# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 5 Conclusions  
The assessment of fine-grained soils settling under loads, such as buildings, vehicles, and infrastructure, heavily relies on the compression index (\(C_{c}\)). Calculating the \(C_{c}\) parameter is therefore crucial for determining the settlement in soil layers. However, using traditional methods, such as oedometer testing, to measure the value of \(C_{c}\) is time-consuming, requires specialized laboratory equipment, and needs expert staff. Predicting \(C_{c}\) values using additional fine-grained soil characteristics like \(w\), PL, LL, PI, \(\epsilon_{0}\), and \(G_{s}\) can save time and costs compared to oedometer testing. In this study, machine learning algorithms, a subfield of artificial intelligence, were employed to estimate the values of \(C_{c}\) using these parameters. A dataset of 915 samples containing these parameters was used to train and test the model. This approach aims to assist geotechnical researchers in calculating the compression index (\(C_{c}\)) more efficiently, thereby saving time and reducing costs. The compression index is crucial for soil consolidation computations in geotechnical engineering, especially for fine-grained soils. Various machine learning models have been used to predict this coefficient based on different soil properties, providing valuable insights into their relationships with the compression index. This study evaluated the effectiveness of machine learning techniques such as a random forest regressor, gradient boosting regressor, and AdaBoost regressor in predicting the compression index, highlighting the impact of the findings. The machine learning algorithms in this study were tested with different parameters and various combinations of input parameters to determine the best results. The random forest regressor model demonstrated a significant coefficient of determination (R\({}^{2}\)) of 0.970 during training and 0.926 during testing when it was trained with the inputs [\(w\), PL, LL, PI, \(e_{0}\), \(G_{s}\)], using parameters such as a maximum depth of 20, a maximum number of features of 1.0, and 100 estimators. The gradient boosting regressor model achieved R\({}^{2}\) values of 0.925 for training and 0.930 for testing when trained with the same inputs and parameters, specifically at a learning rate of 0.1 and with a squared error loss function. The AdaBoost regressor model achieved R\({}^{2}\) values of 0.847 for training and 0.921 for testing with the inputs [\(w\), PL, LL, PI, \(\epsilon_{0}\), \(G_{s}\)] and parameters including 200 estimators, a learning rate of 1.0, and a square loss function. These results indicate that the input parameters--\(w\), PL, LL, PI, \(e_{0}\), and \(G_{s}\)--are effective for successfully predicting the compression index (\(C_{c}\)). When compared with similar studies in the literature, this study achieved higher testing R\({}^{2}\) results. Traditional methods, such as empirical equations, do not typically report performance metrics like R\({}^{2}\) because they do not directly focus on the data fit or model optimization. As a result, R\({}^{2}\) values can only be calculated and compared for data-driven modeling techniques, such as machine learning algorithms. Machine learning algorithms reduce costs and shorten project durations by making fast and accurate predictions in geotechnical engineering. Compared to the traditional methods, these approaches provide reliable results in complex ground conditions, detect potential risks in advance, and enable safer designs. In this way, structural problems can be prevented in the long term. This study demonstrated that machine learning models offer valuable insights into understanding the relationships between soil properties and the compression index (\(C_{c}\)). However, it also highlights the risk of overfitting, particularly when there is a significant disparity between the training and testing data. This underscores the importance of exploring other machine learning models or input combinations to improve the compression index predictions.