22–24 Mar 2021
University of Zagreb Faculty of Civil Engineering, Zagreb, Croatia
Europe/Zagreb timezone

A MACHINE LEARNING FRAMEWORK FOR AUTOMATED GROUND MOTION PREDICTION

Not scheduled
20m
VP (University of Zagreb Faculty of Civil Engineering, Zagreb, Croatia)

VP

University of Zagreb Faculty of Civil Engineering, Zagreb, Croatia

Kačićeva 26 10 000 Zagreb
Full paper - scientific Innovative Technology

Speakers

Dr MOHELDEEN HEJAZI (ISTANBUL TECHNICAL UNIVERSITY)Mrs Serra Tinbir (Istanbul Technical University)

Description

The continuous expansion of seismic catalogs is increasingly challenging the validity of existing ground motion prediction equations. For such real-time data, the transition of ground motion modeling toward automation became eminent. This article put forth a dynamic intelligent ground motion prediction system, automated through a novel hybridization of neural networks with a multi-objective swarm intelligence optimization to facilitate updated predictions. Under the proposed framework, acceleration data are parsed from catalogs in real-time and the continuous stream of seismic data is analyzed to both (i) optimize model predictions and (ii) minimize its computational demand. Though built adaptive to different geographical locations, the system in this article is presented in the context of Turkey. Therefore, real-time strong ground-motion records are obtained from the AFAD database, where the peak ground acceleration (PGA), velocity (PGV), and displacement (PGD) are examined against various seismic variables including earthquake magnitude, source to site distance, average shear-wave velocity, and focal mechanism. The model predictions were verified against a broad testing sample and predictions by various GMPE models for Turkey. Thereafter, the model stability was examined through an investigation into the sensitivity of the PGA, PGV, and PGD predictions to data and parameters’ discrepancies. Finally, this article discusses the future potentials and challenges facing the developed framework.

Keywords ground-motion prediction equations, machine learning, optimization algorithms, Earthquake catalogs
DOI https://doi.org/10.5592/CO/1CroCEE.2021.203

Primary authors

Dr MOHELDEEN HEJAZI (ISTANBUL TECHNICAL UNIVERSITY) Mrs Serra Tinbir (Istanbul Technical University) Mr Pooya ghaffari khalifani (Istanbul Technical University) Prof. Ali Sari (Istanbul Technical University)

Presentation materials