# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

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## Abstract

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## 1. Introduction

_{c}) and the basic soil properties [6,8,9,10,11]. However, only a few widely accepted empirical equations have been proposed in the literature to estimate the swelling index (C

_{s}) from physical soil parameters, such as the natural water content (W), the plasticity index (PI), the liquid limit (WL), the specific gravity, and others. Table 1 summarizes some proposed formulae for estimating C

_{s}. However, major shortcomings have been observed. The large amount of correlations published with respect to the same parameters point to an inherent variability of its usage. Therefore, the application of correlation analysis in other conditions or sites could yield incorrect results [12,13]. In addition, these approaches generally depend on simplified assumptions, such as a linear behavior or production heuristics, which make regression analysis methods less effective when they are used for simulating the complex heterogeneous behavior of soil [14,15,16,17].

_{0}) and W. The (2-8-1) ANN model (meaning two inputs, eight nodes in the hidden layer and one output) efficiently predicted C

_{s}[20]. Das et al. 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]. Kumar and Rani 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]. Kurnaz 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}.

_{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.

_{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.

## 2. Materials and Methods

#### 2.1. Overview of the Methodology

_{h}), the dry density (Y

_{d}), the degree of saturation (Sr), the plasticity index (PI), the water content (w), the void ratio (e

_{0}), the liquid limit (WL), sample depth (Z), and the fine contents (FC) have been used. Firstly, from an effective viewpoint, the suitable input variables and nonlinear components are of considerable importance for efficient prediction. Thus, the Principal Component Analysis (PSA), Gamma Test (GT) and Forward selection (FS) methods have been used to select the optimal set of input variables. Afterward, the advanced machine learning techniques have been applied for modeling optimal inputs, and their accuracy models were evaluated through numerous statistical indicators. To assess the predictive capability of the best model, the k-fold cross-validation approach based on 10 splits has been utilized. Finally, to answer the question “Which input variables have the most or less influence on C

_{s}through the proposed model?”, a sensitivity analysis has been carried out using the step-by-step selection method.

#### 2.2. Oedometer Test

_{c}, C

_{s}, and the coefficient of consolidation (C

_{v}) [20,24]. The compressibility properties are used to predict how the settlement and the swelling will be held. A number of parameters influencing the swelling behavior have been reported in the past, such as W, e, WL, PI, the type and amount of clay (FC), and others [8]. C

_{s}is usually determined using the graphical analysis of compression and recompression curves in void ratio effective stress (e = f(log(σ))) plots [20,28] (see Figure 1), and used typically to estimate the consolidation settlement for soil layers using these formulae:

_{0}: initial void ratio, Δσ

_{v}: load increment, σ’

_{c}: pre-consolidation pressure, σ’

_{v}

_{0}: initial vertical effective stress, C

_{c}: compression index, and C

_{s}: swelling index.

#### 2.3. Case Study

#### 2.4. Optimal Input Selections

#### 2.4.1. Overview of Principal Component Analysis (PCA)

#### 2.4.2. Overview of Gamma Test (GT)

_{i}, and ${y}_{N\left[i,K\right]}$ is the corresponding target. In order to calculate Γ, a least squares fit line should be carried out for the p points (δ(k), γ(k)). Afterward, the bias of the regression line will be easily estimated, which represents the gamma statistics parameter Γ. It is worth mentioning that Γ provides useful findings for building an accurate model. The smaller the value of Γ, the more appropriate is the input set. In addition, V

_{ratio}presents an important indication to assess the predictability of the selected target depending on utilized inputs, and illustrated as:

_{ratio}, the best-input combinations were chosen.

#### 2.4.3. Overview of Forward Selection (FS)

^{2}) is selected and added to the input set. In other words, if the R

^{2}is increased more than 5%, the new variable is accepted and added to the optimal input set. This step is repeated N − 1 times for assessing the impact of each parameter on modelling the target. Finally, among N tested inputs, the ones with optimum R

^{2}are accepted as the model input subset.

