INVESTIGATION INTO AI-ASSISTED OPTIMIZATION OF THIN-WALLED CROSS-SECTIONS

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DOI:

https://doi.org/10.30888/2709-2267.2025-31-00-018

Keywords:

Artificial intelligence, Machine learning, Permanent formwork, Cross-section optimization, Surrogate modeling, Generative Design, Physics-informed neural networks, multi-fidelity data integration, Structural health monitoring, Hybrid AI-FEA workflows.

Abstract

This research reviews the integration of artificial intelligence in structural cross-section selection, highlighting supervised learning, reinforcement learning, evolutionary algorithms, and physics-informed models as complementary methodologies. Supervis

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Published

2025-05-30

How to Cite

Movchan, O. (2025). INVESTIGATION INTO AI-ASSISTED OPTIMIZATION OF THIN-WALLED CROSS-SECTIONS. Sworld-Us Conference Proceedings, 1(usc31-00), 55–62. https://doi.org/10.30888/2709-2267.2025-31-00-018

Issue

No. usc31-00 (2025): Global science and education in the modern realities '2025

Section

Abstracts

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This work is licensed under a Creative Commons Attribution 4.0 International License.