Effects of grain size distribution on the initial strain shear modulus of calcareous sand

The soil’s small strain shear modulus, Gmax or G0, is applied in dynamic behavior analyses and is correlated to other soil properties (density and void ratio) for predicting soil dynamic behavior under seismic loadings such as earthquakes, machinery or traffic vibrations. However, for calcareous sands, selecting representative samples for the field conditions is difficult; therefore, almost all measured soil parameters (post-seismic properties) do not reflect exactly the soil state before seismic loading. In some cases of dynamic loading, a change in grain size distribution (GSD) of soils, especially for calcareous sands might occur. Moreover, many of these sand types behave differently from silica sands owing to their mineralogy, particle characterization, soil skeleton, and the continuous changing of particle size. For this reason, a series of isotropic consolidation tests in ranges of confining pressure from 25 to 300 kPa as well as bender element measurements on a calcareous sand and on a reference silica sand were performed in this study. The effects of differences in gradation and in the type of material on the soil’s small strain shear modulus, Gmax, are discussed

Forecasting Swiss inflation using VAR models

A procedure that has been used at the Swiss National Bank for selecting vector-autoregressive (VAR) models in order to forecast Swiss consumer price inflation is presented. In order to examine and improve the quality of the procedure, it is submitted to several modifications and the results are compared with one another. Combining forecasts substantially improves the quality of the forecasts. Models specified with respect to levels of variables are superior to those specified with respect to differences in variables. Bank loans and the monetary aggregate M3 are the most important variables for inflation forecasting. The optimized procedure reduces the root mean squared error (RMSE) of the inflation forecast to one third of the RMSE of a naive "no change" forecast over the period from 1987 to 2005.inflation forecasting, VAR models, model selection, model evaluation