#### 2.5. Machine Learning Methods

#### 2.6. Statistical Performance Indicators

- Mean absolute error (MAE):$$MEA=\frac{1}{N}{{\displaystyle \sum}}_{i=1}^{N}\left|{Y}_{tar,i}-{Y}_{out,i}\right|(0MAE\infty )$$
- Root mean square error (RMSE):$$RMSE=\sqrt{\frac{1}{N}{{\displaystyle \sum}}_{i=1}^{N}{\left({Y}_{tar,i}-{Y}_{out,i}\right)}^{2}}(0RMSE\infty )$$
- Index of scattering (IOS):$$IOS=\frac{\sqrt{\frac{1}{N}{{\displaystyle \sum}}_{i=1}^{N}{\left({Y}_{tar,i}-{Y}_{out,i}\right)}^{2}}}{\overline{{Y}_{tar}}}(0RMSE\infty )$$
- Nash–Sutcliffe efficiency (NSE):$$NSE=1-\frac{{{\displaystyle \sum}}_{i=1}^{N}{\left({Y}_{tar,i}-{Y}_{out,i}\right)}^{2}}{{{\displaystyle \sum}}_{i=1}^{N}{\left({Y}_{tar,i}-\overline{{Y}_{tar}}\right)}^{2}}(-\infty NSE1)$$
- Pearson correlation coefficient (R):$$R=\frac{{{\displaystyle \sum}}_{i=1}^{N}(\left({Y}_{tar,i}-\overline{{Y}_{tar}}\right)\left({Y}_{out,i}-\overline{{Y}_{out}}\right))}{\sqrt{{{\displaystyle \sum}}_{i=1}^{N}({\left({Y}_{tar,i}-\overline{{Y}_{tar}}\right)}^{2}{\left({Y}_{out,i}-\overline{{Y}_{out}}\right)}^{2})}}\left(-1R1\right)$$
- Index of agreement (IOA):$$IOA=1-\frac{{{\displaystyle \sum}}_{i=1}^{N}{\left({Y}_{tar,i}-{Y}_{out,i}\right)}^{2}}{{{\displaystyle \sum}}_{i=1}^{N}{\left({{\displaystyle \sum}}_{i=1}^{N}\left|{Y}_{out,i}-\overline{{Y}_{tar}}\right|+{{\displaystyle \sum}}_{i=1}^{N}\left|{Y}_{tar,i}-\overline{{Y}_{tar}}\right|\right)}^{2}}(0IOA1)$$

#### 2.7. Methodology

_{s}of soil using previously mentioned geotechnical parameters as an input, the methodology consisted of the following steps:

- Creation of a geotechnical database of Algerian soil, collected from different laboratories around the geotechnical constructions projects in progress or completed before.
- Selecting the optimal input variables using Principal component analysis (OSA), Gamma Test (GT), and Forward selection (FS) has been used.
- Analyzing selected optimal inputs using several machine learning methods. The ELM, DNN, SVR, RF, LASSO, PLS, Ridge, KRidge, Stepwise, and PG methods have been used in this step for proposing 30 models.
- Determine the most appropriate model for predicting the C
_{s}value between the thirty proposed models using important statistical performance indicators as MAE, RMSE, IOS, NSE, R, and IOA. - Assessing the predictive capacity of the best model to overcome under-fitting and over-fitting problem by using the K-fold cross validation approach with K = 10.
- Doing a sensitivity analysis by utilizing the step-by-step method to know the most or less influenced input on C
_{s}through the proposed model.

_{s}is systematically described in Figure 3.

## 3. Results

#### 3.1. Database Compilation

_{s}equal to 0.044 [55].

#### 3.2. Correlation between C_{s} and Geotechnical Parameters

_{s}and soil properties, the SPSS software has used. The cross-correlation between C

_{s}and soil parameters is presented in Figure 4, which could provide a descriptive overview of the data distribution. The results indicate a positive relationship between C

_{s}and others parameters, except for Y

_{d}and Y

_{h}, which seem to have a negative relationship (see Figure 4); this indicates that an increase in these parameters tends to proportionally increase C

_{s}. The Spearman correlation coefficient R and its significance between C

_{s}and other geotechnical parameters are presented in Table 5. The results show that the significance is less than 0.05 except Z, meaning that the majority of correlations are statistically significant. On the other hand, according to Smith’ classification (1986), the C

_{s}is moderately correlated to the aforementioned soil parameters, except Sr and Z which are poorly correlated. The results indicate that these parameters could have a complex nonlinear correlation with C

_{s}. Moreover, in order to mathematically simulate the complex swellings phenomena, new advanced machine learning methods should be applied.

#### 3.3. Optimal Input Selection

#### 3.3.1. Optimal Input Selection Using Principal Component Analysis

#### 3.3.2. Optimal Input Selection Using the Gamma Test

_{h}, Y

_{d}, W, e

_{0}, FC, WL and PI). It was noticed from Table 7 that the first combination contains all nine inputs (dubbed an initial set). Similarly, the second one included eight input parameters (All-Sr), which means omitted Sr from the initial set; the fourth combination comprises all inputs except Y

_{h}, and so on for rest of the combinations as presented in Table 7. The results of GT analysis shown in Table 7 prove that the parameters W, FC, WL, and PI have an important effect on the target (C

_{s}). The four input parameters are chosen according to the maximum value of gamma statistics (Γ), and V

_{ratio}. Based on this finding, four new combinations were tested in Table 8 for the sake of determining the optimal input set. In this case, the best set was designated based on the minimum of Γ and V

_{ratio}. The outcomes of GT on four diverse combinations are illustrated in Table 8. The findings indicate that the WL, PI, FC, and W set had the lowest value of gamma statistics (Γ = 1.3524 × 10

^{−4}, V

_{ratio}= 0.3714, and Mask = 1111), and used as input variables for modelling C

_{s}according to the Gamma Test method.

#### 3.3.3. Optimal Input Selection Using Forward Selection

^{2})) is selected as a new input and gathered into the formerly selected parameters. This work is repeated several times until that appending other parameter to the input set does not improve the modeling performance. Hence, if the determination coefficient increases more than 5%, the novel parameter is selected. Finally, input parameters having the most heavy influence on the target are selected and others ones are rejected. Table 9 shows the results of the forward selection procedure of different input models. The findings indicate that WL, Yd, W, and PI are selected as inputs for modeling C

_{s}according to the forward selection procedure, and the other parameters are eliminated.

