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Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation

The method of incorporating the sources of parameter uncertainty is crucial when conducting probabilistic analysis for service limit state (SLS) design of a deep foundation. This paper describes the method of using Monte Carlo simulation for probabilistic analyses and for calibration of resistance f...

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Detalles Bibliográficos
Autores principales: Vu, Thuy, Loehr, Erik, Smith, Douglas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092556/
https://www.ncbi.nlm.nih.gov/pubmed/30112458
http://dx.doi.org/10.1016/j.heliyon.2018.e00727
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author Vu, Thuy
Loehr, Erik
Smith, Douglas
author_facet Vu, Thuy
Loehr, Erik
Smith, Douglas
author_sort Vu, Thuy
collection PubMed
description The method of incorporating the sources of parameter uncertainty is crucial when conducting probabilistic analysis for service limit state (SLS) design of a deep foundation. This paper describes the method of using Monte Carlo simulation for probabilistic analyses and for calibration of resistance factors of drilled shafts at SLS. The paper presents discussions on the finding of an impossible case, where the different combinations of load, variability of soil strength and target probability of failure made it impossible to calibrate the SLS resistance factors. Resistance factors for drilled shafts in shale are introduced, and were found to be responsive to load levels. The higher load level, the lower the resistance factor. These findings help smooth the transition from allowable stress design to load and resistance factor design for geotechnical engineers.
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spelling pubmed-60925562018-08-15 Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation Vu, Thuy Loehr, Erik Smith, Douglas Heliyon Article The method of incorporating the sources of parameter uncertainty is crucial when conducting probabilistic analysis for service limit state (SLS) design of a deep foundation. This paper describes the method of using Monte Carlo simulation for probabilistic analyses and for calibration of resistance factors of drilled shafts at SLS. The paper presents discussions on the finding of an impossible case, where the different combinations of load, variability of soil strength and target probability of failure made it impossible to calibrate the SLS resistance factors. Resistance factors for drilled shafts in shale are introduced, and were found to be responsive to load levels. The higher load level, the lower the resistance factor. These findings help smooth the transition from allowable stress design to load and resistance factor design for geotechnical engineers. Elsevier 2018-08-13 /pmc/articles/PMC6092556/ /pubmed/30112458 http://dx.doi.org/10.1016/j.heliyon.2018.e00727 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vu, Thuy
Loehr, Erik
Smith, Douglas
Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation
title Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation
title_full Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation
title_fullStr Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation
title_full_unstemmed Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation
title_short Probabilistic analysis and resistance factor calibration for deep foundation design using Monte Carlo simulation
title_sort probabilistic analysis and resistance factor calibration for deep foundation design using monte carlo simulation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092556/
https://www.ncbi.nlm.nih.gov/pubmed/30112458
http://dx.doi.org/10.1016/j.heliyon.2018.e00727
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