Axial resistant behavior of stub fold-fastened multi-cellular steel walls: Tests and simulations DOI

Shengjie Duan,

Jing‐Zhong Tong, Chao-Qun Yu

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112913 - 112913

Published: May 1, 2025

Language: Английский

Unified machine-learning-based design method for cold-formed steel multi-limbs built-up open section columns DOI
Yan Lu,

Bin Wu,

Tianhua Zhou

et al.

Structures, Journal Year: 2025, Volume and Issue: 73, P. 108398 - 108398

Published: Feb. 14, 2025

Language: Английский

Citations

1

Application of Artificial Intelligence to Support Design and Analysis of Steel Structures DOI Creative Commons
Sina Sarfarazi, Ida Mascolo, Mariano Modano

et al.

Metals, Journal Year: 2025, Volume and Issue: 15(4), P. 408 - 408

Published: April 4, 2025

In steel structural engineering, artificial intelligence (AI) and machine learning (ML) are improving accuracy, efficiency, automation. This review explores AI-driven approaches, emphasizing how AI models improve predictive capabilities, optimize performance, reduce computational costs compared to traditional methods. Inverse Machine Learning (IML) is a major focus since it helps engineers minimize reliance on iterative trial-and-error by allowing them identify ideal material properties geometric configurations depending predefined performance targets. Unlike conventional ML that mostly forward predictions, IML data-driven design generation, enabling more adaptive engineering solutions. Furthermore, underlined Explainable Artificial Intelligence (XAI), which enhances model transparency, interpretability, trust of AI. The paper categorizes applications in construction based their impact automation, health monitoring, failure prediction evaluation throughout research from 1990 2025. challenges such as data limitations, generalization, reliability, the need for physics-informed while examining AI’s role bridging real-world applications. By integrating into this work supports adoption ML, IML, XAI analysis design, paving way reliable interpretable practices.

Language: Английский

Citations

1

An explainable machine learning method for predicting and designing crashworthiness of multi-cell tubes under oblique load DOI

Jian Xie,

Junyuan Zhang, Zheng Dou

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 147, P. 110396 - 110396

Published: Feb. 26, 2025

Language: Английский

Citations

0

Deep Rayleigh-Ritz method for elastic local buckling analysis of cold-formed steel columns DOI
Yan Lu, Bo Ren,

Bin Wu

et al.

Structures, Journal Year: 2025, Volume and Issue: 76, P. 109016 - 109016

Published: April 28, 2025

Language: Английский

Citations

0

Axial resistant behavior of stub fold-fastened multi-cellular steel walls: Tests and simulations DOI

Shengjie Duan,

Jing‐Zhong Tong, Chao-Qun Yu

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112913 - 112913

Published: May 1, 2025

Language: Английский

Citations

0