On the application of genetic programming for new generation of ground motion prediction equations

Mehdi Mousavi, Alireza Azarbakht, Sahar Rahpeyma, Ali Farhadi

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationHandbook of Genetic Programming Applications
PublisherSpringer International Publishing AG
Pages289-307
Number of pages19
ISBN (Electronic)9783319208831
ISBN (Print)9783319208824
DOIs
Publication statusPublished - 1 Jan 2015

    Fingerprint

Keywords

  • ground motion
  • genetic programming
  • peak ground acceleration
  • gene expression programming
  • peak ground velocity

Cite this

Mousavi, M., Azarbakht, A., Rahpeyma, S., & Farhadi, A. (2015). On the application of genetic programming for new generation of ground motion prediction equations. In Handbook of Genetic Programming Applications (pp. 289-307). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-20883-1_11