Dataset for Fail-Safe Topology Optimization using Artificial Neural Networks
Citation Link: https://doi.org/10.15480/882.17073
Type
Dataset
Version
v1.0
Date Issued
2026-05-11
Language
English
TORE-DOI
Abstract
This repository contains the training dataset created within the research project "Structural Optimization for Fail-Safe Designs by Machine Learning". The objective of the project is to investigate machine learning approaches for fail-safe topology optimization. In particular, a conditional generative adversarial network (cGAN) was trained to map topology-optimized non-fail-safe structures obtained from standard compliance minimization to corresponding fail-safe designs.
Subjects
Fail-Safe Optimization
Artificial Neural Networks
Damage Tolerance
DDC Class
620: Engineering
006.3: Artificial Intelligence
Funding Organisations