#### 3.4. Swelling Index Prediction through AI Models

_{s}modeled with several machine learning methods produced MAE (5.6 × 10

^{−3}to 12.4 × 10

^{−3}), RMSE (0.007 to 0.0154), IOS (0.165 to 0.355), NSE (−0.88 to 0.75), R (0.59 to 0.94), and IOA (0.69 to 0.95) in the training phase. Similarly, in the validation phase, we obtain MAE (10.6 × 10

^{−3}to 13.6 × 10

^{−3}), RMSE (0.013 to 0.017), IOS (0.298 to 0.363), NSE (−1.68 to 0.13), R (0.53 to 0.71), and IOA (0.69 to 0.82). Furthermore, the finding clearly indicates that the FS-RF (the optimal inputs of FS method trained by RF method) presents the most appropriate model that gives the highest accuracy in terms of MAE (5.6 × 10

^{−3}/10.6 × 10

^{−3}), RMSE (0.007/0.013), IOS (0.165/0.298), NSE (0.75/0.13), R (0.94/0.71), and IOA (0.95/0.82) during the training/validation phase. In addition, the most appropriate FS-RF model clearly follows the criteria of minimum values of error metrics (MEA, RMSE and IOS) and higher values of NSE, R, and IOA for the phase of training and validation. Furthermore, this model is closely followed by the FS-DNN model, which gives an acceptable accuracy and ranked second. Moreover, the results reveal the poor performance of the PSO-Step model in predicting the swelling index. With respect to the performance of machine learning models, during the training phase, the hierarchy follows the order of FS-RF, GT-RF, PRO-RF, FS-DNN, GT-DNN, PSO-DNN, PSO-SVR, FS-GP, GT-GP, PSO-GP, GT-SVR, GT-Step, FS-SVR, GT-Lasso, GT-PLS, FS-PLS, GT-Kridge, FS-Kridge, PSO-LS, GT-LS, PSO-ELM, FS-ELM, PSO-Lasso, PSO-Kridge, PSO-Ridge, GT-Ridge, FS-Ridge, FS-LS, FS-Step, FS-Lasso, PSO-PLS, GT-ELM, and PSO-Step. Finally, the scatter plots between target and output swelling index values of each model are presented in Appendix A.

#### 3.5. Evaluating the Best Fitted Model Using the K-fold Cross Validation Approach

_{s}[20,22,23,24] have evaluated the predictive capacity of their proposed models depending on a single split. Subsequently, the capacity of their models in overcoming the over-fitting and under-fitting problems could not be ascertained. Figure 6 shows the performance measures of the best FS-RF models using 10-fold cross validation depending on training and validation data for each split. The findings prove the performance of the proposed model. The fact that R ranges between 0.92 and 0.94 for training data, and between 0.62 and 0.75 for validation data in the 10 splits, indicates the predictive capacity of the most appropriate FS-RF model for learning data, generating new validation data, and overcoming the over-fitting or under-fitting problems.

#### 3.6. Comparison between the Proposed Models and Empirical Formulae

_{s}. These formulae were presented in Table 1. Table 11 illustrates the statistical results of ${C}_{s,estimated}/{C}_{s,measured}$ for aforementioned empirical equations in comparison with the proposed FS-RF and FS-DNN models. The mean and standard deviation of the ratio ${C}_{s,estimated}/{C}_{s,measured}$ could be useful evidence for evaluating the predictive capacity. The closer the mean value to one and the standard deviation to zero, the better is the model. The results show that the most appropriate FS-RF model is the one with the minimum standard deviation σ

_{FS-RF}= 0.25, in addition to being the closer mean value to 1 average(FS-RF) = 1.07. The other equations indicate a poor predictive capacity, yielding a mean value in the range of 0.45–1.7, and standard deviation value between 0.26–0.99. Equally, the box plot of ${C}_{s,estimated}/{C}_{s,measured}$ of aforementioned formulae is displayed in Figure 7. This graphical representation provides an overview of the dispersion and skewness of every model. The scattering of the most appropriate FS-RF model appears only to be slightly regular and close to one, and is described by a shorter box than the others. The large box distant from 1, increasing to five in certain models, shows little variation in predicting C

_{s}. Data characterized by circles and stars in the figure denote the extreme and extra-extreme value.

#### 3.7. Sensitivity Analysis

_{s}in the proposed model?”, a sensitivity analysis has been carried out using the step-by-step method [58]. In this approach, the normalized input neurons vary at a constant rate, one at a time, while the other variables are held constant. Different constant rates (0.3, 0.6 and 0.9) are selected in the current study. For every input, the percentage of change in the output, as a result of the change in the input, is recorded. The sensitivity of each input is computed based on Equation (18):

_{s}is significantly influenced by WL, and its sensibility ratio is between 32–38%. This parameter is closely followed by the PI, which gives a moderate sensitivity and is ranked second. In addition, W and Y

_{d}have little effect on C

_{s}.

