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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Vydané v:	Multiscale and Multidisciplinary Modeling, Experiments and Design Ročník 4; číslo 4; s. 259 - 274
Hlavní autori:	Onyelowe, Kennedy C., Iqbal, Mudassir, Jalal, Fazal E., Onyia, Michael E., Onuoha, Ifeanyichukwu C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.12.2021
Predmet:	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
On-line prístup:	Získať plný text
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