A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data

Dynamic Cone Penetration Tests (CPTu) profile offshore sediments by impact penetration. To exploit their results in full the measured data is converted to obtain a quasi-static equivalent profile. Dynamic CPTu conversion requires calibrated correction models. Calibration is currently done by using p...

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Autores: Collico, Stefano, Arroyo Álvarez de Toledo, Marcos|||0000-0001-9384-9107, Kopf, Achim, Devincenzi, M.J.
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/385451
Acesso em linha:https://hdl.handle.net/2117/385451
https://dx.doi.org/10.1139/cgj-2022-0311
Access Level:acceso abierto
Palavra-chave:Soil mechanics--Testing
Mecànica dels sòls -- Proves
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia
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oai_identifier_str oai:upcommons.upc.edu:2117/385451
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repository_id_str
spelling A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu dataCollico, StefanoArroyo Álvarez de Toledo, Marcos|||0000-0001-9384-9107Kopf, AchimDevincenzi, M.J.Soil mechanics--TestingMecànica dels sòls -- ProvesÀrees temàtiques de la UPC::Enginyeria civil::GeotècniaDynamic Cone Penetration Tests (CPTu) profile offshore sediments by impact penetration. To exploit their results in full the measured data is converted to obtain a quasi-static equivalent profile. Dynamic CPTu conversion requires calibrated correction models. Calibration is currently done by using paired (i.e., very close) quasi-static and dynamic tests. It is shown here that paired test data, which may be inconvenient to acquire offshore, are not strictly necessary to convert dynamic CPTu data. A new probabilistic methodology is proposed to call upon quasi-static results from a much wider area in the conversion procedure. Those results feed the prior distribution of a converted profile, within a Bayesian updating scheme where strain rate coefficient and correction model error are also described by updated stochastic variables. The updating scheme is solved numerically using the Transitional Markov Chain MonteCarlo sampling algorithm. To avoid undue influence of local profile heterogeneity, the statistic treatment of the quasi-static CPTu data takes place in the frequency domain, using a discrete cosine transform (DCT). The new procedure is applied to a CPTu campaign offshore Nice (France): dynamic tests are converted with equal precision using quasi-static data acquired at distances orders of magnitude larger than what was previously employed.Peer Reviewed20232023-05-0120232023-03-24journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/385451https://dx.doi.org/10.1139/cgj-2022-0311reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3854512026-05-27T15:37:01Z
dc.title.none.fl_str_mv A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
title A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
spellingShingle A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
Collico, Stefano
Soil mechanics--Testing
Mecànica dels sòls -- Proves
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia
title_short A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
title_full A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
title_fullStr A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
title_full_unstemmed A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
title_sort A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data
dc.creator.none.fl_str_mv Collico, Stefano
Arroyo Álvarez de Toledo, Marcos|||0000-0001-9384-9107
Kopf, Achim
Devincenzi, M.J.
author Collico, Stefano
author_facet Collico, Stefano
Arroyo Álvarez de Toledo, Marcos|||0000-0001-9384-9107
Kopf, Achim
Devincenzi, M.J.
author_role author
author2 Arroyo Álvarez de Toledo, Marcos|||0000-0001-9384-9107
Kopf, Achim
Devincenzi, M.J.
author2_role author
author
author
dc.subject.none.fl_str_mv Soil mechanics--Testing
Mecànica dels sòls -- Proves
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia
topic Soil mechanics--Testing
Mecànica dels sòls -- Proves
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia
description Dynamic Cone Penetration Tests (CPTu) profile offshore sediments by impact penetration. To exploit their results in full the measured data is converted to obtain a quasi-static equivalent profile. Dynamic CPTu conversion requires calibrated correction models. Calibration is currently done by using paired (i.e., very close) quasi-static and dynamic tests. It is shown here that paired test data, which may be inconvenient to acquire offshore, are not strictly necessary to convert dynamic CPTu data. A new probabilistic methodology is proposed to call upon quasi-static results from a much wider area in the conversion procedure. Those results feed the prior distribution of a converted profile, within a Bayesian updating scheme where strain rate coefficient and correction model error are also described by updated stochastic variables. The updating scheme is solved numerically using the Transitional Markov Chain MonteCarlo sampling algorithm. To avoid undue influence of local profile heterogeneity, the statistic treatment of the quasi-static CPTu data takes place in the frequency domain, using a discrete cosine transform (DCT). The new procedure is applied to a CPTu campaign offshore Nice (France): dynamic tests are converted with equal precision using quasi-static data acquired at distances orders of magnitude larger than what was previously employed.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-05-01
2023
2023-03-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/385451
https://dx.doi.org/10.1139/cgj-2022-0311
url https://hdl.handle.net/2117/385451
https://dx.doi.org/10.1139/cgj-2022-0311
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
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
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
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score 15,301629

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