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.
Highlights
Introduction and BackgroundFlexible pavement is the most common pavement used in Egypt
There is a general pattern that can be observed across the three activation functions
The present study focuses on developing ANNs models for estimating the compaction parameters of the aggregate base course used in constructing roads in Egypt using the aggregate gradation and Atterberg limits as the inputs to the ANNs
Summary
Introduction and BackgroundFlexible pavement is the most common pavement used in Egypt. It is known that the flexible pavement consists of different layers to transfer the traffic load to the soil. The wearing surface or the surface course is the layer in direct contact with traffic, and it provides characteristics such as friction, smoothness, noise control, rut resistance, and drainage. It prevents the entrance of surface water into the underlying base, subbase, and subgrade [4]. The main objective of the base course is to provide additional load distribution, and this layer is usually constructed out of crushed aggregate. The subbase layer consists of lower quality materials than the base course but better than the subgrade soils. The aggregate base course is typically installed and compacted to a minimum of 95 percent relative compaction, providing the stable foundation needed to support either additional layers of aggregates or the placement of the wearing course, which is applied directly on top of the base course
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