Building seismic behavior improvement using an optimal control algorithm

Munteanu, Ruben Iacob; Nica, George Bogdan; Calofir, Vasile; Iliescu, Sergiu Stelian; Sirbu, Octavian Tiberiu

doi:10.1109/aqtr49680.2020.9130005

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Jamil et al [5] presented the usage of neural network predicting controller as a unique method for vibration control of tall structures using an active tuned mass damper (ATMD) with a single degree of freedom. Munteanu et al [6] have investigated the active control technology and their benefits in the case of seismic structural behavior. Hiramoto et al [7] have developed an active vibration control strategy consisting of state-feedback and feedforward control and used multi-layer artificial neural network for generating future wave form of seismic disturbance.…”

Section: Introductionmentioning

confidence: 99%

Active seismic control using neural network

Gumudavelly,

Mehta,

Chaudhary

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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A novel active control algorithm is proposed in this present study. It is assumed that the shape of the time history of control force is same as that of the shape of time history of ground excitation. Based on the above assumption, control force is determined for a specific target reduction in displacement, velocity and acceleration. Time histories of ground motion compatible to response spectrum are generated for seismic zone three as per Indian seismic conditions. A neural network is trained to generate the desired control force for a specific target reduction. The results of the study show that the reduction provided by the proposed control algorithm matches well with the target reduction.

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Section: Introductionmentioning

confidence: 99%