Artificial neural network for stress-strain behavior of sandy soils: Knowledge based verification

By M. Banimahd, S. Yasrobi, P. Woodward. 2005

113 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/e6a7700eb472296af94e9900667a9476e7b02533

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. N. Güneş
2. Yusuf Erzin

Citations in the corpus

• Predicting CBR Value of Stabilized Pond Ash with Lime and Lime Sludge Using ANN and MR Models (2018, unknown): “Artificial neural network (ANN) is an oversimplified simulation of the human brain [21] and is accepted as a reliable data modeling tool to capture and represent complex relationships between inputs and outputs [22].”
• The Prediction of Swell Percent and Swell Pressure by Using Neural Networks (2011, background): “Overfitting makes multi-layer perceptrons (MLPs) memorize training patterns in such a way that they cannot generalize well to new data [10].”