Prediction of peak ground acceleration of Iran’s tectonic regions using a hybrid soft computing technique

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 ¼ 0.835 and r ¼ 0.0908) and it is subsequently converted into a tractable design equation.

• Publication year

  2016

• Venue

  Geoscience frontiers

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering, Geology

• Identifiers
  DOI 10.1016/J.GSF.2014.10.004
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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