# Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach
## 1 Introduction  
Patches of soft soil are distributed throughout many vital regions near oceans, seas, and rivers. These regions are essential for human activities and the development of various structures such as roads, bridges, embankments, buildings, railroads, tunnels, etc., and new cities are also constructed on this type of soil. The problems presented by soft soil are high compressibility and low shear strength. Moreover, structure settlement continues for long periods. The failure of structures mainly occurs due to a lack of soft soil shear strength. Soft soil is one of the problematic soils encountered all over the world. Consequently, the \"soft soil\" [1], is defined as soil that typically exhibits an undrained shear strength (USS) in the range of 20 to 40 kPa, while the very soft soil possesses USS values of less than 20 kPa. The performance of soft soil depends not only on water content, but also on its structure. Generally, the soft soil remains stiff in the dry state until it is subjected to a drasticincrease in its water content, at which it converts to weak and soft soils [2]. The saturated soft soil causes several problems for structures on it, such as low bearing capacity and long-term excessive settlement [3]. Constructing heavy structures on native soft clay soil is a difficult task. The geotechnical design depends mainly on the (short-term) undrained shear strength of the soft soil, as it has a smaller value compared to that of the (long-term) drained shear strength. Hence, the undrained shear strength of the soft soil is the most important parameter to be determined when calculating its bearing capacity. The selection of suitable improvement methods for the sensitive clay is also mainly dependent on the accurate values of its undrained shear strength. However, there is a difficulty associated with extracting and sampling the soft soil, especially the very sensitive soft clay. The sensitive clay usually has an undrained shear strength in the laboratory smaller than that in the field due to the effect of disturbance on the sample. Since the design of structure foundations depends on the undrained shear strength of the sensitive clay, it must be carefully determined and evaluated from the results of both field and laboratory tests. The precision of the laboratory tests, such as direct shear, unconfined compression, or undrained triaxial compression, significantly depend on the quality of the collected sensitive, undisturbed clay samples [2; 4], and additionally rely on the thickness and the friction of the sharp edges of the circular samplers with soil. On the other hand, field test results (field vane test and piezocone cone penetration test--CPTU) are also influenced by selection techniques [5; 6]. In 2021, [PERSON] [4] stated that the field vane shear test is more accurate than the Swedish cone shear test. Due to the difficulty of determining the undrained shear strength of the sensitive clay, empirical equations are utilized based on clay properties, like clay activity and Arterberg limits [4; 7; 8; 9; 10; 11], water content, plasticity index, and over consolidation ratio [12]. However, there is a shortage of past studies that investigate the functional form and the influential soil properties of the existing empirical correlations. Moreover, [PERSON] et al. [13] stated that pre-consolidation pressures can differ significantly owing to soil disturbances during sampling, which occasionally causes unpredictable values of the pre-consolidation pressures. Subsequently, empirical correlations include only a few soil parameters despite there being greater soil parameter influences on the undrained shear strength of the soft soil. Recently, machine learning (ML) models have been widely utilized to predict the same important soil parameters that are costly, time-consuming, and require great effort to determine in the laboratory or the field.