A Machine Learning Architecture Replacing Heavy Instrumented Laboratory Tests: In Application to the Pullout Capacity of Geosynthetic Reinforced Soils.

Ali, Tabish; Haider, Waseem; Ali, Nazakat; Aslam, Muhammad

Ali, Tabish; Haider, Waseem; Ali, Nazakat; Aslam, Muhammad.

Afiliación

Ali T; Department of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, Suwon 16419, Korea.
Haider W; Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea.
Ali N; School of Innovation, Design and Engineering, Malardalen University, 722 20 Vasteras, Sweden.
Aslam M; Department of Artificial Intelligence, Sejong University, Seoul 05006, Korea.

Sensors (Basel) ; 22(22)2022 Nov 10.

Article en En | MEDLINE | ID: mdl-36433296

Asunto(s)

Aprendizaje Automático; Suelo; Teorema de Bayes; Redes Neurales de la Computación; Algoritmos

Palabras clave

ANN; Bayesian regularization; geosynthetic reinforced soil; machine learning; pullout capacity; weathered granite soil

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Suelo / Aprendizaje Automático Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article Pais de publicación: Suiza

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