Shakya, Sudan ; Schmüdderich, Christoph ; Machaček, Jan ; Prada-Sarmiento, Luis Felipe ; Wichtmann, Torsten (2024)
Influence of Sampling Methods on the Accuracy of Machine Learning Predictions Used for Strain-Dependent Slope Stability.
In: Geosciences, 14 (2)
doi: 10.3390/geosciences14020044
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Supervised machine learning (ML) techniques have been widely used in various geotechnical applications. While much attention is given to the ML techniques and the specific geotechnical problem being addressed, the influence of sampling methods on ML performance has received relatively less scrutiny. This study applies supervised ML to the strain-dependent slope stability (SDSS) method for the prediction of the factor of safety (FoS) using hypoplasticity. It delves into different sampling strategies for training the ML model, emphasizing predictions of soil behavior in lower stress ranges. A novel sampling method is introduced to ensure a more representative distribution of samples in these ranges, which is challenging to achieve through traditional sampling approaches. The ML models were trained using traditional and modified sampling methods. Subsequently, slope stability analyses using SDSS were conducted with ML models trained from six different sampling methods. The results illustrate the impact of sampling methods on the FoS. Besides a noticeable improvement in predictions of shear stresses within the lower stress ranges, a decisive effect on the overall FoS was observed as well.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Shakya, Sudan ; Schmüdderich, Christoph ; Machaček, Jan ; Prada-Sarmiento, Luis Felipe ; Wichtmann, Torsten |
| Art des Eintrags: | Bibliographie |
| Titel: | Influence of Sampling Methods on the Accuracy of Machine Learning Predictions Used for Strain-Dependent Slope Stability |
| Sprache: | Englisch |
| Publikationsjahr: | 5 Februar 2024 |
| Ort: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Geosciences |
| Jahrgang/Volume einer Zeitschrift: | 14 |
| (Heft-)Nummer: | 2 |
| Kollation: | 23 Seiten |
| DOI: | 10.3390/geosciences14020044 |
| URL / URN: | https://www.mdpi.com/2076-3263/14/2/44 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Supervised machine learning (ML) techniques have been widely used in various geotechnical applications. While much attention is given to the ML techniques and the specific geotechnical problem being addressed, the influence of sampling methods on ML performance has received relatively less scrutiny. This study applies supervised ML to the strain-dependent slope stability (SDSS) method for the prediction of the factor of safety (FoS) using hypoplasticity. It delves into different sampling strategies for training the ML model, emphasizing predictions of soil behavior in lower stress ranges. A novel sampling method is introduced to ensure a more representative distribution of samples in these ranges, which is challenging to achieve through traditional sampling approaches. The ML models were trained using traditional and modified sampling methods. Subsequently, slope stability analyses using SDSS were conducted with ML models trained from six different sampling methods. The results illustrate the impact of sampling methods on the FoS. Besides a noticeable improvement in predictions of shear stresses within the lower stress ranges, a decisive effect on the overall FoS was observed as well. |
| Freie Schlagworte: | journal |
| Zusätzliche Informationen: | Artikel-ID: 44 |
| Fachbereich(e)/-gebiet(e): | 13 Fachbereich Bau- und Umweltingenieurwissenschaften 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Geotechnik |
| Hinterlegungsdatum: | 06 Feb 2024 07:00 |
| Letzte Änderung: | 15 Mai 2024 14:23 |
| PPN: | 516942794 |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Influence of Sampling Methods on the Accuracy of Machine Learning Predictions Used for Strain-Dependent Slope Stability. (deposited 14 Mai 2024 13:42)
- Influence of Sampling Methods on the Accuracy of Machine Learning Predictions Used for Strain-Dependent Slope Stability. (deposited 06 Feb 2024 07:00) [Gegenwärtig angezeigt]
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