A hybrid computational approach to derive new ground-motion prediction equations

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@Article{Gandomi2011717,
  author =       "Amir Hossein Gandomi and Amir Hossein Alavi and 
                 Mehdi Mousavi and Seyed Morteza Tabatabaei",
  title =        "A hybrid computational approach to derive new
                 ground-motion prediction equations",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "24",
  number =       "4",
  pages =        "717--732",
  year =         "2011",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2011.01.005",
  URL =          "http://www.sciencedirect.com/science/article/B6V2M-52C83TR-1/2/0e8d2ec5097e6a0e7eef643a7e26d527",
  keywords =     "genetic algorithms, genetic programming, Time-domain
                 ground-motion parameters, Prediction equations,
                 Orthogonal least squares, Nonlinear modelling",
  abstract =     "A novel hybrid method coupling genetic programming and
                 orthogonal least squares, called GP/OLS, was employed
                 to derive new ground-motion prediction equations
                 (GMPEs). The principal ground-motion parameters
                 formulated were peak ground acceleration (PGA), peak
                 ground velocity (PGV) and peak ground displacement
                 (PGD). The proposed GMPEs relate PGA, PGV and PGD to
                 different seismic parameters including earthquake
                 magnitude, earthquake source to site distance, average
                 shear-wave velocity, and faulting mechanisms. The
                 equations were established based on an extensive
                 database of strong ground-motion recordings released by
                 Pacific Earthquake Engineering Research Center (PEER).
                 For more validity verification, the developed equations
                 were employed to predict the ground-motion parameters
                 of the Iranian plateau earthquakes. A sensitivity
                 analysis was carried out to determine the contributions
                 of the parameters affecting PGA, PGV and PGD. The
                 sensitivity of the models to the variations of the
                 influencing parameters was further evaluated through a
                 parametric analysis. The obtained GMPEs are effectively
                 capable of estimating the site ground-motion
                 parameters. The equations provide a prediction
                 performance better than or comparable with the
                 attenuation relationships found in the literature. The
                 derived GMPEs are remarkably simple and straightforward
                 and can reliably be used for the pre-design purposes.",
}

Genetic Programming entries for A H Gandomi A H Alavi Mehdi Mousavi Seyed Morteza Tabatabaei

Citations