Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil

Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer fr...

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Veröffentlicht in:	Multiscale and Multidisciplinary Modeling, Experiments and Design Jg. 4; H. 4; S. 259 - 274
Hauptverfasser:	Onyelowe, Kennedy C., Iqbal, Mudassir, Jalal, Fazal E., Onyia, Michael E., Onuoha, Ifeanyichukwu C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2021
Schlagworte:	Characterization and Evaluation of Materials Engineering Mathematical Applications in the Physical Sciences Mechanical Engineering Numerical and Computational Physics Original Paper Simulation Solid Mechanics Bayesian algorithm Back-propagation algorithm Artificial neural network (ANN) Soft computing Sustainable construction materials Artificial intelligence Machine learning in geotechnics Levenberg–muarquardt algorithm Conjugate gradient algorithm
ISSN:	2520-8160, 2520-8179
Online-Zugang:	Volltext
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