Prediction of soils penetration strength using artificial neural networks
Artificial Neural Networks simulate the learning process of biological neurons, and they have been successfully used in the computation of parameters on several engineering problems where a strong nonlinear relation among the variables exists. In soil science, estimation of some properties involves variables that are complicated to estimate using mathematical models, so the solution for the problems fall into the field of Artificial Intelligence. The present paper reports the elaboration of an Artificial Neural Network for the estimation of soil penetration resistance at different depths, considering as influential variables humidity, density, static load, and inflate pressure. The best estimation results were obtained at a depth of 20-30 cm.
| Main Authors: | , , |
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| Format: | Digital revista |
| Language: | spa eng |
| Published: |
Universidad Nacional de Colombia - Sede Palmira
2011
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| Online Access: | https://revistas.unal.edu.co/index.php/acta_agronomica/article/view/28784 |
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