Reliability based optimum design of Tuned Mass Damper in seismic vibration control of structures with bounded uncertain parameters

Chakraborty, Subrata; Roy, B. K.

doi:10.1016/j.probengmech.2010.07.007

Cited by 112 publications

(69 citation statements)

References 27 publications

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“…Sizing optimization results of random structures are more conservative than those of deterministic structures, and optimization results of deterministic structures can not satisfy dynamic 1m 1m 1m 1m (20) 9 10…”

Section: Discussionmentioning

confidence: 99%

“…However, works related to RBO for structures with random parameters under dynamic load are limited, among which some measures are taken to avoid the direct solution of the structural dynamic reliability of dynamic response [17][18][19][20]. Rao [18] discussed methodology of formulating the optimum design problem for structural systems with random parameters and subjected to random vibration as a mathematical programming problem, and the probabilistic constraints are specified for individual failure modes and the time parameter appearing in the random vibration based constraints is eliminated by replacing the probabilities of failure by suitable upper bounds, by which the RBO is greatly simplified.…”

Section: Introductionmentioning

confidence: 99%

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Reliability-based optimization of trusses with random parameters under dynamic loads

Wriggers

Gao

et al. 2011

Comput Mech

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In this work, a reliability-based optimization technique is addressed to obtain the minimum mean value of random mass of the structures with random parameters under stationary stochastic process excitation. The challenge of the problem lies in randomness involved from both structural parameters and dynamic load, which renders the structural reliability becoming the random dynamic reliability of the first passage problem. In order to obtain minimum mean value of random gross mass, element and system dynamic reliability constraints are constructed, respectively, and the structural sizing and shape optimization models based on the dynamic reliability are then presented. Moreover, among two optimal strategies proposed for optimization models, the second one can effectively reduce the workload by avoiding the computation of the variance of the dynamic response during the iterative process. Finally, the implementation of three examples is discussed to display the feasibility and validity of optimization technique given.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability-based optimization of trusses with random parameters under dynamic loads

Wriggers

Gao

et al. 2011

Comput Mech

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“…Operations on these functions are time consuming and computationally cumbersome, which is why we employ equivalent models. The application of equivalent models is widespread in design optimization [1][2][3][4][5][6], design space exploration [7][8][9][10], sensitivity analysis [11][12][13], and reliability analysis [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Multivariate function approximations using the D-MORPH algorithm

Chakraborty

Chowdhury

2015

Applied Mathematical Modelling

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a b s t r a c tMost real-life problems are computationally burdensome and time consuming, so we have to rely on approximate functions to represent the original functions with a specific level of accuracy. In this study, we propose a new method for approximating multivariate functions based on the diffeomorphic modulation under observable response preserving homotopy (D-MORPH) algorithm. D-MORPH is a regression technique that was originally developed for solving differential equation. Two distinct approaches using the proposed method are described, where one operates over the whole domain and the other operates by sub-dividing the domain into a finite number of sub-domains. This technique is based on a cost/objective function, which depends on a weight matrix. We also introduce a new weight matrix, which dramatically reduces the prediction error and improves the prediction accuracy. The potential of the proposed approach for approximating multivariate functions is illustrated using eight mathematical functions and two practical problems. Furthermore, a comparative assessment with other methods is provided to demonstrate the elegance of the proposed method.

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“…Hoang et al [9] conducted a sensitivity study and concluded that higher mass of TMD is relatively less sensitive to detuning by system uncertainties. Chakraborty and Roy [10] proposed reliability based design of TMD for varying levels of uncertainties. In their study, probability distribution function (PDF) of random variables were not incorporated.…”

Section: Introductionmentioning

confidence: 99%

Robust Design of TMD for Vibration Control of Uncertain Systems Using Adaptive Response Surface Method

Rathi

Chakraborty

2014

Advances in Structural Engineering

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The effect of randomness in system parameters on robust design of tuned mass damper (TMD) is examined in this work. For this purpose, mean and standard deviation based robust design optimization (RDO) scheme is suggested. The performance of TMD is evaluated using the percentage reduction of the root mean square (RMS) of the output displacement. Adaptive response surface method (ARSM) is used for the optimization and for the estimation of first two moments. In this context, moving least square (MLS) based regression technique is used for better fitting of the response surface. A comparative numerical study is conducted to show the effectiveness of the proposed method to improve the reliability of the controller.

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Reliability based optimum design of Tuned Mass Damper in seismic vibration control of structures with bounded uncertain parameters

Cited by 112 publications

References 27 publications

Reliability-based optimization of trusses with random parameters under dynamic loads

Reliability-based optimization of trusses with random parameters under dynamic loads

Multivariate function approximations using the D-MORPH algorithm

Robust Design of TMD for Vibration Control of Uncertain Systems Using Adaptive Response Surface Method

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