Deep neural networks for the estimation of masonry structures failures under rockfalls

Although the principal aim of the rockfall management is to prevent rock boulders from reaching the buildings instead of the buildings resisting the boulder impacts, there usually exists a residual risk that has to be assessed, even when structural protection measurements are taken. The evaluation o...

Descripción completa

Detalles Bibliográficos
Autores: Mavrouli, Olga, Skentou, Athanasia D., Carbonell Puigbó, Josep Maria|||0000-0002-2378-5053, Tsoukalas, Markos Z., Núñez Andrés, María Amparo|||0000-0003-2745-7759, Asteris, Panagiotis G.
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/388319
Acceso en línea:https://hdl.handle.net/2117/388319
https://dx.doi.org/10.3390/geosciences13060156
Access Level:acceso abierto
Palabra clave:Rock mechanics--Mathematical models
Neural networks (Computer science)
Artificial neural networks
Failure
Machine learning
Masonry structures
Optimization algorithms
Rockfalls
Transfer functions
Damage
Rockfall impact
Mecànica de roques--Models matemàtics
Xarxes neuronals artificials
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de roques
id ES_87cbe6b150c19e7ab09e036cd7d15cc5
oai_identifier_str oai:upcommons.upc.edu:2117/388319
network_acronym_str ES
network_name_str España
repository_id_str
spelling Deep neural networks for the estimation of masonry structures failures under rockfallsMavrouli, OlgaSkentou, Athanasia D.Carbonell Puigbó, Josep Maria|||0000-0002-2378-5053Tsoukalas, Markos Z.Núñez Andrés, María Amparo|||0000-0003-2745-7759Asteris, Panagiotis G.Rock mechanics--Mathematical modelsNeural networks (Computer science)Artificial neural networksFailureMachine learningMasonry structuresOptimization algorithmsRockfallsTransfer functionsDamageRockfall impactMecànica de roques--Models matemàticsXarxes neuronals artificialsÀrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de roquesAlthough the principal aim of the rockfall management is to prevent rock boulders from reaching the buildings instead of the buildings resisting the boulder impacts, there usually exists a residual risk that has to be assessed, even when structural protection measurements are taken. The evaluation of the expected damage of buildings due to rockfalls using empirical data from past events is not always possible, as transferring and applying damage observations from one area to another can be unrealistic. In order to simulate potential rockfall scenarios and their damage on buildings, numerical methods can be an alternative. However due to their increased requirements in expertise and computational costs, their integration into the risk analysis is limited, and simpler tools to assess the rockfall vulnerability of buildings are needed. This paper focuses on the application of artificial intelligence AI methods for providing the expected damage of masonry walls which are subjected to rockfall impacts. First, a damage database with 672 datasets was created numerically using the particle finite element method and the finite element method. The input variables are the rock volume (VR), the rock velocity (RV), the masonry wall (t) and the masonry tensile strength fm. The output variable is a damage index (DI) equal to the percentage of the damaged wall area. Different AI algorithms were investigated and the ANN LM 4-21-1 model was selected to optimally assess the expected wall damage. The optimum model is provided here (a) as an analytical equation and (b) in the form of contour graphs, mapping the DI value. Known the VR and the RV, the DI can be directly used as an input for the vulnerability of masonry walls into the quantitative rockfall risk assessment equation.Peer Reviewed20232023-05-2420232023-06-07journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/388319https://dx.doi.org/10.3390/geosciences13060156reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 International (CC BY 4.0)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3883192026-05-27T15:37:01Z
dc.title.none.fl_str_mv Deep neural networks for the estimation of masonry structures failures under rockfalls
title Deep neural networks for the estimation of masonry structures failures under rockfalls
spellingShingle Deep neural networks for the estimation of masonry structures failures under rockfalls
Mavrouli, Olga
Rock mechanics--Mathematical models
Neural networks (Computer science)
Artificial neural networks
Failure
Machine learning
