Estimating the ground shaking at a site along with its variability is often considered the main objective in the field of seismology and earthquake engineering. Specifically, in seismic hazard studies, the ground motion models (GMMs) play a crucial role in predicting the ground motion intensity measures (GMIMs) for all possible earthquake scenarios at any site of interest. In the sparse data regions mostly the GMMs are developed using either seismological models or hybrid empirical approaches. However, if these GMMs do not accommodate the regional seismological attributes, a large uncertainty in ground motion estimates is possible. To overcome this concern, select a GMIM that is close to the physics in the simulations. Fourier amplitude spectra (FAS) is one of such ground motion parameter that can be a stand-alone replacement for the commonly used GMIMs like Pseudo Spectral Acceleration (PSA). However, despite its capabilities, preferred representation in seismic hazard analysis is still the PSA as it depicts majority of parameters that is of interest to a structural engineer.

Hence, the present study aims to develop a GMM for PSA using FAS and significant duration as the predictor variables. However, since there are few GMMs available for FAS, the current study also aims to develop a GMM for FAS using the earthquake parameters as the predictor variables for intraplate regions. This article employs an Artificial Neural network (ANN) to develop both GMMs using the Next Generation Attenuation (NGA)-East database for both horizontal (Effective Amplitude spectra for FAS and RotD50 component for PSA) and vertical components. To verify the performance of the developed models, the residuals analysis and parametric studies have been performed. The parametric study shows that the GMMs can capture the magnitude and distance scaling consistent with the observations. Further, the PSA GMM compared with the global GMMs and it is observed that the predictions lie well within the median of all the available models, proving the models’ effectiveness in estimating the ground motion predictions for future data. However, the major limitation in this study is it can be used only within the considered ranges of the predicted variables such as rupture distances between [19.05-1000] km, the Mw ranges from [3.12-5.74] with Vs30 in the range of [209-2000] m/s. In addition, the alternative methods to develop a reliable GMM that can better characterize the ground motion in the seismic hazard analysis is also presented.