Particle swarm optimization technique-based prediction of peak ground acceleration of Iraq’s tectonic regions

Abstract

Peak ground acceleration ($\mathrm{PGA}$) is one of the critical factors that affect the determination of earthquake intensity. PGA is generally utilized to describe ground motion in a particular zone and is able to efficiently predict the parameters of site ground motion for the design of engineering structures. Therefore, novel models are developed to forecast PGA in the case of the Iraqi database, which utilizes the particle swarm optimization ($\mathrm{PSO}$) approach. A data set of 187 historical ground-motion recordings in Iraq’s tectonic regions was used to build the explicit proposed models. The proposed PGA models relate to different seismic parameters, including the magnitude of the earthquake ($M_w$), average shear-wave velocity ($V_{S30}$), focal depth ($\mathrm{FD}$), and nearest epicenter distance ($R_{\mathrm{EPi}}$) to a seismic station. The derived PGA models are remarkably simple and straightforward and can be used reliably for pre-design purposes. The proposed $\mathrm{PGA}$ models (i.e., models I and II) obtained via the explicit formula produced using the $\mathrm{PSO}$ method are highly correlated to the actual $\mathrm{PGA}$ records owing to low coefficients of variation ($\mathrm{CoV}$) of approximately 2.12% and 2.06%, and mean values (i.e., close to 1.0) of approximately 1.005 and 1.004. Lastly, high-frequency, low absolute relative error ($\mathrm{ARE}$), which is below 5%, is recorded for the proposed models, thereby showing an acceptable error distribution.