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Nonlinear mathematical modeling and optimum design of tuned mass dampers using adaptive dynamic harmony search algorithm
Author(s) -
Keshtegar Behrooz,
Etedali Sadegh
Publication year - 2018
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2163
Subject(s) - harmony search , nonlinear system , benchmark (surveying) , tuned mass damper , algorithm , computer science , mathematical optimization , damper , mathematics , engineering , control engineering , geodesy , geography , physics , quantum mechanics
Summary A novel adaptive dynamic harmony search (ADHS) algorithm is proposed based on the dynamical parameters that are adjusted using the previous results of the harmony memory with a simple formulation. The accuracy and efficiency of ADHS algorithm are compared with the several improved versions of harmony search through mathematical benchmark examples. The optimum design database of tuned mass damper (TMD) parameters for a damped main system under white‐noise base excitation is extracted by the ADHS algorithm for applicable engineering problem. Four mathematical models are calibrated using the nonlinear training approach‐based ADHS for approximating the optimum tuning TMD parameters. By considering the root mean square error and confidence index, a best nonlinear model is selected among the proposed models using ADHS training scheme and several existing empirical models. A 10‐story benchmark structural example under earthquake excitation is considered for validation of the proposed nonlinear model. The simulation results demonstrate that the ADHS provides more accurate and efficient results than the improved harmony search algorithms for mathematical benchmark examples. The proposed nonlinear model also performs with the efficient computational burdens compared with the optimization algorithms for optimum tuning of TMD parameters of a 10‐story structure, more accurately.