IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v187y2020i3d10.1007_s10957-020-01698-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-020-01698-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10957-020-01698-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    2. Liu, Yang & Yu, Nanpeng & Wang, Wei & Guan, Xiaohong & Xu, Zhanbo & Dong, Bing & Liu, Ting, 2018. "Coordinating the operations of smart buildings in smart grids," Applied Energy, Elsevier, vol. 228(C), pages 2510-2525.
    3. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    4. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    5. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    6. Giacomo Valente & Vittoriano Muttillo & Mirco Muttillo & Gianluca Barile & Alfiero Leoni & Walter Tiberti & Luigi Pomante, 2019. "SPOF—Slave Powerlink on FPGA for Smart Sensors and Actuators Interfacing for Industry 4.0 Applications," Energies, MDPI, vol. 12(9), pages 1-13, April.
    7. Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
    8. Filipe, Jorge & Bessa, Ricardo J. & Reis, Marisa & Alves, Rita & Póvoa, Pedro, 2019. "Data-driven predictive energy optimization in a wastewater pumping station," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    10. Arthur Stepchenko & Jurij Chizhov & Ludmila Aleksejeva, 2018. "Transfer of the data preprocessing parameters and fore- casting models," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(6), pages 214-221.
    11. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
    12. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
    13. Vo-Van Thanh & Wencong Su & Bin Wang, 2022. "Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch," Energies, MDPI, vol. 15(6), pages 1-19, March.
    14. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    15. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    16. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    17. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
    18. Suyang Zhou & Di He & Zhiyang Zhang & Zhi Wu & Wei Gu & Junjie Li & Zhe Li & Gaoxiang Wu, 2019. "A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    19. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    20. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joptap:v:187:y:2020:i:3:d:10.1007_s10957-020-01698-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.