A Deep-Neural-Network-Based Prediction Model for Elastic Input Energy Spectra of Horizontal and Vertical Ground Motions

Abstract

ABSTRACT Intensity measures based on energy have proven to be robust indicators of damage for a variety of structural types. This article presents a modified ground-motion model (GMM) incorporating a deep neural network to predict elastic input energy spectra for both horizontal and vertical ground motions, considering the pulselike ground motions. The newly developed model employs six predictor variables, that is, moment magnitude Mw, fault mechanism F, rupture distance Rrup, logarithmic rupture distance ln(Rrup), rupture directivity term Idir, and logarithmic shear-wave velocity ln(VS30) as inputs. A subset of records, sourced from the recently updated Next Generation Attenuation-West2 Project database constituted by 2745 ground motions from 97 earthquakes, have been employed in the development of the model. The performance of the developed model remains within the prescribed error range. In addition, the proposed model is compared against currently used GMMs. The predicted spectra obtained from the present study are in good agreement with those given by other literature, and the standard deviations of residuals have been reduced by ∼20% and are more stable. Observations from these results indicate that the newly proposed model generates improved predictions.