Masonry structures
Optimization algorithms
Rockfalls
Transfer functions
Damage
Rockfall impact
Mecànica de roques--Models matemàtics
Xarxes neuronals artificials
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de roques
title_short Deep neural networks for the estimation of masonry structures failures under rockfalls
title_full Deep neural networks for the estimation of masonry structures failures under rockfalls
title_fullStr Deep neural networks for the estimation of masonry structures failures under rockfalls
title_full_unstemmed Deep neural networks for the estimation of masonry structures failures under rockfalls
title_sort Deep neural networks for the estimation of masonry structures failures under rockfalls
dc.creator.none.fl_str_mv Mavrouli, Olga
Skentou, Athanasia D.
Carbonell Puigbó, Josep Maria|||0000-0002-2378-5053
Tsoukalas, Markos Z.
Núñez Andrés, María Amparo|||0000-0003-2745-7759
Asteris, Panagiotis G.
author Mavrouli, Olga
author_facet Mavrouli, Olga
Skentou, Athanasia D.
Carbonell Puigbó, Josep Maria|||0000-0002-2378-5053
Tsoukalas, Markos Z.
Núñez Andrés, María Amparo|||0000-0003-2745-7759
Asteris, Panagiotis G.
author_role author
author2 Skentou, Athanasia D.
Carbonell Puigbó, Josep Maria|||0000-0002-2378-5053
Tsoukalas, Markos Z.
Núñez Andrés, María Amparo|||0000-0003-2745-7759
Asteris, Panagiotis G.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Rock mechanics--Mathematical models
Neural networks (Computer science)
Artificial neural networks
Failure
Machine learning
Masonry structures
Optimization algorithms
Rockfalls
Transfer functions
Damage
Rockfall impact
Mecànica de roques--Models matemàtics
Xarxes neuronals artificials
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de roques
topic Rock mechanics--Mathematical models
Neural networks (Computer science)
Artificial neural networks
Failure
Machine learning
Masonry structures
Optimization algorithms
Rockfalls
Transfer functions
Damage
Rockfall impact
Mecànica de roques--Models matemàtics
Xarxes neuronals artificials
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de roques
description Although the principal aim of the rockfall management is to prevent rock boulders from reaching the buildings instead of the buildings resisting the boulder impacts, there usually exists a residual risk that has to be assessed, even when structural protection measurements are taken. The evaluation of the expected damage of buildings due to rockfalls using empirical data from past events is not always possible, as transferring and applying damage observations from one area to another can be unrealistic. In order to simulate potential rockfall scenarios and their damage on buildings, numerical methods can be an alternative. However due to their increased requirements in expertise and computational costs, their integration into the risk analysis is limited, and simpler tools to assess the rockfall vulnerability of buildings are needed. This paper focuses on the application of artificial intelligence AI methods for providing the expected damage of masonry walls which are subjected to rockfall impacts. First, a damage database with 672 datasets was created numerically using the particle finite element method and the finite element method. The input variables are the rock volume (VR), the rock velocity (RV), the masonry wall (t) and the masonry tensile strength fm. The output variable is a damage index (DI) equal to the percentage of the damaged wall area. Different AI algorithms were investigated and the ANN LM 4-21-1 model was selected to optimally assess the expected wall damage. The optimum model is provided here (a) as an analytical equation and (b) in the form of contour graphs, mapping the DI value. Known the VR and the RV, the DI can be directly used as an input for the vulnerability of masonry walls into the quantitative rockfall risk assessment equation.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-05-24
2023
2023-06-07
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://hdl.handle.net/2117/388319
https://dx.doi.org/10.3390/geosciences13060156
url https://hdl.handle.net/2117/388319
https://dx.doi.org/10.3390/geosciences13060156
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International (CC BY 4.0)
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
Attribution 4.0 International (CC BY 4.0)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412488540323840
score 15,301603

Similares en LA Referencia