کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4681525 | 1348855 | 2016 | 8 صفحه PDF | دانلود رایگان |
• A hybrid artificial neural network and simulated annealing is proposed for modeling.
• A new model is derived to predict the peak ground acceleration Iran's tectonic regions.
• The models are established based on records of 36 earthquakes.
• The proposed models are also compared with ten other well-known models.
A new model is derived to predict the peak ground acceleration (PGA) utilizing a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called SA-ANN. The proposed model relates PGA to earthquake source to site distance, earthquake magnitude, average shear-wave velocity, faulting mechanisms, and focal depth. A database of strong ground-motion recordings of 36 earthquakes, which happened in Iran's tectonic regions, is used to establish the model. For more validity verification, the SA-ANN model is employed to predict the PGA of a part of the database beyond the training data domain. The proposed SA-ANN model is compared with the simple ANN in addition to 10 well-known models proposed in the literature. The proposed model performance is superior to the single ANN and other existing attenuation models. The SA-ANN model is highly correlated to the actual records (R = 0.835 and ρ = 0.0908) and it is subsequently converted into a tractable design equation.
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Journal: Geoscience Frontiers - Volume 7, Issue 1, January 2016, Pages 75–82