Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Reliability analysis of portal frame subjected to varied lateral loads using machine learning

Reliability analysis of portal frame subjected to varied lateral loads using machine learning Structural reliability analysis has a vital significance in assessing the performance and safety of engineering structures. Traditional methods of reliability analysis often rely on deterministic models, which may not accurately represent the uncertainties and variability present in real-world scenarios. To address this limitation, the paper proposes the integration of machine learning techniques to enhance the accuracy and efficiency of reliability analysis for portal frames subjected to lateral loads. Portal frames are commonly used in low-rise buildings that require open and flexible spaces, such as industrial, commercial, agricultural, and storage facilities. The goal of this study is to find a reliable and efficient method that can accurately predict the structural response and failure probability of portal frames subjected to lateral forces. To estimate the displacement subjected to lateral forces, three hybrid random forest (RF) models have been developed in this study: random forest-dragonfly optimization algorithm (RF-DOA), random forest-sparrow search algorithm (RF-SSA) and random forest-whale optimization algorithm (RF-WOA). The displacement due to lateral forces has been efficiently predicted by all the proposed models. When the three models were compared, the RF-WOA showed best prediction and high accuracy during the testing phase. Also, the RF-WOA model outperformed the RF-DOA and RF-SSA algorithms, based on the outcomes of rank analysis, regression line analysis, and reliability analysis. Therefore, the RF-WOA model can be used as the most precise machine-learning algorithm to determine displacement subjected to lateral forces. Engineers and designers can benefit from the developed methodology in optimizing the analysis and design of portal frames, thereby enhancing the safety of structures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

Reliability analysis of portal frame subjected to varied lateral loads using machine learning

Loading next page...
 
/lp/springer-journals/reliability-analysis-of-portal-frame-subjected-to-varied-lateral-loads-8LXapiohRF

References (38)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-023-00893-x
Publisher site
See Article on Publisher Site

Abstract

Structural reliability analysis has a vital significance in assessing the performance and safety of engineering structures. Traditional methods of reliability analysis often rely on deterministic models, which may not accurately represent the uncertainties and variability present in real-world scenarios. To address this limitation, the paper proposes the integration of machine learning techniques to enhance the accuracy and efficiency of reliability analysis for portal frames subjected to lateral loads. Portal frames are commonly used in low-rise buildings that require open and flexible spaces, such as industrial, commercial, agricultural, and storage facilities. The goal of this study is to find a reliable and efficient method that can accurately predict the structural response and failure probability of portal frames subjected to lateral forces. To estimate the displacement subjected to lateral forces, three hybrid random forest (RF) models have been developed in this study: random forest-dragonfly optimization algorithm (RF-DOA), random forest-sparrow search algorithm (RF-SSA) and random forest-whale optimization algorithm (RF-WOA). The displacement due to lateral forces has been efficiently predicted by all the proposed models. When the three models were compared, the RF-WOA showed best prediction and high accuracy during the testing phase. Also, the RF-WOA model outperformed the RF-DOA and RF-SSA algorithms, based on the outcomes of rank analysis, regression line analysis, and reliability analysis. Therefore, the RF-WOA model can be used as the most precise machine-learning algorithm to determine displacement subjected to lateral forces. Engineers and designers can benefit from the developed methodology in optimizing the analysis and design of portal frames, thereby enhancing the safety of structures.

Journal

Asian Journal of Civil EngineeringSpringer Journals

Published: Feb 1, 2024

Keywords: Reliability analysis; Portal frame; Lateral load; Machine learning; Structural response; Failure probability; Hybrid RF models

There are no references for this article.