Identification of Passive Devices for Vibration Control by Evolutionary Algorithms

Passive devices for vibration control (i.e., dampers and isolators) are widely adopted in many areas of engineering. For instance, such devices now provide reliable and affordable solutions in the seismic protection of industrial machines, technical equipment, buildings, and bridges. Their main advantages are simplicity and limited costs if compared to other strategies of vibration control, which also explains why the use of passive devices is becoming so popular in many civil engineering applications. The design of earthquake-resistant structures requires the assessment of the protection system’s performance by assuming specific mathematical laws for the adopted devices. These, in turn, depend on the mechanical parameters that have to be tuned properly. As a consequence, a reliable parametric identification of passive devices for vibration control is a critical point in the design process. In this chapter, a procedure is described for the dynamic identification of passive control devices through laboratory tests and evolutionary algorithms. The methodology consists of first, using standardized experimental tests, where a predefined loading condition is imposed by an external actuator; second, using soft computing numerical techniques to identify the mechanical parameters of the candidate mathematical law by minimizing the difference between the time histories of the experimental and analytical dynamic loads. The proposed procedure can be applied to a wide range of models because of its inherent stability and low computational cost and allows comparing different mechanical laws by ranking their agreement with experimental data. Final results demonstrate the effectiveness of the proposed strategy.

[1]  Giuseppe Quaranta,et al.  Modified Genetic Algorithm for the Dynamic Identification of Structural Systems Using Incomplete Measurements , 2011, Comput. Aided Civ. Infrastructure Eng..

[2]  Alessandro Palmeri,et al.  Correlation coefficients for structures with viscoelastic dampers , 2006 .

[3]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[4]  B Samali,et al.  Bouc-Wen model parameter identification for a MR fluid damper using computationally efficient GA. , 2007, ISA transactions.

[5]  Nicos Makris,et al.  Comparison of Modeling Approaches for Full-scale Nonlinear Viscous Dampers , 2008 .

[6]  Giuseppe Marano,et al.  Stochastic optimum design criterion for linear damper devices for seismic protection of buildings , 2007 .

[7]  A. Gandomi,et al.  Mixed variable structural optimization using Firefly Algorithm , 2011 .

[8]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[9]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[10]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[11]  Amir Hossein Gandomi,et al.  Erratum to: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2013, Engineering with Computers.

[12]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[13]  Giuseppe Quaranta,et al.  Parameters identification of Van der Pol–Duffing oscillators via particle swarm optimization and differential evolution , 2010 .

[14]  G. Marano,et al.  Genetic-Algorithm-Based Strategies for Dynamic Identification of Nonlinear Systems with Noise-Corrupted Response , 2010 .

[15]  J R Saunders,et al.  A particle swarm optimizer with passive congregation. , 2004, Bio Systems.

[16]  Gloria Terenzi,et al.  Dynamics of SDOF Systems with Nonlinear Viscous Damping , 1999 .

[17]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[18]  Rong-Fong Fung,et al.  Using the modified PSO method to identify a Scott-Russell mechanism actuated by a piezoelectric element , 2009 .

[19]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[20]  Giuseppe Quaranta,et al.  Genetic Algorithms in Mechanical Systems Identification: State-of-the-Art Review , 2009 .

[21]  Leandro dos Santos Coelho,et al.  Fuzzy Identification Based on a Chaotic Particle Swarm Optimization Approach Applied to a Nonlinear Yo-yo Motion System , 2007, IEEE Transactions on Industrial Electronics.

[22]  F. Rüdinger Tuned mass damper with nonlinear viscous damping , 2007 .

[23]  He-sheng Tang,et al.  Differential evolution strategy for structural system identification , 2008 .

[24]  Francesco Ricciardelli,et al.  Fatigue analyses of buildings with viscoelastic dampers , 2006 .

[25]  Jiang Chuanwen,et al.  A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation , 2005, Math. Comput. Simul..

[26]  F. Rüdinger Optimal vibration absorber with nonlinear viscous power law damping and white noise excitation , 2006 .