Estimating In Situ Shear Wave Velocity Using Machine Learning Techniques

Machine learning (ML) is widely used in many industries to develop predictive models and gain new insights. This paper presents an application of ML to a geotechnical problem: estimating shear wave velocity (Vs) from cone penetration testing (CPT) data where no direct Vs data was obtained. This application is valuable when using historical CPT data obtained prior to the widespread availability of seismic CPT (SCPT) equipment or in situations where SCPT is difficult, such as below the water surface in impounded tailings and coal combustion residual (CCR) ponds. We developed both material-specific and universally applicable ML models to estimate Vs based on conventional CPT measurements. The models were built in Python using two tree-based algorithms: random forest and gradient-boosted trees. The ML models were compared to both existing widely accepted models and a linear regression technique to assess if the performance gains justify the increased model complexity. The ML models show more accurate estimates of Vs than other available methods.