# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
###### Abstract
have predicted the swelling pressure using an \(ANN\) model with an input layer containing W, dry density (\(Y_{d}\)), \(WL\), \(PI\), and clay fraction [22]. [PERSON] and [PERSON] have developed an \(ANN\) model with one hidden layer to predict \(C_{s}\) and the \(C_{c}\) of clay by learning from 68 samples. They used the \(FC\), \(WL\), \(PI\), maximum dry density, and optimum moisture content as an input layer. The suggested \(ANN\) model (5-8-1) provided better predictability in comparison with the multiple regression analysis (\(MRA\)) model [23]. [PERSON] et al. have used \(ANN\) models to estimate \(C_{s}\) and \(C_{c}\) from input layer including the \(W\), \(e_{0}\), \(WL\), and \(PI\). The proposed \(ANN\) model (4-6-2) has proven its efficiency in the prediction of \(C_{c}\). Nevertheless, the predicted \(C_{s}\) values were not satisfactorily compared to the compression index [24]. Table 2 recapitulates the aforementioned proposed \(ANN\) models in the literature to estimate the swelling index \(C_{s}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Correlations** & **Comments** & **References** \\ \hline \(C_{s}(W_{L},~{}G_{s})\) & \(C_{s}=0.0463\left(\frac{W_{L}}{100}\right)G_{s}\) & (1) & fine-grained soils & [18] \\ \(C_{s}(I_{P})\) & \(C_{s}=0.00194(I_{P}-4.6)\) & (2) & fine-grained soils & [19] \\ \(C_{s}(W_{n})\) & \(C_{s}=0.0133e^{0.036W_{n}}\) & (3) & fine-grained soils & Isik 1 [20] \\ \(C_{s}(e_{0})\) & \(C_{s}=0.0121e^{1.3131e_{0}}\) & (4) & fine-grained soils & Isik 2 [20] \\ \(C_{s}(Y_{h})\) & \(C_{s}=0.1257Y_{h}^{-2.8826}\) & (5) & fine grained soils & Isik 3 [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlations proposed in the literature to estimate the swelling index \(C_{s}\). The quality, learning ability, and effectiveness have made the use of the _ANN_ method very useful. According to the author's knowledge, the previous studies have used only the _ANN_ method for estimating \(C_{s}\), although recent studies have showed that other techniques could have yielded more effective and accurate results than the _ANN_ method in geotechnical applications [25; 26; 27]. Furthermore, the aforementioned studies have modeled \(C_{s}\) using a few input parameters and, therefore, ignored the different soil parameters that could increase the learning capacity of the network. Consequently, the complicated mechanism of the swelling phenomena has been oversimplified. Moreover, few samples have been used, meaning that the proposed models have a limited capacity to generalize new data not used in the few training data. Furthermore, they evaluated the predictive capacity of proposed models based on only one split to validate data learning. Therefore, the capacity of their model to overcome the over-fitting and under-fitting problems cannot be confirmed. In view of the aforementioned shortcoming, this research sheds the light on the capability of the advanced machine learning methods to generate a reliable model, contributing to easily and effectively predict \(C_{s}\), filling a gap in the literature where there is a lack in the use of the advanced machine learning methods in modeling swelling phenomenal. Consequently, the elaborated model offers plenty of benefits such as its reliability, and lowering the budget used to predict \(C_{s}\) from the easily obtained soil parameters and without the need to operate the odometer test.