On the Application of Genetic Programming for New Generation of Ground Motion Prediction Equations

Abstract

The ground-motion prediction equations (GMPEs)generally predict ground-motion intensities such aspeak ground acceleration (PGA), peak ground velocity(PGV), and response spectral acceleration (SA), as afunctional form of magnitude, site-to-source distance,site condition, and other seismological parameters. Anadequate prediction of the expected ground motionintensities plays a fundamental role in practicalassessment of seismic hazard analysis, thus GMPEs areknown as the most potent elements that conspicuouslyaffect the Seismic Hazard Analysis (SHA). Recently,beside two common traditional methodologies, i.e.empirical and physical relationships, the applicationof Genetic Programming, as an optimization techniquebased on the Evolutionary Algorithms (EA), has taken onvast new dimensions. During recent decades, thecomplexity of obtaining an appropriate predictive modelleads to different studies that aim to achieve GeneticProgramming-based GMPEs. In this chapter, the concepts,methodologies and results of different studiesregarding driving new ground motion relationships basedon Genetic Programming are discussed.