Simulation using artificial neural networks in geotechnical engineering - Tapa blanda

Benali, Amal

9786206065111: Simulation using artificial neural networks in geotechnical engineering

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ISBN 10: 6206065111 ISBN 13: 9786206065111

Editorial: Our Knowledge Publishing, 2023

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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.

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EditorialOur Knowledge Publishing
A�o de publicaci�n2023
ISBN 10 6206065111
ISBN 13 9786206065111
Encuadernaci�nTapa blanda
IdiomaIngl�s
N�mero de p�ginas52
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Simulation using artificial neural networks in geotechnical engineering

Benali, Amal

Publicado por Our Knowledge Publishing, 2023

ISBN 10: 6206065111 ISBN 13: 9786206065111

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Simulation using artificial neural networks in geotechnical engineering

Benali, Amal

Publicado por Our Knowledge Publishing, 2023

ISBN 10: 6206065111 ISBN 13: 9786206065111

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Simulation using artificial neural networks in geotechnical engineering

Benali, Amal

Publicado por Our Knowledge Publishing, 2023

ISBN 10: 6206065111 ISBN 13: 9786206065111

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Simulation using artificial neural networks in geotechnical engineering

Benali, Amal

Publicado por Our Knowledge Publishing, 2023

ISBN 10: 6206065111 ISBN 13: 9786206065111

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Simulation using artificial neural networks in geotechnical engineering

Amal Benali

Publicado por Our Knowledge Publishing Jun 2023, 2023

ISBN 10: 6206065111 ISBN 13: 9786206065111

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Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9786206065111

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Simulation using artificial neural networks in geotechnical engineering

Amal Benali

Publicado por Our Knowledge Publishing, 2023

ISBN 10: 6206065111 ISBN 13: 9786206065111

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Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9786206065111

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