Seismic active control by neutral networks

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A study on the application of artificial neural networks (ANNs) to active structural control under seismic loads is carried out. The structure considered is a single-degree-of-freedom (SDF) system with an active bracing device. The control force is computed by a trained neural network. The feedforward neural network architecture and an adaptive backpropagation training algorithm is used in the study. The neural net is trained to reproduce the function that represents the response-excitation relationship of the SDF system under seismic loads. The input-output training patterns are generated randomly. In the backpropagation training algorithm, the learning rate is determined by ensuring the ... continued below

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15 p.

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Tang, Yu December 31, 1995.

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Description

A study on the application of artificial neural networks (ANNs) to active structural control under seismic loads is carried out. The structure considered is a single-degree-of-freedom (SDF) system with an active bracing device. The control force is computed by a trained neural network. The feedforward neural network architecture and an adaptive backpropagation training algorithm is used in the study. The neural net is trained to reproduce the function that represents the response-excitation relationship of the SDF system under seismic loads. The input-output training patterns are generated randomly. In the backpropagation training algorithm, the learning rate is determined by ensuring the decrease of the error function at each epoch. The computer program implemented is validated by solving the classification of the XOR problem. Then, the trained ANN is used to compute the control force according to the control strategy. If the control force exceeds the actuator`s capacity limit, it is set equal to that limit. The concept of the control strategy employed herein is to apply the control force at every time step to cancel the system velocity induced at the preceding time step so that the gradual rhythmic buildup of the response is destroyed. The ground motions considered in the numerical example are the 1940 El Centro earthquake and the 1979 Imperial Valley earthquake in California. The system responses with and without the control are calculated and compared. The feasibility and potential of applying ANNs to seismic active control is asserted by the promising results obtained from the numerical examples studied.

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15 p.

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OSTI as DE96005266

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  • ANNIE `95: artificial neural networks in engineering, St. Louis, MO (United States), 12-15 Nov 1995

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  • Other: DE96005266
  • Report No.: ANL/RE/CP--86387
  • Report No.: CONF-9511121--3
  • Grant Number: W-31109-ENG-38
  • Office of Scientific & Technical Information Report Number: 205063
  • Archival Resource Key: ark:/67531/metadc667344

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  • December 31, 1995

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  • June 29, 2015, 9:42 p.m.

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  • Dec. 16, 2015, 3:51 p.m.

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Tang, Yu. Seismic active control by neutral networks, article, December 31, 1995; Illinois. (digital.library.unt.edu/ark:/67531/metadc667344/: accessed September 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.