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

• Published in: Di xue qian yuan. 2016, Vol.7 (1), p.75-82
• Main Authors: Gandomi, Mostafa, Soltanpour, Mohsen, Zolfaghari, Mohammad R., Gandomi, Amir H.
• Format: Article
• Language: English
• Subjects: ann模型; Artificial neural networks; Explicit formulation; Peak ground acceleration; Simulated annealing; 人工神经网络; 伊朗; 峰值加速度; 技术预测; 构造区; 混合方法; 软计算
• ISSN: 1674-9871
• DOI: 10.1016/j.gsf.2014.10.004

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