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The main scope of the InterPACIFIC (Intercomparison of methods for site parameter and velocity profile characterization) project is to assess the reliability of in-hole and surface-wave methods, used for estimating shear wave velocity. Three test-sites with different subsurface conditions were chosen: a soft soil, a stiff soil and a rock outcrop. This paper reports the surface-wave methods results. Specifically 14 teams of expert users analysed the same experimental surface-wave datasets, consisting of both passive and active data. Each team adopted their own strategy to retrieve the dispersion curve and the shear-wave velocity profile at each site. Despite different approaches, the dispersion curves are quite in agreement with each other. Conversely, the shear-wave velocity profiles show a certain variability that increases in correspondence of major stratigraphic interfaces. This larger variability is mainly due to non-uniqueness of the solution and lateral variability. As expected, the observed variability in V-s,V-30 estimatesis small, as solution non-uniqueness plays a limited role. (C) 2015 Elsevier Ltd. All rights reserved.
We compare the ability of various site-condition proxies (SCPs) to reduce the aleatory variability of ground motion prediction equations (GMPEs). Three SCPs (measured V-S30, inferred V-S30, local topographic slope) and two accelerometric databases (RESORCE and NGA-West2) are considered. An artificial neural network (ANN) approach including a random-effect procedure is used to derive GMPEs setting the relationship between peak ground acceleration (PGA), peak ground velocity (PGV), pseudo-spectral acceleration [PSA(T)], and explanatory variables (M-w, R-JB, and V-S30 or Slope). The analysis is performed using both discrete site classes and continuous proxy values. All "non-measured" SCPs exhibit a rather poor performance in reducing aleatory variability, compared to the better performance of measured V-S30. A new, fully data-driven GMPE based on the NGA-West2 is then derived, with an aleatory variability value depending on the quality of the SCP. It proves very consistent with previous GMPEs built on the same data set. Measuring V-S30 allows for benefit from an aleatory variability reduction up to 15%.