Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters

Abstract

Laboratory tests for the estimation of the compaction parameters, namely the maximum dry density (MDD) and optimum moisture content (OMC) are time-consuming and costly. Thus, this paper employs the artificial neural network technique for the prediction of the OMC and MDD for the aggregate base course from relatively easier index properties tests. The grain size distribution, plastic limit, and liquid limits are used as the inputs for the development of the ANNs. In this study, multiple ANNs (240 ANNs) are tested to choose the optimum ANN that produces the best predictions. This paper focuses on studying the impact of three different activation functions: number of hidden layers, number of neurons per hidden layer on the predictions, and heatmaps are generated to compare the performance of every ANN with different settings. Results show that the optimum ANN hyperparameters change depending on the predicted parameter. Additionally, the hyperbolic tangent activation is the most efficient activation function as it outperforms the other two activation functions. Additionally, the simplest ANN architectures results in the best predictions, as the performance of the ANNs deteriorates with the increase in the number of hidden layers or the number of neurons per hidden layers.

Prediction of Soil Compaction Parameters Using Machine Learning Models

“…Khuntia et al [12] used Artificial Neural Network (ANN), Least Squares Support Vector Machine (LS-SVM), and Multiple Adaptive Regression Spline Curve (MARS) to develop a prediction model for the compaction parameters of sandy soils, and the MARS model was more accurate. Othman [26] constructed a prediction model for aggregate base materials by ANN trained with different numbers of hidden layers, different numbers of hidden layer neurons, and three activation functions. The results showed that the hyperbolic tangent function (Tanh) is the most effective activation function and the performance of the ANN deteriorates with an increase in the number of hidden layers or the number of neurons per hidden layer.…”