Site-Condition Proxies,Ground Motion Variability,and Data-Driven GMPEs: Insights from the NGA-West2 and RESORCE Data Sets

Abstract

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%.