An Optimized Artificial Neural Network Structure to Predict Clay Sensitivity in a High Landslide Prone Area Using Piezocone Penetration Test (CPTu) Data: A Case Study in Southwest of Sweden (Englisch)
- Neue Suche nach: Abbaszadeh Shahri, Abbas
- Neue Suche nach: Abbaszadeh Shahri, Abbas
In:
Geotechnical and Geological Engineering
;
34
, 2
; 745-758
;
2016
-
ISSN:
- Aufsatz (Zeitschrift) / Elektronische Ressource
-
Titel:An Optimized Artificial Neural Network Structure to Predict Clay Sensitivity in a High Landslide Prone Area Using Piezocone Penetration Test (CPTu) Data: A Case Study in Southwest of Sweden
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Beteiligte:Abbaszadeh Shahri, Abbas ( Autor:in )
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Erschienen in:Geotechnical and Geological Engineering ; 34, 2 ; 745-758
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Verlag:
- Neue Suche nach: Springer International Publishing
- Neue Suche nach: Springer Science + Business Media B.V
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Erscheinungsort:Dordrecht [u.a.]
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Erscheinungsdatum:2016
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ISSN:
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ZDBID:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
- Neue Suche nach: 56.20$jIngenieurgeologie$jBodenmechanik / 38.58 / 57.00 / 38.58$jGeomechanik / 56.20 / 57.00$jBergbau: Allgemeines
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Schlagwörter:
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Klassifikation:
BKL: 56.20$jIngenieurgeologie$jBodenmechanik / 38.58 Geomechanik / 57.00 Bergbau: Allgemeines / 38.58$jGeomechanik / 56.20 Ingenieurgeologie, Bodenmechanik / 57.00$jBergbau: Allgemeines -
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