Generative inverse design of steel gridshell joints with multi-objective optimisation
File(s)
Published version
Author(s)
Chen, Man-Tai
Pan, Yue
Zuo, Wenkang
Zhao, Ou
Gardner, Leroy
Type
Journal Article
Abstract
The design of steel gridshell joints, simultaneously minimising weight, maximising stiffness and ensuring a uniform stress distribution, is a challenging multi-objective problem. This paper presents a generative inverse design framework integrating topology optimisation (TO), data-driven surrogate modelling and multi-objective optimisation to automatically generate high-performance steel joint designs. A parametric workflow links a BESO-based TO module with a Bayesian-optimised XGBoost surrogate model for predicting joint compliance and stress variation. An NSGA-II parametric evolutionary optimiser then explores trade-offs among competing objectives, while K-means clustering extracts representative Pareto-optimal solutions. The effectiveness of the framework is validated by a case study, with the generated joints achieving up to 40% weight reduction and improved stiffness and stress uniformity relative to a conventional hollow joint. One selected design was successfully fabricated via selective laser melting 3D printing, demonstrating practical manufacturability. The proposed framework is also adaptive to other steel gridshell joint forms.
Date Issued
2026-05-01
Date Acceptance
2026-02-13
Citation
Advanced Engineering Informatics, 2026, 72
ISSN
1474-0346
Publisher
Elsevier
Journal / Book Title
Advanced Engineering Informatics
Volume
72
Copyright Statement
© 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Publication Status
Published
Article Number
104483
Date Publish Online
2026-02-17