A STFT semiactive controller for base isolated buildings with variable stiffness isolation systems

Narasimhan, Sriram; Nagarajaiah, Satish

doi:10.1016/j.engstruct.2004.11.010

Cited by 85 publications

(41 citation statements)

References 21 publications

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“…The performance of the SOI-WNN is compared against 3 xed-input strategies optimized over the search space τ = [1,40] and d = [1, 10]:…”

Section: Results -Wind Excitationmentioning

confidence: 99%

“…Despite the advancements of this technology, broad implementation of those control mechanisms for eective structural hazard mitigation is rare [1]. This is due to a lack of acceptability, impeded by the lack of applicability of control schemes [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…• τ = 31 and d = 8, which are the optimal xed input parameters obtained within the search space τ = [1,40] and d = [1,10] while simulating the system with forcing.…”

Section: Synthetic Examplementioning

confidence: 99%

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Self-organizing input space for control of structures

Laflamme

Slotine

Connor

2012

Smart Mater. Struct.

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We propose a novel type of neural networks for structural control, which comprises an adaptive input space. This feature is purposefully designed for sequential input selection during adaptive identification and control of nonlinear systems, which allows the input space to be organized dynamically, while the excitation is occurring. The neural network has the main advantages of (1) automating the input selection process for time series that are not known a priori; (2) adapting the representation to nonstationarities; and (3) using limited observations. The algorithm designed for the adaptive input space assumes local quasi-stationarity of the time series, and embeds local maps sequentially in a delay vector using the embedding theorem. The input space of the representation, which in our case is a wavelet neural network, is subsequently updated. We demonstrate that the neural net has the potential to significantly improve convergence of a black-box model in adaptive tracking of a nonlinear system. Its performance is further assessed in a full-scale simulation of an existing civil structure subjected to nonstationary excitations (wind and earthquakes), and shows the superiority of the proposed method. Disciplines Civil Engineering | Construction Engineering and Management | Environmental Engineering | Structural EngineeringComments This is a manuscript from an article from Smart Materials and Structures,21 (115015) We propose a novel type of neural networks for structural control, which comprises an adaptive input space. This feature is purposefully designed for sequential input selection during adaptive identication and control of nonlinear systems, which allows the input space to be organized dynamically, while the excitation is occurring. The neural network has the main advantages of 1) automating the input selection process for time series that are not known a priori; 2) adapting the representation to nonstationarities; and 3) using limited observations. The algorithm designed for the adaptive input space assumes local quasi-stationarity of the time series, and embeds local maps sequentially in a delay vector using the embedding theorem. The input space of the representation, which in our case is a wavelet neural network, is subsequently updated. We demonstrate that the neural net has the potential to signicantly improve convergence of a black-box model in adaptive tracking of a nonlinear system. Its performance is further assessed in a full-scale simulation of an existing civil structure subjected to nonstationary excitations (wind and earthquakes), and shows the superiority of the proposed method.

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“…The performance of the SOI-WNN is compared against 3 xed-input strategies optimized over the search space τ = [1,40] and d = [1, 10]:…”

Section: Results -Wind Excitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Self-organizing input space for control of structures

Laflamme

Slotine

Connor

2012

Smart Mater. Struct.

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“…Most prior research into active or semi-actively controlled structures F o r P e e r R e v i e w employed either sinusoidal, random, single, or selected earthquake excitations to illustrate the benefits of control [8,12,[34][35][36][37]. As the characteristics of seismic excitation are entirely random and vary significantly unlike other types of vibrational excitation, the use of a number of multiple time history records over a range of seismic levels is essential for effective controller evaluation.…”

Section: Earthquake Suites and Statistical Assessmentmentioning

confidence: 99%

Semi‐active tuned mass damper building systems: Design

Chey

Chase

Mander

et al. 2009

Earthq Engng Struct Dyn

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SUMMARYPassive and Semi-Active Tuned Mass Damper (PTMD and SATMD) building systems are proposed to mitigate structural response due to seismic loads. The structure's upper portion self plays a role either as a tuned mass passive damper or a semi-active resetable device is adopted as a control feature for the PTMD, creating a SATMD system. Twodegree-of-freedom (2-DOF) analytical studies are employed to design the prototype structural system, specify its element characteristics and effectiveness for seismic responses, including defining the resetable device dynamics. The optimal parameters are derived for the large mass ratio by numerical analysis. For the SATMD building system the stiffness of the resetable device design is combined with rubber bearing stiffness. From parametric studies, effective practical control schemes can be derived for the SATMD system. To verify the principal efficacy of the conceptual system, the controlled system response is compared to the response spectrum of the earthquake suites used. The control ability of the SATMD scheme is compared to that of an uncontrolled (No TMD) and an ideal passive tuned mass damper (PTMD) building systems for multi-level seismic intensity.

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“…Since active control systems require high external power supplies, semiactive systems have been proposed to replace them. Several semiactive mechanisms have been examined in combination with the base isolation system such as variable orifice damper [7,8] and variable stiffness system [9,10]. Weber et al [11] studied the performance of structure equipped with a semiactive friction pendulum against the seismic loading and determined the optimal friction coefficient of isolation system for minimizing the structure acceleration.…”

Section: Introductionmentioning

confidence: 99%

Effective Semiactive Base Isolation System under Multiple Earthquakes

Mohebbi

Dadkhah

2018

Advances in Civil Engineering

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A method is proposed to design an effective semiactive control system composed of a linear low damping base isolation and a supplemental magnetorheological (MR) damper when the structure subjected to multiple earthquakes. In the proposed design method, the parameters of semiactive control system have been determined based on minimizing the average of maximum response of isolated structure under multiple design ground motions. To select appropriate value for force related weighting parameter, defined in performance index, a range has been suggested for each design objective. For numerical simulations, a scaled three-story base-isolated frame subjected to different scaled real earthquakes as well as filtered white noise excitations and the proposed method has been applied to design semiactive base isolation system under multiple earthquakes.e results of numerical simulations have shown the capability of the proposed method in designing an effective semiactive base isolation system, the performance of which under multiple earthquakes has been almost close to the case that it is designed optimally for each earthquake separately. Also, under multiple earthquakes, using the passive-off and passive-on forms of MR damper can be recommended, respectively, regarding to the objectives of minimizing the maximum acceleration and base drift.

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A STFT semiactive controller for base isolated buildings with variable stiffness isolation systems

Cited by 85 publications

References 21 publications

Self-organizing input space for control of structures

Self-organizing input space for control of structures

Semi‐active tuned mass damper building systems: Design

Effective Semiactive Base Isolation System under Multiple Earthquakes

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