Cost-effective multi-objective optimal positioning of magnetorheological dampers and active actuators in large nonlinear structures

Abstract

The optimal number and location of control devices not only play a major role in an effective structural control system but also lead to a cost-effective design. This article presents a multi-objective optimization method based on a new genetic algorithm for simultaneous finding of the optimal number and placement of actuators and magnetorheological dampers, in active and semi-active vibration control of structures. The proposed strategy considers three objective functions to be minimized through optimization, including peak inter-storey drift ratio, peak acceleration and peak base shear force to make sure both human comfort and safety of the structure are guaranteed. Also, by choosing a pre-defined level of performance on dynamic responses of a structure, the designer can decide on decreasing or increasing the number of control devices in a systematic way and minimize the control cost. The approach is then validated through a nonlinear 20-storey benchmark problem. The results from active control system show how a problem that was initially solved with 25 actuators can be solved with less than a quarter of those actuators, having similar results in terms of aforementioned indices. The optimal distribution of different numbers of magnetorheological dampers in the same benchmark building is also studied in this article and compared to those obtained from actuators. Due to highly nonlinear behaviour of these devices, and also the complexity of the under-study benchmark structure, few reported researches have been conducted in this area. Also, the comparison between optimal places of active and semi-active control devices in the same structure has hitherto not been reported in the open literature.

A genetic algorithm–based design approach for smart base isolation systems

“…GA developed by Holland (1975) is a powerful and capable optimization tool which has been used to generate useful solutions for complicated optimization problems in different fields of engineering. In designing structural control systems, GAs have been previously applied for optimal design of fuzzy controllers (Yan and Zhou, 2006), determining the optimum parameters of TMDs (Hadi and Arfiadi, 1998; Mohebbi and Joghataie, 2012), or MTMDs (Mohebbi et al, 2015b) for linear and nonlinear frames, designing optimal MR dampers (Xue et al, 2011), optimal design of AMDs (Mohebbi et al, 2015c) as well as optimal design and placement of sensors or actuators on structures for active control of structures (Abdullah et al, 2001; Askari et al, 2017; Joghataie and Mohebbi, 2012).…”

Seismic Response Control of Structures with Stiffness Irregularities through Optimally Placed Active Tendons using Multiobjective Genetic Algorithm

“…For the same purpose Liu et al [12] used a discrete nonlinear optimization method and genetic algorithm. Askari et al [13] used multi-objective genetic algorithm in active control and magneto rheological dampers in semi-active control simultaneously. From the above discussion, it is found that most of the research done on irregular structures is based on their performance in a seismic event and the seismic structural control of irregular structures is yet to be fully investigated.…”

Multiobjective optimal placement of active tendons to control irregular multistory buildings with soil–structure interaction

“…The sensitivities of the four optimization criteria with respect to different earthquake records were explored. Askari et al found the optimal number and placement of actuators and magnetorheological dampers in active and semiactive vibration control of structures, simultaneously, using a multiobjective optimization method based on a new GA.…”