Resumen

VALDES HOLGUIN, Nidia Johana; GONZALEZ SALCEDO, Luis Octavio  y  E. WILL, Adrián L. Prediction of the soils penetration strength using artificial neural networks. Acta Agron. [online]. 2011, vol.60, n.3, pp.252-262. ISSN 0120-2812.

Artificial Neural Networks simulate the learning process of biological neurons, and these have been successfully used in the computation of parameters on several engineering problems where exist a strong nonlinear relation among the variables. 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 penetration resistance of soil 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.

Palabras clave : Artificial intelligence; artificial neural networks; soils; soil compaction; soil penetration strength.

        · resumen en Español     · texto en Español     · Español ( pdf )