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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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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. |
format | Online Article Text |
id | pubmed-6092556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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