## 4. Discussion

#### 4.1. Significance of the Findings and Cross-Validation of the Results

_{s}. Needless to say, C

_{s}is one of the most indispensable geotechnical parameters required to estimate the settlement and the swelling degree in the every site. Firstly, in order to identify the optimal input parameters, which have the ideal influence on C

_{s}three advanced methods have been used. PSO proposed three lower-dimensional parameters as an optimal input. GT indicated that WL, PI, FC, and W could formalize the optimal input set. However, FS showed that WL, Y

_{d}, W, and PI are the best ones. The reason behind the difference between the three approaches lies in the philosophy of each one in handling the data. Based on that, ten advanced machine learning methods (ELM, DNN, SVR, RF, LASSO, PLS, Ridge, KRidge, Stepwise, and GP) have applied for modeling the three selected optimal input set (PSO, GT, and FS). The findings clearly indicate that the optimal input is the one chosen by FS and trained by the RF method (FS-RF). The latter presents the most appropriate model, which gave the minimum values of error metrics (MEA, RMSE, and IOS) and higher values of NSE, R, and IOA compared to other models. Furthermore, the emerging model was evaluated by the K-fold cross validation approach and compared with other proposed formulae. The conclusion is that the FS-RF model could generate new data without over-fitting or under-fitting, and being more effective than the empirical formulae. The other most interesting aspect is the optimal input set related to the best FS-RF model (WL, Y

_{d}, W, PI). Interestingly, this also accords with several studies, which have showed that physical parameters indirectly affected the swelling phenomena [59]. It is known that the micro-scale features of swelling soils comprise the mineral composition of clay particles, their reaction with the water chemistry, and the cations attracted to the clay particle by electrical forces. These micro-scale factors influence macro-scale physical factors, such as density, plasticity, and water content to control the engineering comportment of soil [59]. Additionally, the last part consists of the sensitivity analysis, which gives an overview about the more influenced parameters on C

_{s}according to the proposed model. The findings indicate that WL and PI are respectively the most affected factors on C

_{s}, meaning that swelling phenomena are primarily influenced by plasticity parameters. In addition, water content and dry density have little effect on C

_{s}.

#### 4.2. Scientific Importance of the Findings and Novelty of the Research

_{s}without doing an Oedomter test. The performance of the estimation has been highly developed compared with other models and formulae proposed in the literature, which are based just on simple regression or neural networks. According to these data, we can infer that the Random Forest method, which is applied in this study for the first time for modeling swelling index, could yield more effective and accurate results than the DeepANN and ANN method in modelling geotechnical phenomena. These results provide further support for the hypothesis that macroscale physical factors, such as density, plasticity, and water content, are the parameters that affected the swelling phenomena. Moreover, the sensitivity analysis of the proposed model revealed the most influenced parameters between them for better understanding the complex behavior of the swelling. This investigation enhances our understanding that the plasticity of soil consisting of the WL and PI are the most affected factors on swelling phenomena. In addition to these conceptual advantages, for enhancing the training phase, a large number of samples (875 tests) and multiple input parameters have been used in this study. The sophisticated k-fold cross-validation approach was utilized to test the capability of the best model to overcome under-fitting and over-fitting problems.

#### 4.3. Limitations of the Study and Future Research Directions

_{s}is therefore suggested. We note, for example, Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA), bee colony algorithm (ABC), Bio-geography-Based Optimization (BBO), Whale Optimization Algorithm (WOA), Ant Colony Optimization (ACO), and Grey Wolf Optimizer (GWO). These algorithms have proved high-performance results combined with machine learning techniques leading to improving their learning and rapidly converging to the best solution. The application of these meta-heuristic algorithms combined with machine learning methods have shown very impressive results in the abroad fields [60,61,62].

## 5. Conclusions

_{s}) from easily obtained geotechnical physical parameters. To achieve our aim, several advanced machine learning methods were used for a practical analysis aimed at modeling the physical parameters including the wet density (Y

_{h}), the dry density (Y

_{d}), the degree of saturation (Sr), the plasticity index (PI), the water content (w), the void ratio (e), the liquid limit (WL), sample depth (Z), and the fine contents (FC). Firstly, principal component analysis (PCA), Gamma test (GT), and the forward selection (FS) approach are utilized to reduce the input variable numbers and choose the optimal ones. The results indicate the reduction of nine input variables to four (using FS and GT) and three (using PCA techniques). Afterward, the advanced machine learning techniques have applied for modeling the proposed optimal inputs and their accuracy models were evaluated through six statistical indicators (MAE, RMSE, IOS, NSE, R, and IOA). The comparison of results assessment between different proposed models revealed the superiority of the FS-RF model, which gives the highest accuracy in terms of MAE (5.6 × 10

