## 1 Introduction  
Demand for land development to support infrastructure continues to rise. Given this, the improvement and reclamation of soft ground has been actively undertaken across the globe including in the coastal areas of South Korea [1, 2, 3]. Soft ground refers to soil with low shear strength and high compressibility [4], where clay and silt make up a significant portion. The behavior of salts and clays is dependent on the applied loading conditions, making it difficult to predict their behavior [5]. This is particularly the case with clay. The microstructure and swelling potential of clay can vary significantly depending on the soil's water content and the type and proportion of clay minerals it contains [6, 7]. This variation challenges our understanding of the behavior of soft ground, increasing the likelihood of failures and ultimately causing extensive damage to infrastructure [8]. Factors, such as the inaccurate determination of design parameters during soft ground improvement,frequently give rise to soil consolidation and differential settlement, which in turn lead to structural failures [9]. Therefore, a precise estimation of consolidation settlement is essential, if the structural safety and sustainability of structures built on soft ground is to be assured [10]. To accurately predict consolidation settlement, the consolidation characteristics of the soil must be carefully considered during the construction phase. This makes a precise determination of the compression index of vital importance. The compression index (C\({}_{c}\)), defined as the slope of the linear portion of the void ratio-pressure curve obtained from consolidation tests, is a critical soil parameter for estimating the primary consolidation settlement of soft ground. Clay with a high compression index undergoes a greater reduction in void ratio under the same stress change compared with clay with a low compression index. This greater reduction results in larger consolidation settlement. Consequently, the compression index plays a crucial role in understanding the consolidation characteristics of soft ground. However, consolidation tests conducted to determine the compression index are beset with several challenges. These include difficulties in preserving undisturbed soil samples during sampling and transportation, the time-consuming nature of the testing process, and the reliance on the technician's expertise during the analysis of results [11; 12]. Therefore, studies have long been conducted to simplify the estimation of the compression index by utilizing correlations between soil properties and the compression index. Previously, correlation equations based on linear relationships between single soil parameters, such as natural water content, initial void ratio, liquid limit, and shear wave velocity, and the compression index have been proposed [13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Moreover, studies have been conducted that not only utilize a single soil parameter, but also identify correlations between multiple geotechnical parameters and the compression index [13; 15; 16; 17; 22; 23; 24; 25]. While these traditional empirical equations allowed for a rapid estimation of the approximate compression index values, they had limitations when it came to explaining the complex relationships between the compression index and various independent variables. Consequently, to overcome these limitations, various methods employing machine learning (ML) techniques and soft computing methods that offer solutions to complex real-world problems [26] have been increasingly applied in geotechnical engineering. Various soft computing and ML techniques, including XGBoost (XGB), artificial neural network (ANN), and genetic algorithm (GA), have been employed for the task of predicting the compression index [16; 17; 22; 27; 28; 29; 30; 31; 32]. [PERSON] and [PERSON] [17], [PERSON] and [PERSON] [30], [PERSON] et al. [31], [PERSON] and [PERSON] [16], and [PERSON] et al.