Accounting for the inevitable uncertainty in shear wave velocity (Vs) profiles has been recognized to be critical to seismic ground response analysis (GRA) and probabilistic seismic hazard analysis (PSHA). Vs stochastic randomization models are among the most widely used approaches to account for this uncertainty. However, the suitability of these models, as currently implemented, has been undermined by their inability to match observations at vertical seismometer arrays.
This motivated modifications, such as randomizing the cumulative shear wave travel time (tts) instead of directly Vs. While some preliminary assessments suggest that tts randomization allows for better management of Vs uncertainty, the framework has only been implemented in a few case studies. The aim of this study is to fill this gap by scrutinizing the different steps of tts randomization.
By using real experimental data and Monte Carlo simulations, we argue that some of the steps of this framework pose some challenges. For each challenge, we devise recommendations and demonstrate that they provide a clear improvement on the current framework. The findings of this study will enhance our understanding of the tts randomization framework and will enable researchers and engineers to adopt it robustly and confidently at engineering scales.