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<p>This paper presents a novel ensemble learning framework that integrates Adaptive Boosting Regression Threshold (AdaBoost.RT) with weighted extreme learning machines (WELMs) to improve seismic performance in intelligent control systems. In our approach, the Imperialist Competitive Algorithm (ICA) is used to optimize the relative error threshold of AdaBoost.RT, and WELMs are selected as base predictors due to their fast-training times and robustness in handling nonlinear phenomena. The proposed framework incorporates sample weights into the output weights of the ELMs and updates them iteratively, effectively capturing complex dynamic behaviors, including those arising from soil–structure interaction (SSI) under stochastic excitations. Comparative analyses with established ensemble learning techniques demonstrate that the presented optimized, fast, and efficient model achieves superior predictive accuracy, generalization capabilities, and computational efficiency. The methodology is validated through extensive simulations and experimental data, showcasing its potential for addressing smart control challenges in engineering applications. This research not only advances the state of the art in ensemble learning for active control systems but also contributes to the broader field of nonlinear dynamics by providing a reliable, efficient, and robust tool for system identification and vibration control in oscillating systems.</p>

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A New Weighting Scheme for Ensemble Learning in Seismic Vibration Control of High-Rise Buildings with Soil–Structure Interaction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • Javad Palizvan Zand,
  • Javad Katebi,
  • Arman Atasoy

摘要

This paper presents a novel ensemble learning framework that integrates Adaptive Boosting Regression Threshold (AdaBoost.RT) with weighted extreme learning machines (WELMs) to improve seismic performance in intelligent control systems. In our approach, the Imperialist Competitive Algorithm (ICA) is used to optimize the relative error threshold of AdaBoost.RT, and WELMs are selected as base predictors due to their fast-training times and robustness in handling nonlinear phenomena. The proposed framework incorporates sample weights into the output weights of the ELMs and updates them iteratively, effectively capturing complex dynamic behaviors, including those arising from soil–structure interaction (SSI) under stochastic excitations. Comparative analyses with established ensemble learning techniques demonstrate that the presented optimized, fast, and efficient model achieves superior predictive accuracy, generalization capabilities, and computational efficiency. The methodology is validated through extensive simulations and experimental data, showcasing its potential for addressing smart control challenges in engineering applications. This research not only advances the state of the art in ensemble learning for active control systems but also contributes to the broader field of nonlinear dynamics by providing a reliable, efficient, and robust tool for system identification and vibration control in oscillating systems.