RELIABILITY ASSESSMENT OF STEEL PORTAL FRAMES USING GAN FOR GENERATING SYNTHETIC DATA SAMPLE

Abstract

This paper addresses the challenge of reliability estimation in structural engineering, where determining failure probabilities is often uncertain due to insufficient characterization of randomness and scale of design problems. Current approaches, like EN 1990, lack sufficient data for research and simulations, particularly for low failure probabilities (around 10^-4), making Monte Carlo simulations less accurate. The paper introduces Generative Adversarial Networks (GANs) as a solution to generate synthetic data to supplement existing examples. The study applies GANs to assess the reliability of steel portal-framed industrial buildings and evaluate the safety of this structural solution according to Eurocodes.