A Systematic Review on Utilizing Artificial Intelligence in Lateral Resisting Systems of Buildings DOI
Yasir Abduljaleel, Fathoni Usman, Agusril Syamsir

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 27, 2025

Language: Английский

Study on assembly characteristics of an assembled wind tunnel balance with four triaxial-force piezoelectric sensors DOI

Yaogang Tian,

Jin Wang, Yaqi Wu

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117496 - 117496

Published: April 1, 2025

Language: Английский

Citations

0

A hybrid machine learning framework for wind pressure prediction on buildings with constrained sensor networks DOI Creative Commons

Foad Mohajeri Nav,

Seyedeh Fatemeh Mirfakhar,

Reda Snaiki

et al.

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

Abstract Accurate and efficient prediction of wind pressure distributions on high‐rise building façades is crucial for mitigating structural risks in urban environments. Conventional approaches rely extensive sensor networks, often hindered by cost, accessibility, architectural limitations. This study proposes a novel hybrid machine learning (ML) framework that reconstructs high‐fidelity (HFWP) coefficient fields from limited number sensors leveraging dynamic spatiotemporal feature extraction mapping. The methodology consists four key stages: (1) low‐fidelity field reconstruction data using constrained QR decomposition, (2) dimensionality reduction both HFWP reconstructions to extract dominant features, (3) mapping the reduced‐order representations long short‐term memory network, (4) over time. proposed approach, which predicts time history coefficients various directions, validated tunnel data, with case studies multiple façades—including windward, right‐side, leeward surfaces—under placement scenarios. also evaluated against alternative ML models, demonstrating superior accuracy reconstructing full field. results highlight robustness generalization capability model across different directions configurations, making it practical solution real‐time estimation health monitoring digital twin applications.

Language: Английский

Citations

0

A Systematic Review on Utilizing Artificial Intelligence in Lateral Resisting Systems of Buildings DOI
Yasir Abduljaleel, Fathoni Usman, Agusril Syamsir

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 27, 2025

Language: Английский

Citations

0