Development predictive model of the mechanical properties of soil using artificial neural networks
DOI:
https://doi.org/10.21501/21454086.4042Keywords:
Redes neuronales, Perceptrones multicapa, Neuronas prealimentadas;, Propiedades del suelo, Humedad del suelo, Métodos de predicción, Modelos de predicción, Inteligencia artificial, Carreteras, Ingeniería civilAbstract
Determining soil properties is important in pavement design, for this reason, 4 artificial neural networks based on multilayer Perceptron were developed in this study to predict the maximum dry density (MDD), optimum moisture content (OMC), California bearing ratio (CBR) at 95 % MDD and CBR at 100 % MDD respectively. The method considers a dataset with 285 examples, definition of base architecture through Bayesian optimization and cross-validation, modification of the architecture and hyperparameters to improve performance. The models were trained considering 3000 epochs, RELU function, learning rate, dropout, and were evaluated using the Correlation Coefficient (R) and Mean Squared Error (MSE).
MDD was predicted with R=0,90, OMC with R=0,87, CBR at 95% with R=0,92, CBR at 100% with R=0,89 respectively,
showing that the models are efficient in predicting soil properties.
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