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

• Published in: Engineering applications of artificial intelligence 2011-06, Vol.24 (4), p.717-732
• Main Authors: Gandomi, Amir Hossein, Alavi, Amir Hossein, Mousavi, Mehdi, Tabatabaei, Seyed Morteza
• Format: Article
• Language: English
• Subjects: Genetic programming; Nonlinear modeling; Orthogonal least squares; Prediction equations; Time-domain ground-motion parameters
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2011.01.005

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.