ABSTRACT
Peak Ground Velocity (PGV) is one of the most important ground motion parameters that has been widely used as a damage potential indicator, as well as in seismic design of structures and assessment of buried pipelines and liquefaction potential analysis. Therefore, estimating a precise value for this parameter is of great importance. In this paper, Genetic Programming (GP), a well-known Artificial Intelligence method is utilized to develop an attenuation relationship for PGV based on the strong ground motion database released by Pacific Earthquake Engineering Research center (PEER). Different PGV attenuation relationships are proposed for strike-slip, normal, and reverse faulting mechanisms as functions of earthquake magnitude, source to site distance, and local site geotechnical condition. The values of coefficient of determination, root mean square error and mean absolute error are calculated for the developed PGV attenuation relationships and reveal the accuracy of proposed model. Results of the parametric study demonstrate that PGV is higher for larger earthquake magnitudes while it is lower for sites which are located farther from the source and have lower shear wave velocities.
