Evaluation of empirical and machine learning models for predicting shear wave velocity of granular soils based on laboratory element tests

Shear wave velocity (V-s) is crucial for designing geotechnical systems subjected to dynamic loads, especially in seismically active regions. The shear wave velocity of geomaterials can be determined using in situ and laboratory tests. However, due to time and cost limitations, the V-s is not easily available in most projects. Various empirical models have been developed by researchers for predicting the shear wave velocity of geomaterials. However, most of these models have been developed for specific soil types and loading characteristics. In this work, for predicting the shear wave velocity of granular soils using various combinations of input parameters, various empirical models were proposed. Furthermore, machine learning (ML) methods were utilized to predict the V-s. The suggested models consider the impact of grading characteristics such as fine content (FC), gravel content (GC), median particle size (D-50), uniformity coefficient (C-u), and coefficient of curvature (C-c), as well as void ratio (e), mean effective confining pressure (sigma(m)'), consolidation stress ratio (K-C), and specimen preparation techniques for reconstitution of specimens. To achieve this, a series of bender element tests were performed on various sand and gravel mixtures. Furthermore, data from previous studies were also used. So, the study utilized 513 data points from laboratory element experiments conducted on granular soils. For predicting the V-s of granular soils, four empirical models and three ML models, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR), were developed in this study. The findings showed that the ANN model outperforms the other comparative models in terms of accuracy and error. While the empirical models may serve as useful tools for initial Vs estimation in construction projects, the study primarily highlights the significance of using ML methods to enhance the prediction accuracy of V-s based on the available soil properties.