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Synopsis
Seismic evaluation of existing facilities and design of earthquake-resistant structures requires estimation of strong ground motion parameters. Ground motion prediction equations are derived for estimating strong ground motion parameters. Traditionally, ground motion prediction equations are developed using statistical regression analyses. This book presents a very promising application of the powerful and versatile neural network approach to the derivation of ground motion prediction equations using Japanese earthquake records and site characteristics. Multi-layer perceptron neural network models with back-propagation learning scheme have been developed to predict strong ground motion parameters that are of primary significance in earthquake engineering using the actual seismic data without any simplification and assumptions. Statistics of the results presented indicate that artificial neural network is capable of learning and representing the local variations quite accurately. This book is intended to help structural engineers in selection of appropriate characterization of ground motion for engineering applications that are used in seismic analysis and design.
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About the Author
Arjun C.R., received his M.Tech degree in Earthquake Engineering with specialization in Structural Dynamics from IIT Roorkee. His areas of interest include strong ground motion studies, structural dynamics, earthquake-resistant design and design of machine foundations. He is a life member of Indian Society of Earthquake Technology (ISET).
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Neural Network-Based Estimation of Strong Ground Motion Parameters
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LAP Lambert Academic Publishing Jan 2013, 2013
ISBN 10: 3659275050
ISBN 13: 9783659275050
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Seismic evaluation of existing facilities and design of earthquake-resistant structures requires estimation of strong ground motion parameters. Ground motion prediction equations are derived for estimating strong ground motion parameters. Traditionally, ground motion prediction equations are developed using statistical regression analyses. This book presents a very promising application of the powerful and versatile neural network approach to the derivation of ground motion prediction equations using Japanese earthquake records and site characteristics. Multi-layer perceptron neural network models with back-propagation learning scheme have been developed to predict strong ground motion parameters that are of primary significance in earthquake engineering using the actual seismic data without any simplification and assumptions. Statistics of the results presented indicate that artificial neural network is capable of learning and representing the local variations quite accurately. This book is intended to help structural engineers in selection of appropriate characterization of ground motion for engineering applications that are used in seismic analysis and design. 88 pp. Englisch. Seller Inventory # 9783659275050
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Neural Network-Based Estimation of Strong Ground Motion Parameters
Published by
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659275050
ISBN 13: 9783659275050
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Seismic evaluation of existing facilities and design of earthquake-resistant structures requires estimation of strong ground motion parameters. Ground motion prediction equations are derived for estimating strong ground motion parameters. Traditionally, ground motion prediction equations are developed using statistical regression analyses. This book presents a very promising application of the powerful and versatile neural network approach to the derivation of ground motion prediction equations using Japanese earthquake records and site characteristics. Multi-layer perceptron neural network models with back-propagation learning scheme have been developed to predict strong ground motion parameters that are of primary significance in earthquake engineering using the actual seismic data without any simplification and assumptions. Statistics of the results presented indicate that artificial neural network is capable of learning and representing the local variations quite accurately. This book is intended to help structural engineers in selection of appropriate characterization of ground motion for engineering applications that are used in seismic analysis and design. Seller Inventory # 9783659275050
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Neural Network-Based Estimation of Strong Ground Motion Parameters
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LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659275050
ISBN 13: 9783659275050
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: C .R. ArjunArjun C.R., received his M.Tech degree in Earthquake Engineering with specialization in Structural Dynamics from IIT Roorkee. His areas of interest include strong ground motion studies, structural dynamics, earthquake-resi. Seller Inventory # 5144955
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Neural Network-Based Estimation of Strong Ground Motion Parameters
Published by
LAP LAMBERT Academic Publishing Jan 2013, 2013
ISBN 10: 3659275050
ISBN 13: 9783659275050
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Seismic evaluation of existing facilities and design of earthquake-resistant structures requires estimation of strong ground motion parameters. Ground motion prediction equations are derived for estimating strong ground motion parameters. Traditionally, ground motion prediction equations are developed using statistical regression analyses. This book presents a very promising application of the powerful and versatile neural network approach to the derivation of ground motion prediction equations using Japanese earthquake records and site characteristics. Multi-layer perceptron neural network models with back-propagation learning scheme have been developed to predict strong ground motion parameters that are of primary significance in earthquake engineering using the actual seismic data without any simplification and assumptions. Statistics of the results presented indicate that artificial neural network is capable of learning and representing the local variations quite accurately. This book is intended to help structural engineers in selection of appropriate characterization of ground motion for engineering applications that are used in seismic analysis and design.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 88 pp. Englisch. Seller Inventory # 9783659275050
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Neural Network-Based Estimation of Strong Ground Motion Parameters
Published by
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659275050
ISBN 13: 9783659275050
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Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Neural Network-Based Estimation of Strong Ground Motion Parameters | Arjun C . R. | Taschenbuch | 88 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659275050 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 106105854