Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing

In this study, new models are derived to predict the peak time-domain characteristics of strong ground-motions utilizing a novel hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA. The principal ground-motion parameters formulated are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The proposed models relate PGA, PGV and PGD to earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. A database of strong ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER) is used to establish the models. For more validity verification, the ANN/SA models are employed to predict the ground-motion parameters of a part of the database beyond the training data domain. ANN and multiple linear regression analyses are performed to benchmark the proposed models. Contributions of the input parameters to the prediction of PGA, PGV and PGD are evaluated through a sensitivity analysis. The ANN/SA attenuation models give precise estimations of the site ground-motion parameters. The proposed models perform superior than the single ANN, regression and existing attenuation models. The optimal ANN/SA models are subsequently converted into tractable design equations. The derived equations can readily be used by designers as quick checks on solutions developed via more in-depth deterministic analyses.

► A new hybrid ANN/SA method is presented to predict the ground-motion parameters.
► Temperature cycling cooling schedule provides a more robust training than the linear schedule.
► The ANN/SA-based design equations provide analysis tools accessible to practicing engineers.
► The proposed models perform superior than the ANN, regression and existing attenuation models.