Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques

Mahir   Hason; Alaa  Al-Zuhairi; Hanoon  Hanoon; Ali  Abdulhameed; Ahmed Al Zand; Imad  Abbood

doi:10.1590/1679-78256940

Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques

Authors

Mahir M. Hason Disaster Information Management Centre, Ministry of Science and Technology, Baghdad 10071 Iraq https://orcid.org/0000-0001-8292-5047
Alaa Hussein Al-Zuhairi Department of Civil Engineering, University of Baghdad, Baghdad 10071 Iraq https://orcid.org/0000-0003-3859-6949
Ammar N. Hanoon Department of Reconstruction and Projects, University of Baghdad, Baghdad 10071, Iraq https://orcid.org/0000-0002-1369-9856
Ali A. Abdulhameed Department of Reconstruction and Projects, University of Baghdad, Baghdad 10071, Iraq https://orcid.org/0000-0002-6845-3669
Ahmed W. Al Zand Department of Civil Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia https://orcid.org/0000-0002-8137-5829
Imad S. Abbood Engineering Affairs Department, Sunni Endowment Diwan, Baghdad 10071, Iraq https://orcid.org/0000-0002-4016-7185

DOI:

https://doi.org/10.1590/1679-78256940

Abstract

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.

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Published

2022-05-06

Issue

Vol. 19 No. 3 (2022)

Section

Articles

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