Idioma: Inglés
Publicado por VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Añadir al carritoCondición: New. pp. 348.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: Mispah books, Redhill, SURRE, Reino Unido
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Añadir al carritoPaperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Idioma: Inglés
Publicado por VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: Majestic Books, Hounslow, Reino Unido
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Añadir al carritoCondición: New. Print on Demand pp. 348 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.
Idioma: Inglés
Publicado por VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 90,42
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 348.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Feb 2010, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 79,00
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The objective of this book is to develop intelligent models [geostatistic, artificial neural network (ANN) and support vector machine(SVM)] to estimate corrected standard penetration test (SPT) value, Nc, in the three dimensional (3D) subsurface of Bangalore. The present book also highlights the capability of SVM over the developed geostatistic models (simple kriging, ordinary kriging and disjunctive kriging) and ANN models. Further in this book, liquefaction susceptibility is evaluated from Standard Penetration Test (SPT), Cone Penetration Test (CPT) and Shear Wave (Vs) data using ANN and SVM. In this book, an attempt has also been made to evaluate geotechnical site characterization by carrying out in situ tests using different in situ techniques such as CPT, SPT and multi channel analysis of surface wave (MASW) techniques. SVM model has been also adopted to determine over consolidation ratio (OCR) based on piezocone data. SVM model outperforms all the available methods for OCR prediction. 348 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: moluna, Greven, Alemania
EUR 63,42
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Samui PijushDr. Pijush Samui is an associate professor at Center for Disaster Mitigation and Management in Vellore Institute of Technology, Vellore, India. He has published 53 technical papers in journals and conferences. He is .
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: preigu, Osnabrück, Alemania
EUR 66,50
Cantidad disponible: 5 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. INTELLIGENT MODELS in geotechnical engineering | INTELLIGENT MODELS in geotechnical engineering | Pijush Samui (u. a.) | Taschenbuch | 348 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838348766 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Feb 2010, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 79,00
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The objective of this book is to develop intelligent models [geostatistic, artificial neural network (ANN) and support vector machine(SVM)] to estimate corrected standard penetration test (SPT) value, Nc, in the three dimensional (3D) subsurface of Bangalore. The present book also highlights the capability of SVM over the developed geostatistic models (simple kriging, ordinary kriging and disjunctive kriging) and ANN models. Further in this book, liquefaction susceptibility is evaluated from Standard Penetration Test (SPT), Cone Penetration Test (CPT) and Shear Wave (Vs) data using ANN and SVM. In this book, an attempt has also been made to evaluate geotechnical site characterization by carrying out in situ tests using different in situ techniques such as CPT, SPT and multi channel analysis of surface wave (MASW) techniques. SVM model has been also adopted to determine over consolidation ratio (OCR) based on piezocone data. SVM model outperforms all the available methods for OCR prediction.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 348 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838348761 ISBN 13: 9783838348766
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 79,95
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The objective of this book is to develop intelligent models [geostatistic, artificial neural network (ANN) and support vector machine(SVM)] to estimate corrected standard penetration test (SPT) value, Nc, in the three dimensional (3D) subsurface of Bangalore. The present book also highlights the capability of SVM over the developed geostatistic models (simple kriging, ordinary kriging and disjunctive kriging) and ANN models. Further in this book, liquefaction susceptibility is evaluated from Standard Penetration Test (SPT), Cone Penetration Test (CPT) and Shear Wave (Vs) data using ANN and SVM. In this book, an attempt has also been made to evaluate geotechnical site characterization by carrying out in situ tests using different in situ techniques such as CPT, SPT and multi channel analysis of surface wave (MASW) techniques. SVM model has been also adopted to determine over consolidation ratio (OCR) based on piezocone data. SVM model outperforms all the available methods for OCR prediction.