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Simulation Using Artificial Neural by Benali Amal (11 results)
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Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Condition: New.
-
Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Condition: New.
-
Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing Jun 2023, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -With the rapid technological and industrial development that the world has seen, particularly in construction technology with these huge oil platforms in the depths of the ocean or desert. This requires an adequate load-bearing structural system, capable of distributing forces from one level to another until they reach the foot of the structure, known as the foundation. The important role of deep foundations in transmitting service loads from the superstructure to the deep soil bearing layers has prompted the use of empirical and semi-empirical methods for the axial bearing capacity design of a pile. Alternatively, artificial neural networks (ANNs) have recently been used to predict the ultimate capacity of piles based on in situ tests. Very recently, several researchers have successfully used the RNAs artificial neural network approach for the development of integrated models in conjunction with other probabilistic and evolutionary methods.Books on Demand GmbH, Überseering 33, 22297 Hamburg 52 pp. Englisch.
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Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Taschenbuch. Condition: Neu. Simulation using artificial neural networks in geotechnical engineering | Amal Benali | Taschenbuch | Englisch | 2023 | Our Knowledge Publishing | EAN 9786206065111 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing Jun 2023, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With the rapid technological and industrial development that the world has seen, particularly in construction technology with these huge oil platforms in the depths of the ocean or desert. This requires an adequate load-bearing structural system, capable of distributing forces from one level to another until they reach the foot of the structure, known as the foundation. The important role of deep foundations in transmitting service loads from the superstructure to the deep soil bearing layers has prompted the use of empirical and semi-empirical methods for the axial bearing capacity design of a pile. Alternatively, artificial neural networks (ANNs) have recently been used to predict the ultimate capacity of piles based on in situ tests. Very recently, several researchers have successfully used the RNAs artificial neural network approach for the development of integrated models in conjunction with other probabilistic and evolutionary methods. 52 pp. Englisch.
-
Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Condition: New. Print on Demand.
-
Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Condition: New. PRINT ON DEMAND.
-
Simulation using artificial neural networks in geotechnical engineering
Published by Our Knowledge Publishing, 2023
ISBN 10: 6206065111 ISBN 13: 9786206065111
Language: English
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the rapid technological and industrial development that the world has seen, particularly in construction technology with these huge oil platforms in the depths of the ocean or desert. This requires an adequate load-bearing structural system, capable of distributing forces from one level to another until they reach the foot of the structure, known as the foundation. The important role of deep foundations in transmitting service loads from the superstructure to the deep soil bearing layers has prompted the use of empirical and semi-empirical methods for the axial bearing capacity design of a pile. Alternatively, artificial neural networks (ANNs) have recently been used to predict the ultimate capacity of piles based on in situ tests. Very recently, several researchers have successfully used the RNAs artificial neural network approach for the development of integrated models in conjunction with other probabilistic and evolutionary methods.
