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Structural Engineering and Mechanics Volume 88, Number 6, December25 2023 , pages 535-549 DOI: https://doi.org/10.12989/sem.2023.88.6.535 |
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Optimal deep machine learning framework for vibration mitigation of seismically-excited uncertain building structures |
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Afshin Bahrami Rad, Javad Katebi and Saman Yaghmaei-Sabegh
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| Abstract | ||
| Deep extreme learning machine (DELM) and multi-verse optimization algorithms (MVO) are hybridized for designing an optimal and adaptive control framework for uncertain buildings. In this approach, first, a robust model predictive control (RMPC) scheme is developed to handle the problem uncertainty. The optimality and adaptivity of the proposed controller are provided by the optimal determination of the tunning weights of the linear programming (LP) cost function for clustered external loads using the MVO. The final control policy is achieved by collecting the clustered data and training them by DELM. The efficiency of the introduced control scheme is demonstrated by the numerical simulation of a ten-story benchmark building subjected to earthquake excitations. The results represent the capability of the proposed framework compared to robust MPC (RMPC), conventional MPC (CMPC), and conventional DELM algorithms in structural motion control. | ||
| Key Words | ||
| artificial intelligence; deep machine learning; intelligent control; optimization; soft computing | ||
| Address | ||
| Afshin Bahrami Rad, Javad Katebi and Saman Yaghmaei-Sabegh: Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran | ||