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Steel and Composite Structures
  Volume 44, Number 1, July10 2022 , pages 49-63
DOI: https://doi.org/10.12989/scs.2022.44.1.049
 


Development of ensemble machine learning models for evaluating seismic demands of steel moment frames
Hoang D. Nguyen, JunHee Kim and Myoungsu Shin

 
Abstract
    This study aims to develop ensemble machine learning (ML) models for estimating the peak floor acceleration and maximum top drift of steel moment frames. For this purpose, random forest, adaptive boosting, gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) models were considered. A total of 621 steel moment frames were analyzed under 240 ground motions using OpenSees software to generate the dataset for ML models. From the results, the GBRT and XGBoost models exhibited the highest performance for predicting peak floor acceleration and maximum top drift, respectively. The significance of each input variable on the prediction was examined using the best-performing models and Shapley additive explanations approach (SHAP). It turned out that the peak ground acceleration had the most significant impact on the peak floor acceleration prediction. Meanwhile, the spectral accelerations at 1 and 2 s had the most considerable influence on the maximum top drift prediction. Finally, a graphical user interface module was created that places a pioneering step for the application of ML to estimate the seismic demands of building structures in practical design.
 
Key Words
    earthquake engineering; ensemble learning models; machine learning; seismic demands; steel moment frames
 
Address
Hoang D. Nguyen:Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil,
Eonyang-eup, Ulju-gun, Ulsan, South Korea

JunHee Kim:Department of Architecture and Architectural Engineering, Yonsei University, 50 Yonseiro, Seadaemun-gu, Seoul 120-749, South Korea

Myoungsu Shin:Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil,
Eonyang-eup, Ulju-gun, Ulsan, South Korea
 

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