porosity, cracking, or steel corrosion (e.g., carbonation), or to increased porosity, microcracking, expansion, and spalling (e.g., external sulphate or acid attack, as well as thermal damage). Delineating the boundary of this damage is therefore essential for concrete quality assessment. This work pursues two goals. First, we use neural networks (NNs) to perform ultrasonic tomographic reconstruction of concrete specimens in order to estimate the advancing damage front. Unlike X-ray tomography, ultrasonic tomography is strongly affected by diffraction and related wave phenomena; NNs can learn to compensate for these effects but require large training datasets. Second, we demonstrate a cost-effective training strategy based on simulated measurements, avoiding the effort of assembling extensive experimental databases for cement-based materials. The trained reconstruction network was evaluated on cylindrical specimens composed of an outer, externally exposed ring and an inner, unaffected core. Reconstruction accuracy, quantified by the Structural Similarity Index (SSIM), reached 0.95 for simulated signals and up to 0.82 for real signals. These results indicate that NN-based ultrasonic tomography, trained purely on simulations, can reliably delineate gradient damage fronts and provide a practical tool for non-destructive evaluation of concrete.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-04-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/229988
url https://riunet.upv.es/handle/10251/229988
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-120262GB-I00 MONITORIZACION INTELIGENTE DE MATERIALES COMPUESTOS A TRAVES DE ONDAS MECANICAS Y ALGORITMOS DE PROCESADO NO LINEAL DE LA SEÑAL
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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