# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 1 Introduction  
Soil classification serves as a foundational component of geotechnical engineering, offering a structured system to organize and classify soils according to their physical and engineering attributes [1; 2; 3]. This systematic approach enables engineers and geologists to comprehend and communicate the properties and behaviors of various soil types, facilitating applications such as foundation design, construction planning, and environmental management [4; 5; 6; 7]. Classical soil classification methods include the unified soil classification system (USCS), the United States department of agriculture (USDA) soil taxonomy, the American association of state highway and transportation officials (AASHTO) classification system, the Canadian system of soil classification, and the British soil classification system [8; 9; 10; 11; 12; 13; 14]. The USCS is a widely adopted method for classifying soils based on their physical and mechanical properties. It is extensively utilized in geotechnical engineering to determine soil behavior for construction projects, assess soil suitability, and design foundations. The AASHTO soil classification system, on the other hand, is mainly used to evaluate soils for highway construction and other transportation infrastructure. It aids in assessing soil suitability for use as subgrade material, predicting performance under load, and guiding construction practices. The AASHTO system focuses on soil behavior related to highway performance, including stability and drainage. Both the USCS and AASHTO system offer valuable frameworks for soil classification, each addressing specificengineering needs. While these systems remain fundamental in soil classification, their effectiveness is significantly enhanced by modern computational techniques, which provide more accurate and efficient soil evaluations for various engineering applications. The USCS stands out as one of the most extensively utilized soil classification frameworks globally. It groups soils into distinct categories based on factors like particle sizes, mineral composition, plasticity, and other engineering characteristics [15; 16; 17]. By categorizing soils into classifications like sands, silts, clays, and their combinations, the USCS establishes a uniform terminology for describing soil traits and performance. Typically, the USCS classification process involves laboratory analyses and field evaluations, where soil samples are scrutinized for parameters like grain size distribution and Atterberg limits, which include plasticity index (PI), plastic limit (PL), and liquid limit (LL), along with other relevant attributes [18; 19; 20]. These findings are then compared against predefined classification standards to assign each soil sample to a specific group or category within the classification system. Traditional methods rely heavily on expert knowledge and manual processes, which are time-consuming and prone to human error [21; 22; 23]. Machine learning (ML) offers a promising alternative, providing automated and accurate classification based on soil data. Researchers are exploring advanced techniques, including ML algorithms like logistic regression (LR), support vector machine (SVM), random forest (RF), artificial neural networks (ANNs), and decision trees (DTs), to devise effective soil classification methods [24; 25; 26; 27; 28; 29]. A concrete example of using ML for soil classification is provided by [PERSON] et al. [25]. A new classification method for determining different soil classes, based on three ML approaches--support vector classification (SVC), multilayer perceptron (MLP), and RF models--has been proposed by [PERSON] et al. [27]. It was indicated by the results that, while all three models perform well, the SVC model is the most accurate in classifying soils. A fuzzy decision tree approach to soil classification is applied by [PERSON] et al. [28].