Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability
Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainti...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vergote, Thomas A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy - Chang, Guanru ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:266 ; year:2022 ; day:15 ; month:12 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.oceaneng.2022.113181 |
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| 520 | |a Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. | ||
| 520 | |a Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. | ||
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10.1016/j.oceaneng.2022.113181 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001983.pica (DE-627)ELV059755725 (ELSEVIER)S0029-8018(22)02464-7 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Vergote, Thomas A. verfasserin aut Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Bayesian updating Elsevier Random fields Elsevier Pile driveability Elsevier Raymackers, Sylvie oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.113181 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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10.1016/j.oceaneng.2022.113181 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001983.pica (DE-627)ELV059755725 (ELSEVIER)S0029-8018(22)02464-7 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Vergote, Thomas A. verfasserin aut Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Bayesian updating Elsevier Random fields Elsevier Pile driveability Elsevier Raymackers, Sylvie oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.113181 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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10.1016/j.oceaneng.2022.113181 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001983.pica (DE-627)ELV059755725 (ELSEVIER)S0029-8018(22)02464-7 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Vergote, Thomas A. verfasserin aut Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Bayesian updating Elsevier Random fields Elsevier Pile driveability Elsevier Raymackers, Sylvie oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.113181 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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10.1016/j.oceaneng.2022.113181 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001983.pica (DE-627)ELV059755725 (ELSEVIER)S0029-8018(22)02464-7 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Vergote, Thomas A. verfasserin aut Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Bayesian updating Elsevier Random fields Elsevier Pile driveability Elsevier Raymackers, Sylvie oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.113181 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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10.1016/j.oceaneng.2022.113181 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001983.pica (DE-627)ELV059755725 (ELSEVIER)S0029-8018(22)02464-7 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Vergote, Thomas A. verfasserin aut Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. Bayesian updating Elsevier Random fields Elsevier Pile driveability Elsevier Raymackers, Sylvie oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:266 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.oceaneng.2022.113181 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 266 2022 15 1215 0 |
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Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability |
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Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability |
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Vergote, Thomas A. |
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Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
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Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
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Vergote, Thomas A. |
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10.1016/j.oceaneng.2022.113181 |
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building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability |
| title_auth |
Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability |
| abstract |
Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. |
| abstractGer |
Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. |
| abstract_unstemmed |
Monopile driveability is associated with a large number of soil, hammer and model uncertainties. In this paper, a probabilistic framework is provided to understand what role variability plays in this problem. The framework is applied to a project where piles were driven into London Clay. Uncertainties on different variables are estimated, such as pile weight, water level during installation, hammer efficiency, soil variability and spatial variability and uncertainty of empirical parameters as well as uncertainty of the model itself. A Bayesian mixture model and conditional random field theory is applied to consider the spatial variability between the cone penetration tests (CPT). The combination of the spatial variability and parameter uncertainty provides a prior estimation of the self-penetration of the piles by carrying out Monte Carlo simulations with the model. |
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| title_short |
Building a framework for probabilistic assessment accounting for soil, spatial, operational and model uncertainty, applied to pile driveability |
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https://doi.org/10.1016/j.oceaneng.2022.113181 |
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Raymackers, Sylvie |
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