## 1 Introduction  
The physical and mechanical properties of soil are essential parameters for various engineering construction designs. However, conducting experiments to obtain soil properties can be extremely cumbersome, requiring significant amounts of human, material, and financial resources [1]. Furthermore, sampling and transportation issues can cause significant disturbance and accidental errors in the soil, thereby affecting the accuracy of the results [2]. Finding a reasonable and efficient alternative can help reduce the cost of engineering investigations and improve work efficiency [3]. [PERSON] et al. proposed a correlation chart based on the liquid limit, plastic limit, and normal stress of more than 30 soil samples to estimate the internal friction angle of soils [4]. [PERSON] et al. fitted the shear strength of soils based on static probing data and obtained an empirical correlation between the two [5]. [PERSON] et al. used a multiple linear regression method to predict the unsaturated shear strength of soils based on various other soil indicators, with relatively accurate prediction results [6]. Many other scholars have also made many attempts in this area [7], and these attempts have reference significance for the prediction of soil strength and can help to effectively reduce the cost of engineering surveys. In recent years, machine learning algorithms have become a hot topic in various research fields, with neural networks, random forests, and other algorithms widely applied [8; 9; 10]. In the field of geotechnical engineering, machine learning algorithms have received widespread attention due to their advantages in solving nonlinear and multifactor problems. Many studies have been conducted on the prediction of slope safety factors [11], soil liquefaction [12], basic pavement quality [13], and shield tunneling construction [14]. Compared to traditional data fitting methods, machine learning algorithms also have great advantages in predicting soil properties. [PERSON] et al. first used neural networks to model and analyze the strength characteristics of lateritic soil and obtained a model that was more in line with actual field results [15]. [PERSON] et al. used neural networks to establish a model that correlates the physical properties of clay with its shear strength and compared it with traditional prediction methods, concluding that the neural network method has significant advantages [16]. Many scholars have attempted to predict soil strength using physical properties, mainly based on the fact that the physical properties of soil are closely related to its mechanical properties [17; 18]. However, there is no clear correlation between different physical properties and soil properties, and obtaining experimental data for physical properties often requires multiple experiments using various methods. Therefore, models constructed solely based on physical properties have significant uncertainties, necessitating the search for more reasonable prediction models. Soil is composed of various minerals, and the mineral composition is the fundamental determining factor of soil properties. It can be used as an important direction for predicting soil properties [19]. [PERSON] et al. analyzed soils in Japan and Taiwan and established an empirical formula for the relationship between mineral composition and soil mechanical properties [20]. [PERSON] et al. analyzed the impact of mineral composition on soil properties using a triangular diagram method and demonstrated that it can predict soil mechanical properties relatively well [21]. Compared to these traditional prediction methods, artificial neural network methods are more accurate and reliable, as they can effectively handle the complex interactions within the soil. Moreover, using mineral composition as a predictor allows obtaining the necessary data from a single experiment, avoiding errors that may arise due to sample or environmental variations.