Estimation of the compaction parameters of aggregate base course using artificial neural networks

Abstract

AbstractThe process of estimating the compaction parameters namely the maximum dry density (MDD) and optimum moisture content (OMC) through laboratory tests is time-consuming, labor-intensive, and costly. These issues can be avoided by developing prediction models that are able to accurately predict the compaction parameters from index properties that are easier to estimate in the laboratory. As a result, this study focuses on employing artificial neural networks (ANNs) for the prediction of the compaction parameters of aggregate base course samples from the grain size distribution and Atterberg limits. Additionally, different ANNs with different structures were tested in order to set the optimum hyperparameters that minimize the errors in the predictions. Specifically, this study investigates the impact of the number of hidden layers, number of neurons per hidden layer, and activation functions on the performance of the ANNs. Furthermore, the weight decay method, which is the most common regularization technique, was used during the training of the ANNs in order to avoid overfitting and control the changes in the connection weights. The results indicate that the optimum hyperparameter settings changes depending on the optimized output. Additionally, the ReLU activation function is the most stable function that produces the best predictions. Moreover, the results show that ANN approach represents a major innovative tool for accurately predicting the compaction parameters with R2 values of 0.826 and 0.911 for predicting the MDD and OMC.