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Handbook of Genetic Programming Applications pp 289–307Cite as

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

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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.

Keywords

  • Ground Motion
  • Genetic Programming
  • Peak Ground Acceleration
  • Gene Expression Programming
  • Peak Ground Velocity

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Arak University, Arak, Iran

    Mehdi Mousavi, Alireza Azarbakht & Ali Farhadi

  2. Earthquake Engineering Research Center, University of Iceland, Reykjavík, Iceland

    Sahar Rahpeyma

Authors
  1. Mehdi Mousavi
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  2. Alireza Azarbakht
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  3. Sahar Rahpeyma
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  4. Ali Farhadi
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Corresponding author

Correspondence to Mehdi Mousavi .

Editor information

Editors and Affiliations

  1. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, USA

    Amir H. Gandomi

  2. Department of Civil & Environmental Engineering, Michigan State University, East Lansing, Michigan, USA

    Amir H. Alavi

  3. Department of Computer Science and Information Systems, University of Limerick, Limerick, Limerick, Ireland

    Conor Ryan

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Mousavi, M., Azarbakht, A., Rahpeyma, S., Farhadi, A. (2015). On the Application of Genetic Programming for New Generation of Ground Motion Prediction Equations. In: Gandomi, A., Alavi, A., Ryan, C. (eds) Handbook of Genetic Programming Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20883-1_11

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