## 1 Introduction
The proposed ANN model achieved a mean target value of 0.5409 and a correlation coefficient (\(R^{2}\)) of 0.939, and outperformed the models by [PERSON] (\(R^{2}\) = 0.929) and [PERSON] (\(R^{2}\) = 0.892). The predicted \(C_{c}\) values from the ANN model demonstrated a better distribution around the trend line and highlighted its superior accuracy and strong agreement with laboratory results. Also, in another study [15], artificial neural network (ANN) methodologies have been proposed as efficient alternatives to traditional 15-day consolidation tests for predicting the compression index (\(C_{c}\)) in fine-grained soils. Another study trained an ANN using a dataset of 560 high- and low-plasticity soil samples from Turkey, with input parameters such as the natural water content, the LL, the PL, the PI, and the initial void ratio. Using Matlab 2023a's regression learner program, the model achieved an \(R^{2}\) of 0.81, demonstrating its ability to provide reliable \(C_{c}\) predictions with fewer experiments and significantly shorter timeframes, making it a valuable tool for geotechnical engineering. Genetic Programming (GP) is becoming increasingly popular in geotechnical engineering because of its flexibility and ability to model non-linear relationships. It has been successfully used to predict soil properties such as shear strength, permeability, and bearing capacity [16; 17; 18]. Research by [PERSON] et al. [19] and [PERSON] et al. [20] shows that GP can accurately model soil parameters, especially when traditional methods fall short. Its strength lies in its evolutionary approach where solutions adapt without needing predefined formulas. It makes this method ideal for handling complex, non-linear geotechnical data [21; 22; 23]. XGBoost, as a powerful gradient-boosting technique, has gained recognition for its efficiency and accuracy in analyzing large, high-dimensional datasets. It has been widely used in civil and geotechnical engineering to predict properties like soil compaction and undrained shear strength [24; 25]. Studies by [PERSON] and [PERSON] [26] and [PERSON] et al. [27] demonstrated that XGBoost often outperforms traditional machine learning models due to its robust regularization features and resistance to overfitting. Additionally, its interpretability, through feature importance metrics, provides valuable insights into how soil properties influence \(C_{c}\)[28; 29; 30]. Combining GP and XGBoost into a hybrid model addresses the limitations of each standalone approach. This hybridization leverages GP's adaptability and XGBoost's computational power, leading to improved accuracy and reliability in predicting complex parameters like soil strength and stability under varying conditions [17; 31; 32]. Recent studies have shown that hybrid models significantly enhance performance and can accommodate the variability seen across different soil types and conditions, making them highly adaptable solutions for predicting \(C_{c}\)[33]. Comparative studies, such as those by [PERSON] et al. [34] and [PERSON] et al. [35], highlight that hybrid machine learning models consistently outperform traditional methods. By combining multiple techniques, hybrid models handle non-linear relationships more effectively and reduce prediction errors for parameters like \(C_{c}\)[36]. Despite these advancements, challenges remain in generalizing these models across various soil profiles and conditions. Further research is needed to validate their applicability in diverse geotechnical settings [29; 37]. Additionally, incorporating interpretability techniques, like SHAP (SHap-ley Additive exPlanations), could further clarify the relationship between soil properties and \(C_{c}\), improving their acceptance in practice [38; 39; 40]. This paper explores the use of GP, XGBoost, and their hybrid model to predict \(C_{c}\) using a comprehensive geotechnical database, and demonstrates their potential in advancing soil behavior prediction.