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


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.

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Correspondence to Giovanni Domenico Di Girolamo.

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Smarra, F., Girolamo, G.D.D., Gattulli, V. et al. Learning Models for Seismic-Induced Vibrations Optimal Control in Structures via Random Forests. J Optim Theory Appl (2020). https://doi.org/10.1007/s10957-020-01698-7

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  • Data-driven
  • Predictive control
  • Earthquake engineering
  • Regression tree
  • Random forest
  • Time-variant systems

Mathematics Subject Classification

  • 93B30
  • 62M20
  • 93xx
  • 93C30
  • 93C95