^{−3}/10.6 × 10

^{−3}), RMSE (0.007/0.013), IOS (0.165/0.298), NSE (0.75/0.13), R (0.94/0.71), and IOA (0.95/0.82) during the training/validation phase. For assessing the predictive capacity of the proposed FS-RF model, the K-fold cross validation approach with K = 10 has been carried out. The results show that this model has a high correlation coefficient, ranging between 0.92 and 0.94 for training data, and 0.62 to 0.75 for validation data in the 10 splits, meaning that any over-fitting or under-fitting have been found. Three criteria were used to compare the performances of the most appropriate FS-RF model with the proposed formulas in the literature: the mean, the standard deviation, and the Box plot of the ratio ${C}_{s,estimated}/{C}_{s,measured}$. The findings indicate that the aforementioned FS-RF model is more effective than the empirical formulae. Finally, a sensitivity analysis was carried out in order to assess the impacts of the soil parameter inputs on the model performance. The results proved that WL has the most important effect on the prediction of C

_{s}. PI has a moderate influence and ranked second. In addition, W and Y

_{d}have little effect on C

_{s}.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

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**Figure 5.**The eigenvalue versus the number of factors (The black line represents the eigenvalues of each factor and the red one shows the eigenvalues equal to or greater than 1).

**Figure 7.**Box plot of the ratio ${C}_{s,estimated}/{C}_{s,measured}$ for some empirical formulas and the most appropriate model (Hollow and asterisks circles indicate respectively the extreme and extra-extreme value).

Variables | Correlations | Comments | References | |
---|---|---|---|---|

${C}_{s}$(${W}_{L},{G}_{s}$) | ${C}_{s}=0.0463\left(\frac{{W}_{L}}{100}\right){G}_{s}$ | (1) | fine-grained soils | [18] |

${C}_{s}$(${I}_{P}$) | ${C}_{s}=0.00194\left({I}_{P}-4.6\right)$ | (2) | fine-grained soils | [19] |

${C}_{s}$(${W}_{n}$) | ${C}_{s}=0.0133{e}^{0.036{W}_{n}}$ | (3) | fine-grained soils | Isik 1 [20] |

${C}_{s}$(${e}_{0}$) | ${C}_{s}=0.0121{e}^{1.3131{e}_{0}}$ | (4) | fine-grained soils | Isik 2 [20] |

${C}_{s}$(Y_{h}) | ${C}_{s}=0.1257{{{\rm Y}}_{h}}^{-2.8826}$ | (5) | fine grained soils | Isik 3 [20] |

Authors | Inputs | Targets | Architecture (Inputs–Nodes–Outputs) | Database | References |
---|---|---|---|---|---|

Işık (2009) | e_{0} and W | C_{s} | 2-8-1 | 42 | [20] |

Das et al. (2010) | W, Y_{d}, WL, PI, and FC | C_{s} | 5-3-1 | 230 | [22] |

Kumar and Rani (2011) | FC, WL, PI, Y_{opt}, and W_{opt} | C_{s} and C_{c} | 5-8-2 | 68 | [23] |

Kurnaz et al. (2016) | W, e_{0}, WL, and PI | C_{s} and C_{c} | 4-6-2 | 246 | [24] |

Algorithms | Algorithm Parameters | Value |
---|---|---|

ELM | Hidden layers | H = 1 |

hidden neurons | N = 12 | |

activation function | ‘linear’ | |

regulation parameter | C = 0.02 | |

DNN | Hidden layers | H = 2 |

hidden neurons in the first layer | N1 = (1–20) | |

hidden neurons in the second layer | N2 = (1–20) | |

activation function in the first layer | ‘Tansg’ | |

activation function in the second layer | ‘Tansg’ | |

SVR | regulation parameter C | Series of C |

regulation parameter lambda | Series of lambda | |

kernel function | ‘rbf’ | |

RF | nTrees | nTrees = 100 |

mTrees | mTrees = 26 | |

LASSO | lambda | series of lambda |

PLS | PLS components | NumComp = 3 for PSO NumComp = 4 for GT and FS |

Ridge | regularization parameter lambda | lambda = 1 |

KRidge | regularization parameter lambda | lambda = 1 |

kernel function | ‘linear’ | |

parameter for kernel | sigma = 2 × 10^{−7} | |

PG | Function set | +, −, ×, ÷, power, ln, sqrt, sin, cos, tan |

Population size | 100 up to 500 | |

Number of generations | 1000 | |

Genetic operators | Reproduction, crossover, mutation |

Sr | Y_{h} | Y_{d} | W | e_{0} | FC | W_{L} | PI | C_{s} | ||
---|---|---|---|---|---|---|---|---|---|---|

N | Valid | 875 | 875 | 875 | 875 | 875 | 875 | 875 | 875 | 875 |

Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Mean | 89.45 | 2.01 | 1.67 | 20.61 | 0.63 | 86.55 | 50.11 | 26.09 | 0.0443 | |

Std. Error of Mean | 0.397 | 0.003 | 0.004 | 0.164 | 0.005 | 0.572 | 0.338 | 0.233 | 0.00072 | |

Median | 94.00 | 2.01 | 1.67 | 20.00 | 0.62 | 94.00 | 50.00 | 26.00 | 0.0399 | |

Mode | 100.00 | 2.04 | 1.69 | 20.00 | 0.61 | 98.00 | 58.00 | 29.00 | 0.04 | |

Std. Deviation | 11.77 | 0.09 | 0.13 | 4.86 | 0.13 | 16.92 | 10.00 | 6.89 | 0.01910 | |

