Abstract
This paper discusses a novel approach for nonlinear parameter identification of structures. An inverse problem was formulated as an optimization problem, using two objective functions in time domain. The first objective function is formulated as an error between measured acceleration and predicted acceleration of the model. While the second objective function minimizes the substructure Instantaneous Power Flow Balance, which is the sum of input power, dissipated power, transmitted power and time rate of kinetic and strain energy to zero. Here a cubic nonlinearity in spring (Duffing equation) and a quadratic nonlinearity in damper are used to model the nonlinear system. Numerical simulations were performed on a 10-DOF nonlinear system under harmonic excitation using Particle Swarm Optimization tool under noise-free and 5% noisy cases. Identified results are compared in terms of mean absolute percentage error, with other methods in nonlinear parameter identification available in literature. Simulation results show the accuracy of proposed method in nonlinear parameter identification even at high noise contamination cases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kerschen, G., et al.: Past, present and future of nonlinear system identification in structural dynamics. Mech. Syst. Signal Process. 20(3), 505–592 (2006)
Noel, J.-P., Kerschen, G.: Nonlinear system identification in structural dynamics: 10 more years of progress. Mech. Syst. Signal Process. 83, 2–35 (2017)
Tomlinson, G.R., Worden, K.: Nonlinearity in Structural Dynamics: Detection, Identification and Modelling. CRC Press (2000)
Koh, C.G., See, L.M., Balendra, T.: Estimation of structural parameters in time domain: a substructure approach. Earthq. Eng. Struct. Dyn. 20(8), 787–801 (1991)
Koh, C.G., Hong, B., Liaw, C.Y.: Sub structural and progressive structural identification methods. Eng. Struct. 25(12), 1551–1563 (2003)
Varghese, C.K., Shankar, K.: Damage identification using combined transient power flow balance and acceleration matching technique. Structural Control and Health Monitoring. 21(2), 135–155 (2014)
Koh, C.G., Shankar, K.: Substructural identification method without interface measurement. J. Eng. Mech. 129(7), 769–776 (2003)
Mace, B.R.: Power flow between two continuous one-dimensional subsystems: a wave solution. J. Sound Vib. 154(2), 289–319 (1992)
Stephen, N.G.: On energy harvesting from ambient vibration. J. Sound Vib. 293(1–2), 409–425 (2006)
Varghese, C.K., Shankar, K.: Identification of structural parameters using combined power flow and acceleration approach in a substructure. Int. J. Eng. Technol. Innov. 1(1), 65–79 (2016)
Kumar, R.K., Shankar, K.: Parametric identification of structures with nonlinearities using global and substructure approaches in the time domain. Adv. Struct. Eng. 12(2), 195–210 (2009)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory, Micro Machine and Human Science, 1995. MHS’95, Proceedings of the Sixth International Symposium on. IEEE, 1995
Perez, R.L., Behdinan, K.: Particle swarm approach for structural design optimization. Comput. Struct. 85(19–20), 1579–1588 (2007)
Xue, S., Tang, H., Zhou, J.: Identification of structural systems using particle swarm optimization. J. Asian Archit. Build. Eng. 8(2), 517–524 (2009)
Clough, R.W., Penzien, J.: Dynamics of Structures, Second edn. McGraw-Hill, New York, USA, New York (1993)
Rao, S.S.: Engineering Optimization, Theory and Practice, Fourth edn. Wiley, New York (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Anish, R., Shankar, K. (2021). Parametric Identification of Nonlinear Structures Using Particle Swarm Optimization Based on Power Flow Balance Criteria. In: Awrejcewicz, J. (eds) Perspectives in Dynamical Systems III: Control and Stability. DSTA 2019. Springer Proceedings in Mathematics & Statistics, vol 364. Springer, Cham. https://doi.org/10.1007/978-3-030-77314-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-77314-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77313-7
Online ISBN: 978-3-030-77314-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)