Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods

Nowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and...

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Vydané v:Engineering applications of artificial intelligence Ročník 128; s. 107388
Hlavní autori: Asgarkhani, N., Kazemi, F., Jakubczyk-Gałczyńska, A., Mohebi, B., Jankowski, R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.02.2024
Predmet:
Buckling-restrained braced frame
Machine-learning algorithm
Residual interstory drift
Seismic failure probability
Seismic performance curve
Seismic retrofit
Seismic performance curve
Residual interstory drift
Machine-learning algorithm
Seismic retrofit
Seismic failure probability
Buckling-restrained braced frame
ISSN:0952-1976, 1873-6769
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Shrnutí:Nowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to provide a comprehensive tool for estimating seismic responses of Interstory Drift (ID) and RID with novel approaches to fulfill the shortcomings of existing formulas. The Machine Learning (ML) method is an interdisciplinary approach that makes it possible to solve these types of engineering problems. Therefore, the current study proposes ML algorithms to provide a prediction model for determining the seismic response, seismic performance curve, and seismic failure probability curve of BRBFs. To train ML-based prediction models, Nonlinear Time-History Analysis (NTHA) and Incremental Dynamic Analysis (IDA) were performed on the 2-, to 12-Story BRBFs subjected to 78 far-field ground motions, and 606944 data points were prepared for different prediction purposes. The results indicate that the considered approaches are justified. For instance, the proposed ML methods have the ability to predict the maximum ID, maximum RID and maximum roof ID with the accuracy of even 98.7%, 95.2%, and 93.8%, respectively, for the 4-Story BRBF. Moreover, a general preliminary estimation tool is introduced to provide predictions based on the input parameters considered in the study. [Display omitted] •Machine-Learning (ML) techniques used to predict maximum Interstory Drift (ID) and Residual Interstory Drift (RID) of BRBFs.•ML models were trained to build a surrogate model to assess seismic response, performance, and failure probability of BRBFs.•Statistical indicators showed that proposed Stacked ML model had higher performance and accuracy compared to others methods.•Proposed ML models can estimate the distribution of Interstory Drift (ID) and Residual Interstory Drift (RID) of BRBFs.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2023.107388