## 1 Introduction  
Swelling pressure (p\({}_{\text{s}}\)) is a critical parameter in various fields, including geotechnical engineering [1]. It refers to the pressure exerted by a material when it expands upon absorbing a fluid, usually water. This phenomenon is particularly significant in the context of expansive soils, which can undergo substantial volume changes due to variations in moisture content. Expansive soils can pose significant economic costs due to their swelling and shrinking behaviours. These changes can lead to structural damage in buildings and infrastructure, resulting in a remarkably high cost, with the annual global expenditure on repairs estimated to reach several billion dollars [2, 3]. [PERSON] [4] indicated that the swelling behaviour is primarily influenced by the combined effect of several interacting factors, which can be categorized as: (a) local geology; (b) engineering properties; and (c) local environment of deposition. Geological factors encompass the rock type and age, which are related to the type and quantity of clay minerals, the type and amount of cementing material, and the arrangement of soil particles. Engineering factors include moisture content, Atterberg limits, and dry density. Environmental factors involve confining pressure, weathering type and degree, clay fraction content, initial water content, and water chemistry. While the swelling potential is influenced by geological and engineering factors, the amount and rate of swelling are governed by environmental conditions. Expansive soils are widely distributed throughout the world, although they are especially abundant in arid zones, where conditions are suitable for the formation of clayey minerals of the smectite group such as montmorillonite or some types of illites [5]. In geotechnical practice, the determination of \(\mathrm{p_{s}}\) is crucial for designing foundations and retaining structures, tunnels, and other geotechnical structures. Accurate predictions can facilitate the implementation of appropriate engineering measures to mitigate its effects and reduce costs in engineering projects [6; 7]. There are different techniques applicable in this type of soil to mitigate its potential volume changes, essentially involving the addition of a stabilizing material (e.g., refs. [8; 9; 10; 11]). Various laboratory tests, such as the oedometer test and the triaxial test, are commonly employed to directly measure \(\mathrm{p_{s}}\)[12]. However, there are numerous correlations available that relate \(\mathrm{p_{s}}\) to other easily obtainable geotechnical parameters [13]. Many researchers have extensively investigated the factors influencing soil swelling and have attempted to establish relationships between soil properties, including clay content, dry unit weight, water content, liquid limit, and plasticity index (e.g., refs. [4; 14; 15; 16; 17; 18; 19]). In the face of these challenges, the application of machine learning (ML) techniques can offer promising opportunities for advancing the understanding of clay swelling [20]. ML models can handle the complexity of the swelling process, making them powerful tools for predicting \(\mathrm{p_{s}}\) based on a range of input parameters (e.g., refs. [21; 22; 23]). [PERSON] et al. [24] provided a comprehensive review of the ML methods applied to obtain \(\mathrm{p_{s}}\). However, it is important to note that most traditional statistical correlations and existing ML algorithms are based on datasets of remoulded clay samples, where the soil structure is altered during both the extraction process and the compaction for testing. This reliance on remoulded samples may not accurately represent the behaviour of undisturbed soils, potentially limiting the applicability of these models to in situ soil conditions. Numerous studies (e.g., ref. [25]) have shown that correlations derived from remoulded samples can differ significantly from those based on undisturbed samples, highlighting the need for caution when applying such models to natural soil conditions.