The achieved results revealed that the support vector regression model attained the most accurate prediction for soil undrained shear strength. The coefficient of determination was mainly used to assess the performance of each predictive model. Several machine learning algorithms were adopted to provide the soft clay modeling, including the linear, Gaussian process regression, ensemble and regression trees, and the support vector regression. These features included the water content, liquid limit, dry unit weight, plasticity index, consistency index, void ratio, specific gravity, and pocket penetration shear.
In this context, a dataset of 111 geotechnical testing points were collected based on laboratory and field examinations of the soil’s key features. In this research, accurate field-tested data were fed to advanced machine learning models to predict the undrained shear strength of the sensitive clay to save hard effort, time, repeated laboratory testing, and costs. Consequently, extracting undisturbed samples of sensitive clay faces several difficulties on construction sites. However, determining undrained shear strength is very difficult, costly, and time-consuming, especially for sensitive clay. This soil feature strongly influences the selection of appropriate soil improvement methods. The bearing capacity of structure foundations on soft soil depends mainly on their undrained shear strength. Constructing structures and/or infrastructures on soft soil is a considerable risk that needs great attention from structural engineers.
Soft soil is classified as problematic soil owing to sustaining low shear strength and high settlement under structures. These regions are vital and attractive for population and governments development.
Soft soils are commonly located in many regions near seas, oceans, and rivers all over the world.
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