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Learning Models for Seismic-Induced Vibrations Optimal Control in Structures via Random Forests

Author

Listed:
  • Francesco Smarra

    (Università degli Studi dell’Aquila)

  • Giovanni Domenico Di Girolamo

    (Università degli Studi dell’Aquila)

  • Vincenzo Gattulli

    (Sapienza - University of Rome)

  • Fabio Graziosi

    (Università degli Studi dell’Aquila)

  • Alessandro D’Innocenzo

    (Università degli Studi dell’Aquila)

Abstract

Data-driven modeling of dynamical systems gathers attention in several applications; in conjunction with model predictive control, novel different identification techniques that merge machine learning and optimization are presented and compared with the purpose of reducing seismic response of frame structures and minimize control effort. Performance of neural network-, random forest- and regression tree-based identification algorithms in producing reliable models exploiting historical data coming from a real structure is shown. Peculiarities of each data-driven-based model emphasizing the strong potentialities of such approaches are highlighted, and it is shown in a simulative environment how, by slightly increasing the complexity of a model via random forests, we can reduce by half the active control effort with respect to the control computed exploiting regression trees-based models.

Suggested Citation

  • Francesco Smarra & Giovanni Domenico Di Girolamo & Vincenzo Gattulli & Fabio Graziosi & Alessandro D’Innocenzo, 2020. "Learning Models for Seismic-Induced Vibrations Optimal Control in Structures via Random Forests," Journal of Optimization Theory and Applications, Springer, vol. 187(3), pages 855-874, December.
    • Handle: RePEc:spr:joptap:v:187:y:2020:i:3:d:10.1007_s10957-020-01698-7
    DOI: 10.1007/s10957-020-01698-7
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    References listed on IDEAS

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    1. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    2. Qing Wang & Jianhui Wang & Xiaofang Huang & Li Zhang, 2017. "Semiactive Nonsmooth Control for Building Structure with Deep Learning," Complexity, Hindawi, vol. 2017, pages 1-8, November.
    3. Behl, Madhur & Smarra, Francesco & Mangharam, Rahul, 2016. "DR-Advisor: A data-driven demand response recommender system," Applied Energy, Elsevier, vol. 170(C), pages 30-46.
    4. Reni Suryanita & Harnedi Maizir & Hendra Jingga, 2017. "Prediction of Structural Response Based on Ground Acceleration Using Artificial Neural Networks," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 3(2), pages 74-83.
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