PSI - Issue 64

Amir Shamsaddinlou et al. / Procedia Structural Integrity 64 (2024) 360–367 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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Through a detailed comparative analysis of various cost functions, including the relatively controlled drift response, the transfer function of acceleration response, and particularly the pole placement scheme, our research emphasizes the superior performance of the pole placement strategy in optimizing TMD parameters. The findings illuminate the distinctive advantage of employing the pole placement scheme in the design of TMDs, offering a more stable and robust solution to seismic challenges. This method contributes to a deeper understanding of structural dynamics and helps develop more sophisticated and reliable design strategies in structural engineering. For future works, it would be valuable to explore integrating advanced computational techniques and machine learning algorithms to further refine the pole placement optimization process. Exploring the application of this scheme on a broader array of structural types and considering different seismic intensity models could also expand the scope of its effectiveness. Additionally, real-world implementation and testing of TMD designs optimized through this method would be invaluable in validating the theoretical models and contributing to the evolution of practical seismic mitigation solutions. References Bekdaş, G. and Nigdeli, S.M., 2011. Estimating optimum parameters of tuned mass dampers using harmony search. Engineering Str uctures. 33, 2716-2723. Cheng, F.Y., Jiang, H. and Lou, K., 2008. Smart structures: innovative systems for seismic response control, CRC press. De Domenico, D., Impollonia, N. and Ricciardi, G., 2018. Soil-dependent optimum design of a new passive vibration control system combining seismic base isolation with tuned inerter damper. Soil Dynamics and Earthquake Engineering. 105, 37-53. De Domenico, D., Qiao, H., Wang, Q., Zhu, Z. and Marano, G., 2020. Optimal design and seismic performance of Multi ‐ Tuned Mass Damper Inerter (MTMDI) applied to adjacent high ‐ rise buildings. The Structural Design of Tall and Special Buildings. 29, e1781. Den Hartog, J.P., 1985. Mechanical vibrations, Courier Corporation. Dorigo, M., Birattari, M. and Stutzle, T., 2006. Ant colony optimization. IEEE computational intelligence magazine. 1, 28-39. Fahimi Farzam, M. and Kaveh, A., 2020. Optimum design of tuned mass dampers using colliding bodies optimization in frequency domain. Iranian Journal of Science and Technology, Transactions of Civil Engineering. 44, 787-802. Geem, Z.W., Kim, J.H. and Loganathan, G.V., 2001. A new heuristic optimization algorithm: harmony search. simulation. 76, 60-68. Holland, J.H., 1992. Genetic algorithms. Scientific american. 267, 66-73. Kaveh, A., Mohammadi, S., Hosseini, O.K., Keyhani, A. and Kalatjari, V., 2015. Optimum parameters of tuned mass dampers for seismic applications using charged system search. Iranian Journal of Science and Technology. Transactions of Civil Engineering. 39, 21. Kaveh, A. and Talatahari, S., 2010. A novel heuristic optimization method: charged system search. Acta mechanica. 213, 267-289. Kennedy, J. and Eberhart, R., 1995. Particle swarm optimization, Proceedings of ICNN'95-international conference on neural networks. ieee, pp. 1942-1948. Lee, C.-L., Chen, Y.-T., Chung, L.-L. and Wang, Y.-P., 2006. Optimal design theories and applications of tuned mass dampers. Engineering structures. 28, 43-53. Marrazzo, P.R., Montuori, R., Nastri, E. and Benzoni, G., 2024. Advanced seismic retrofitting with high-mass-ratio Tuned Mass Dampers. Soil Dynamics and Earthquake Engineering. 179, 108544. Mirjalili, S., 2016. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems. 96, 120-133. Mirjalili, S., Mirjalili, S.M. and Lewis, A., 2014. Grey wolf optimizer. Advances in engineering software. 69, 46-61. Ogata, K., 1999. Modern control engineering. Book Reviews. 35, 1184. Raeesi, F., Shirgir, S., Azar, B.F., Veladi, H. and Ghaffarzadeh, H., 2020. Enhanced salp swarm algorithm based on opposition learning and merit function methods for optimum design of MTMD. Earthquakes and Structures. 18, 719-730. Saremi, S., Mirjalili, S. and Lewis, A., 2017. Grasshopper optimisation algorithm: theory and application. Advances in engineering software. 105, 30-47. Shamsaddinlou, A., Shirgir, S., Hadidi, A. and Azar, B.F., 2023. An efficient reliability-based design of TMD & MTMD in nonlinear structures under uncertainty, Structures. Elsevier, pp. 258-274. Talatahari, S. and Azizi, M., 2021. Chaos game optimization: a novel metaheuristic algorithm. Artificial Intelligence Review. 54, 917-1004.