Variance | 138.54 | 0.01 | 0.02 | 23.64 | 0.02 | 286.23 | 100.09 | 47.43 | 0.000 | |

Skewness | −1.32 | 0.10 | 0.29 | 0.36 | 0.21 | −1.75 | −0.08 | −0.12 | 0.686 | |

Std. Error of Skewness | 0.083 | 0.083 | 0.083 | 0.083 | 0.083 | 0.083 | 0.083 | 0.083 | 0.092 | |

Kurtosis | 1.09 | −0.20 | −0.09 | −0.08 | −0.29 | 2.41 | −0.28 | −0.43 | 0.073 | |

Std. Error of Kurtosis | 0.165 | 0.165 | 0.165 | 0.165 | 0.165 | 0.165 | 0.165 | 0.165 | 0.183 | |

Range | 64.45 | 0.57 | 0.73 | 26.00 | 0.79 | 78.00 | 64.31 | 38.00 | 0.10 | |

Minimum | 41.00 | 1.70 | 1.34 | 8.00 | 0.23 | 22.00 | 19.00 | 7.00 | 0.01 | |

Maximum | 100.00 | 2.27 | 2.07 | 34.00 | 1.02 | 100.00 | 83.31 | 45.00 | 0.11 | |

Percentiles | 25 | 84 | 1.95 | 1.58 | 17.10 | 0.53 | 81.82 | 42.81 | 21.50 | 0.03 |

50 | 94 | 2.01 | 1.67 | 20.00 | 0.62 | 94.00 | 50.00 | 26.00 | 0.041 | |

75 | 99 | 2.075 | 1.75 | 23.85 | 0.71 | 98.00 | 58.00 | 31.38 | 0.057 |

**Table 5.**Matrix of correlation between the geotechnical parameters (* Correlation significant at α = 0.05; ** Correlation significant at α = 0.01).

Sr | Z | Y_{h} | Y_{d} | W | e_{0} | FC | WL | PI | C_{s} | ||
---|---|---|---|---|---|---|---|---|---|---|---|

Sr | R | 1 | 0.199 ** | 0.170 ** | −0.197 ** | 0.582 ** | −0.06 | 0.194 ** | 0.082 * | −0.01 | 0.138 ** |

Sig. (2-tailed) | 0 | 0 | 0 | 0 | 0.06 | 0 | 0.02 | 0.78 | 0 | ||

Z | R | 0.199 ** | 1 | 0.281 ** | 0.164 ** | 0.03 | 0.02 | 0.127 ** | 0 | −0.06 | 0.02 |

Sig. (2-tailed) | 0 | 0 | 0 | 0.33 | 0.54 | 0 | 1 | 0.07 | 0.58 | ||

Y_{h} | R | 0.170 ** | 0.281 ** | 1 | 0.877 ** | −0.481 ** | −0.579 ** | −0.317 ** | −0.275 ** | −0.267 ** | −0.230 ** |

Sig. (2-tailed) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||

Y_{d} | R | −0.197 ** | 0.164 ** | 0.877 ** | 1 | −0.803 ** | −0.659 ** | −0.384 ** | −0.348 ** | −0.292 ** | −0.324 ** |

Sig. (2-tailed) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||

W | R | 0.582 ** | 0.03 | −0.481 ** | −0.803 ** | 1 | 0.633 ** | 0.385 ** | 0.372 ** | 0.264 ** | 0.349 ** |

Sig. (2-tailed) | 0 | 0.33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||

e_{0} | R | −0.06 | 0.02 | −0.579 ** | −0.659 ** | 0.633 ** | 1 | 0.227 ** | 0.321 ** | 0.260 ** | 0.216 ** |

Sig. (2-tailed) | 0.06 | 0.54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||

FC | R | 0.194 ** | 0.127 ** | −0.317 ** | −0.384 ** | 0.385 ** | 0.227 ** | 1 | 0.429 ** | 0.412 ** | 0.387 ** |

Sig. (2-tailed) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||

WL | R | 0.082 * | 0 | −0.275 ** | −0.348 ** | 0.372 ** | 0.321 ** | 0.429 ** | 1 | 0.914 ** | 0.553 ** |

Sig. (2-tailed) | 0.02 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||

PI | R | −0.01 | −0.06 | −0.267 ** | −0.292 ** | 0.264 ** | 0.260 ** | 0.412 ** | 0.914 ** | 1 | 0.512 ** |

Sig. (2-tailed) | 0.78 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||

C_{s} | R | 0.138 ** | 0.02 | −0.230 ** | −0.324 ** | 0.349 ** | 0.216 ** | 0.387 ** | 0.553 ** | 0.552 ** | 1 |

