Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques



Peak ground acceleration (PGA) is frequently used to describe ground motions accurately to defined the zone is critical for structural engineering design. This study developed a novel models for predicting the PGA using Artificial Neural Networks-Gravitational Search Algorithm (ANN-GSA) and Response Surface Methodology (RSM). This paper grants the prediction of PGA for the seismotectonic of Iraq, which is considered the earlier attempt in Iraqi region. The magnitude of the earthquake, the average shear-wave velocity, the focal depth, the distance between the station, and the earthquake source were used in this study. The proposed models are constructed using a database of 187 previous ground motion records, this dataset is also utilized to evaluate the effect of PGA’s parameters. In general, the results demonstrate that the newly proposed models exhibit a high degree of correlation, perfect mean values, a low coefficient of variance, fewer errors, and an acceptable performance index value compared to actual PGA values. However, the composite ANN-GSA model performs better than the RSM model.