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 as peak ground acceleration (PGA), peak ground velocity (PGV), and response spectral acceleration (SA), as a functional form of magnitude, site-to-source distance, site condition, and other seismological parameters. An adequate prediction of the expected ground motion intensities plays a fundamental role in practical assessment of seismic hazard analysis, thus GMPEs are known as the most potent elements that conspicuously affect the Seismic Hazard Analysis (SHA). Recently, beside two common traditional methodologies, i.e. empirical and physical relationships, the application of Genetic Programming, as an optimization technique based on the Evolutionary Algorithms (EA), has taken on vast new dimensions. During recent decades, the complexity of obtaining an appropriate predictive model leads to different studies that aim to achieve Genetic Programming-based GMPEs. In this chapter, the concepts, methodologies and results of different studies regarding driving new ground motion relationships based on Genetic Programming are discussed.