Sig. (2-tailed) | 0 | 0.58 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

Number | Eigenvalue | % Variance | % Cumulative Variance |
---|---|---|---|

1 | 3.81 | 42.34 | 42.34 |

2 | 1.61 | 17.85 | 60.19 |

3 | 1.48 | 16.44 | 76.63 |

4 | 0.92 | 10.21 | 86.84 |

5 | 0.65 | 7.23 | 94.07 |

6 | 0.42 | 4.64 | 98.71 |

7 | 0.08 | 0.91 | 99.62 |

8 | 0.03 | 0.29 | 99.91 |

9 | 0.01 | 0.09 | 100.00 |

Input Parameters | Gamma Test Statistics | ||
---|---|---|---|

Γ | V_{ratio} | Mask | |

All | 0.00014759 | 0.4054 | 111111111 |

All-Sr | 0.00014653 | 0.4025 | 011111111 |

All-Z | 0.00015130 | 0.4156 | 101111111 |

All-Y_{h} | 0.00014672 | 0.4030 | 110111111 |

All-Y_{d} | 0.00014689 | 0.4034 | 111011111 |

All-W | 0.00017471 | 0.4798 | 111101111 |

All-e_{0} | 0.00014712 | 0.4041 | 111110111 |

All-FC | 0.00019292 | 0.5299 | 111111011 |

All-WL | 0.00017584 | 0.4829 | 111111101 |

All-PI | 0.00016223 | 0.4456 | 111111110 |

Input Parameters | Gamma Test Statistics | ||
---|---|---|---|

Γ | V_{ratio} | Mask | |

WL | 0.00020688 | 0.5944 | 1000 |

WL, PI | 0.00018845 | 0.5176 | 1100 |

WL, PI, FC | 0.00017979 | 0.4938 | 1110 |

WL, PI, FC, W | 0.00013524 | 0.3714 | 1111 |

Input Subset | ANN Architecture | R^{2} | Decision |
---|---|---|---|

WL | 1-2-1 | 0.327 | WL selected |

WL, Sr | 2-4-1 | 0.332 | Sr rejected |

WL, Z | 2-4-1 | 0.328 | Z rejected |

WL, Y_{d} | 2-4-1 | 0.38 | Y_{d} selected |

WL, Y_{d}, Y_{h} | 3-6-1 | 0.41 | Y_{h} rejected |

WL, Y_{d}, W | 3-6-1 | 0.444 | W selected |

WL, Y_{d}, W, e_{0} | 4-8-1 | 0.47 | e_{0} rejected |

WL, Y_{d}, W, FC | 4-8-1 | 0.46 | FC rejected |

WL, Y_{d}, W, PI | 4-8-1 | 0.498 | PI selected. |

PSO | GT | FS | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

MAE × 10^{−3} | RMSE | IOS | NSE | R | IOA | MAE × 10^{−3} | RMSE | IOS | NSE | R | IOA | MAE × 10^{−3} | RMSE | IOS | NSE | R | IOA | |

Training | ||||||||||||||||||

DNN | 9.5 | 0.013 | 0.283 | 0.56 | 0.75 | 0.85 | 8.3 | 0.0113 | 0.251 | 0.64 | 0.80 | 0.88 | 8.4 | 0.011 | 0.245 | 0.67 | 0.82 | 0.89 |

ELM | 12 | 0.015 | 0.355 | −0.67 | 0.61 | 0.72 | 12 | 0.0153 | 0.340 | −1.33 | 0.61 | 0.69 | 12,2 | 0.015 | 0.34 | −0.88 | 0.61 | 0.72 |

Lasso | 12.2 | 0.0154 | 0.344 | −0.76 | 0.60 | 0.72 | 12.1 | 0.0151 | 0.335 | −0.65 | 0.61 | 0.73 | 12.1 | 0.015 | 0.34 | −0.84 | 0.59 | 0.71 |

PLS | 11.9 | 0.015 | 0.338 | −0.81 | 0.6 | 0.71 | 12.1 | 0.0152 | 0.339 | −0.66 | 0.61 | 0.73 | 12 | 0.015 | 0.34 | −0.69 | 0.61 | 0.73 |

RF | 5.8 | 0.0075 | 0.168 | 0.72 | 0.94 | 0.95 | 5.7 | 0.0075 | 0.167 | 0.72 | 0.94 | 0.95 | 5.6 | 0.007 | 0.165 | 0.75 | 0.94 | 0.95 |

Kridge | 12 | 0.015 | 0.342 | −0.73 | 0.61 | 0.72 | 12 | 0.015 | 0.343 | −0.71 | 0.61 | 0.73 | 12 | 0.015 | 0.334 | −0.67 | 0.61 | 0.73 |

Ridge | 12.2 | 0.015 | 0.341 | −0.77 | 0.60 | 0.72 | 11.9 | 0.015 | 0.337 | −0.71 | 0.61 | 0.72 | 11.9 | 0.015 | 0.343 | −0.74 | 0.60 | 0.72 |

LS | 12 | 0.0152 | 0.343 | −0.63 | 0.62 | 0.73 | 12.1 | 0.015 | 0.34 | −0.64 | 0.61 | 0.73 | 12 | 0.015 | 0.341 | −0.74 | 0.60 | 0.72 |

Step | 12.1 | 0.0153 | 0.346 | −0.86 | 0.59 | 0.71 | 11.9 | 0.015 | 0.33 | −0.61 | 0.62 | 0.74 | 12.4 | 0.015 | 0.343 | −0.76 | 0.60 | 0.72 |

