Seismic Performance and Configuration Assessment of Deficient Steel Frames Equipped with Buckling Restrained Braces

Document Type : Regular Paper

Authors

1 M.Sc., Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates

2 Ph.D., Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates

3 Ph.D., Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, United Arab Emirates

Abstract

Integrating Buckling Restrained Braces (BRBs) into seismic-resistant structural frameworks presents a sophisticated approach to improving seismic performance. Despite the breadth of research conducted in this area, a noticeable gap persists in comprehending the optimal deployment of BRBs within steel frames that exhibit deficiencies to attain maximal structural efficiency and resilience in seismic events. To address this gap, the present study examines the most efficacious configurations of BRBs. Employing a methodological framework that encompasses the design, modeling, and analysis of twenty-four steel frames demonstrating deficiencies and outfitted with BRBs in varied configurations, this investigation utilizes nonlinear response history analysis as its core analytical tool. This comparative analysis examines eight distinct BRB configurations against a reference scenario devoid of BRBs to identify which most effectively augments seismic resistance. The outcomes derived from the nonlinear response history analysis underscore the pronounced influence of BRB configurations and geometrical variations on critical parameters, including frame weight, base shear, overturning moment, and lateral displacement at the story level. Case C2 was identified as the optimal configuration due to its balanced combination of enhanced performance and weight reduction, making it a reasonable choice for structural efficiency.

Graphical Abstract

Seismic Performance and Configuration Assessment of Deficient Steel Frames Equipped with Buckling Restrained Braces

Highlights

  • This work aims to examine the applicability of M5 decision tree machine learning algorithms to predict liquefaction vulnerability.
  • Both classification and regression models have been developed to determine nonlinear relationships between the physical properties of the soil.
  • In this study, 200 data sets were used for different divisional approaches such as grid search cross validation (Gridsearch Cv), kfold clustering means, and fuzzy clustering means (FCM) to develop successful regression and classification models.
  • It was found that the developed M5 decision tree regression model shows R = 0.9471 for testing and R = 0.9297 for training.
  • The developed M5 decision tree classification model shows an accuracy of 95% for testing and 98.75% for training.
  • The Kfold cross-validation technique predicted a more accurate value compared to other divisional approaches.

Keywords

Main Subjects

References

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History

  • Receive Date: 17 April 2024
  • Revise Date: 24 September 2024
  • Accept Date: 30 October 2024

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