Learning models for seismic-induced vibrations optimal control in structures via random forests

DOI10.1007/S10957-020-01698-7zbMath1464.93023OpenAlexW3037139827MaRDI QIDQ2025721

Vincenzo Gattulli, Fabio Graziosi, Francesco Smarra, Giovanni Domenico Di Girolamo, Alessandro D'Innocenzo

Publication date: 14 May 2021

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10957-020-01698-7

zbMATH Keywords

earthquake engineeringpredictive controlrandom forestregression treedata-driventime-variant systems

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) System identification (93B30) Seismology (including tsunami modeling), earthquakes (86A15) Model predictive control (93B45)

Uses Software

• TensorFlow
• Keras

Cites Work

• 4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
• Semiactive nonsmooth control for building structure with deep learning
• Data-driven switching modeling for MPC using regression trees and random forests
• Predictive Control for Linear and Hybrid Systems
• Subspace model identification Part 1. The output-error state-space model identification class of algorithms
• Random forests

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