SVR | 10.3 | 0.014 | 0.32 | 0.12 | 0.7 | 0.8 | 11.8 | 0.015 | 0.33 | −0.57 | 0.64 | 0.75 | 11.8 | 0.015 | 0.331 | −0.63 | 0.63 | 0.74 |

GP | 11.3 | 0.014 | 0.305 | −0.22 | 0.67 | 0.78 | 11.1 | 0.014 | 0.302 | 0.46 | 0.68 | 0.79 | 11 | 0.014 | 0.299 | 0.47 | 0.69 | 0.8 |

Validation | ||||||||||||||||||

DNN | 10.8 | 0.0135 | 0.304 | 0.47 | 0.69 | 0.82 | 11.2 | 0.0149 | 0.347 | 0.41 | 0.66 | 0.80 | 10,3 | 0.014 | 0.312 | 0.47 | 0.70 | 0.82 |

ELM | 11.4 | 0.014 | 0.346 | −0.53 | 0.6 | 0.73 | 12.5 | 0.015 | 0.35 | −1.68 | 0.64 | 0.69 | 11.7 | 0.015 | 0.331 | −0.93 | 0.62 | 0.72 |

Lasso | 11.5 | 0.014 | 0.318 | −0.65 | 0.64 | 0.74 | 12 | 0.015 | 0.325 | −0.55 | 0.64 | 0.75 | 11.6 | 0.015 | 0.346 | −0.85 | 0.67 | 0.74 |

PLS | 12.3 | 0.016 | 0.354 | −0.66 | 0.65 | 0.74 | 11.7 | 0.0146 | 0.312 | −0.59 | 0.64 | 0.74 | 12.2 | 0.015 | 0.339 | −0.59 | 0.61 | 0.73 |

RF | 11 | 0.0138 | 0.308 | −0.29 | 0.70 | 0.79 | 11.1 | 0.0143 | 0.32 | −0.17 | 0.70 | 0.8 | 10.6 | 0.013 | 0.298 | 0.13 | 0.71 | 0.82 |

Kridge | 12 | 0.0156 | 0.335 | −1.14 | 0.61 | 0.7 | 11.7 | 0.015 | 0.316 | −0.86 | 0.65 | 0.73 | 12.4 | 0.015 | 0.34 | −0.93 | 0.60 | 0.71 |

Ridge | 11.8 | 0.0144 | 0.322 | −0.37 | 0.63 | 0.753 | 12 | 0.015 | 0.34 | −0.75 | 0.66 | 0.74 | 12.1 | 0.014 | 0.333 | −0.79 | 0.63 | 0.73 |

LS | 12 | 0.015 | 0.334 | −0.49 | 0.57 | 0.72 | 12 | 0.015 | 0.33 | −0.49 | 0.62 | 0.75 | 12.2 | 0.015 | 0.33 | −0.55 | 0.63 | 0.75 |

Step | 11.8 | 0.0145 | 0.315 | −0.47 | 0.67 | 0.76 | 12.3 | 0.015 | 0.34 | −0.67 | 0.61 | 0.73 | 11 | 0.014 | 0.31 | −0.54 | 0.64 | 0.75 |

SVR | 12.1 | 0.016 | 0.34 | −0.76 | 0.53 | 0.69 | 12.6 | 0.015 | 0.34 | −0.51 | 0.59 | 0.71 | 12.6 | 0.015 | 0.344 | −0.49 | 0.58 | 0.71 |

GP | 12.8 | 0.016 | 0.36 | −0.95 | 0.53 | 0.62 | 13.6 | 0.0162 | 0.357 | −1.14 | 0.55 | 0.60 | 13.5 | 0.017 | 0.363 | −0.72 | 0.55 | 0.62 |

**Table 11.**Statistical results of the ratio ${C}_{s,estimated}/{C}_{s,measured}$ for some proposed empirical formulae.

Equations No. | Study | Average | Standard Deviation |
---|---|---|---|

FS-RF (in the current study) | 1.07 | 0.25 | |

FS-DNN (in the current study) | 1.08 | 0.34 | |

(2) | Cozzolino 1961 | 1.096 | 0.761 |

(1) | Nagaraj and Srinivasa 1986 | 1.695 | 0.989 |

(3) | Işık1 2009 | 0.81 | 0.497 |

(4) | Işık2 2009 | 0.766 | 0.461 |

(5) | Işık3 2009 | 0.446 | 0.259 |

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**MDPI and ACS Style**

Amin Benbouras, M.; Petrisor, A.-I. Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils. *Appl. Sci.* **2021**, *11*, 536.
https://doi.org/10.3390/app11020536

**AMA Style**

Amin Benbouras M, Petrisor A-I. Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils. *Applied Sciences*. 2021; 11(2):536.
https://doi.org/10.3390/app11020536

**Chicago/Turabian Style**

Amin Benbouras, Mohammed, and Alexandru-Ionut Petrisor. 2021. "Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils" *Applied Sciences* 11, no. 2: 536.
https://doi.org/10.3390/app11020536