PREdiction of NOn-LINear soil behavior (PRENOLIN) is an international benchmark aiming to test multiple numerical simulation codes that are capable of predicting nonlinear seismic site response with various constitutive models. One of the objectives of this project is the assessment of the uncertainties associated with nonlinear simulation of 1D site effects. A first verification phase (i.e., comparison between numerical codes on simple idealistic cases) will be followed by a validation phase, comparing the predictions of such numerical estimations with actual strongmotion recordings obtained at well-known sites. The benchmark presently involves 21 teams and 23 different computational codes.We present here the main results of the verification phase dealing with simple cases. Three different idealized soil profiles were tested over a wide range of shear strains with different input motions and different boundary conditions at the sediment/bedrock interface. A first iteration focusing on the elastic and viscoelastic cases was proved to be useful to ensure a common understanding and to identify numerical issues before pursuing the nonlinear modeling. Besides minor mistakes in the implementation of input parameters and output units, the initial discrepancies between the numerical results can be attributed to (1) different understanding of the expression "input motion" in different communities, and (2) different implementations of material damping and possible numerical energy dissipation. The second round of computations thus allowed a convergence of all teams to the Haskell-Thomson analytical solution in elastic and viscoelastic cases. For nonlinear computations, we investigate the epistemic uncertainties related only to wave propagation modeling using different nonlinear constitutive models. Such epistemic uncertainties are shown to increase with the strain level and to reach values around 0.2 (log 10 scale) for a peak ground acceleration of 5 m=s 2 at the base of the soil column, which may be reduced by almost 50% when the various constitutive models used the same shear strength and damping implementation.
The Central and Eastern United States (CEUS) has experienced an abnormal increase in seismic activity, which is believed to be related to anthropogenic activities. The U.S. Geological Survey has acknowledged this situation and developed the CEUS 2016 1 year seismic hazard model using the catalog of 2015 by assuming stationary seismicity in that period. However, due to the nonstationary nature of induced seismicity, it is essential to identify change points for accurate probabilistic seismic hazard analysis (PSHA). We present a Bayesian procedure to identify the most probable change points in seismicity and define their respective seismic rates. It uses prior distributions in agreement with conventional PSHA and updates them with recent data to identify seismicity changes. It can determine the change points in a regional scale and may incorporate different types of information in an objective manner. It is first successfully tested with simulated data, and then it is used to evaluate Oklahoma's regional seismicity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.