Fire resistance rating prediction of timber-to-steel connections and design optimization informed by explainable machine learning DOI
Tongchen Han, Zhidong Zhang, Weiwei Wu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 156, С. 111127 - 111127

Опубликована: Май 29, 2025

Язык: Английский

Critical temperature prediction in cold-formed steel columns exposed to local fire DOI
Ravikant Singh, Avik Samanta

Journal of Constructional Steel Research, Год журнала: 2025, Номер 229, С. 109509 - 109509

Опубликована: Март 11, 2025

Язык: Английский

Процитировано

1
Application of Artificial Intelligence to Support Design and Analysis of Steel Structures DOI Creative Commons
Sina Sarfarazi, Ida Mascolo, Mariano Modano

и другие.

Metals, Год журнала: 2025, Номер 15(4), С. 408 - 408

Опубликована: Апрель 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.

Язык: Английский

Процитировано

1
Data-driven prediction and optimization of fire performance for cold-formed steel walls with board joints DOI
Mingming Yu, Yaqiong Liu, Zhe Chang

и другие.

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112797 - 112797

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Hybrid machine learning approach with FHO algorithm and WERCS method for predicting fire resistance of timber columns DOI

T. D. Nguyen,

Van-Thanh Pham, Quang-Viet Vu

и другие.

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112679 - 112679

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Web crippling strength of cold-formed steel lipped channels under interior-two-flange loading: Data-driven modelling with shapley explanations DOI

Yehan Karunaratne,

Shashika Dharmawansha,

Theventhiran Suganiyah

и другие.

Structures, Год журнала: 2025, Номер 78, С. 109241 - 109241

Опубликована: Май 24, 2025

Язык: Английский

Процитировано

0
Fire resistance rating prediction of timber-to-steel connections and design optimization informed by explainable machine learning DOI
Tongchen Han, Zhidong Zhang, Weiwei Wu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 156, С. 111127 - 111127

Опубликована: Май 29, 2025

Язык: Английский

Процитировано

0