## 1 Introduction  
For many years, the cone penetration test (CPT) has been the predominant method for conducting field exploration in geotechnical engineering [1; 2; 3; 4; 5]. This test requires a cone-shaped instrument to be inserted into the soil at a consistent penetration rate, while measuring the cone tip resistance (qc) and sleeve friction (fs). The CPT continuously provides precise, repeatable results for its entire profile depth. Moreover, the CPT is a relatively quick and inexpensive means of acquiring field data for estimating parameters for many applications, such as soil classification, environmental studies, hydrological analysis, and seismic site response assessments. Soil classification is essential in geotechnical engineering, especially when evaluating site response to seismic events. Accurate soil classification helps to understand the dynamic properties of soil and the effects of earthquakes on the soil's behavior. The traditional soil classification based on the CPT data involves analyzing 2D charts. Early research was aimed to predict the distribution of soil particles by using the CPT measurements, as outlined in the pioneering work of [PERSON] [6]. However, later work by [PERSON] and [PERSON] [7] suggested that a more useful soil classification approach in practical engineering projects would involve considering soil behavior, rather than solely relying on soil particle distribution. As a result, [PERSON] developed soil classification charts based on a soil behavior type index using CPT measurements [4; 8]. Additionally, there are alternative methods for soil classification. The Unified Soil Classification System (USCS), for example, relies on extensive field and laboratory tests to classify soil [9]. Soil classification and parameter estimation using traditional methods can be costly and time-consuming. Field and laboratory testing is required, and soil samples need to be transported to a laboratory where particle size distribution and Afterberg limits are conducted. These tests take time to complete, and the results may not be immediately available. Additionally, soil properties can significantly change with variations in the temperature and moisture content. However, in recent years, machine learning (ML) techniques have shown great promise in soil classification. Many studies have demonstrated the potential of ML techniques in soil classification based on CPT measurements [10; 11; 12; 13; 14; 15]. The study conducted by [15] explored the feasibility of utilizing a general regression neural network (GRNN) to predict soil composition and overall soil type employing CPT data. The research demonstrated that the GRNN model successfully categorized soils as either coarse-grained or fine-grained. Similarly, studies have demonstrated the effectiveness of artificial neural network (ANN) models in predicting complex soil profiles [16; 17; 18]. In addition, various machine learning techniques such as random forests (RFs) [19], support vector machines (SVMs) [20; 21], decision trees (DTs) [22], gradient boosting machine (GBM) [23; 24; 25; 26], and logistic regression (LR) [27] have been utilized for a variety of geotechnical engineering applications including classification and liquefaction. ML techniques have been widely used in various fields, including image [28; 29; 30] and speech recognition, natural language processing, and data analysis, to extract insights from large datasets. For instance, ML algorithms are used to identify objects, faces, and patterns in images, which is crucial in facial recognition, autonomous driving, and object detection in security systems. ML algorithms are also used to transcribe speech into text, enabling the creation of voice assistants, language translation software, and speech-to-text citation tools. In addition, ML algorithms are used to analyze large datasets, uncovering patterns and insights that would be difficult or impossible to identify manually. This technique has numerous applications in finance, healthcare, and scientific research.