Semi-active vibration control of a non-collocated civil structure using evolutionary-based BELBIC

A buildings resilience to seismic activity can be increased by providing ways for the structure to dynamically counteract the effect of the Earth’s crust movements. This ability is fundamental in certain regions of the globe, where earthquakes are more frequent, and can be achieved using different strategies. State-of-the-art anti-seismic buildings have, embedded on their structure, mostly passive actuators such as base isolation, Tuned Mass Dampers (TMD) and viscous dampers that can be used to reduce the effect of seismic or even wind induced vibrations. The main disadvantage of this type of building vibration reduction strategies concerns their inability to adapt their properties in accordance to both the excitation signal or structural behaviour. This adaption capability can be promoted by adding to the building active type actuators operating under a closed-loop. However, these systems are substantially larger than passive type solutions and require a considerable amount of energy that may not be available during a severe earthquake due to power grid failure. An intermediate solution between these two extremes is the introduction of semi-active actuators such as magneto–rheological dampers. The inclusion of magneto–rheological actuators is among one of the most promising semi-active techniques. However, the overall performance of this strategy depends on several aspects such as the actuators number and location within the structure and the vibration sensors network. It can be the case where the installation leads to a non-collocated system which presents additional challenges to control. This paper proposes to tackle the problem of controlling the vibration of a non-collocated three-storey building by means of a brain–emotional controller tuned using an evolutionary algorithm. This controller will be used to adjust the stiffness coefficient of a magneto–rheological actuator such that the building’s frame oscillation under earthquake excitation, is mitigated. The obtained results suggest that, using this control strategy, it is possible to reduce the building vibration to secure levelsinfo:eu-repo/semantics/publishedVersio

Teaching-learning based optimization for parameter estimation of double tuned mass dampers

The classical methods for parameter estimation of tuned mass dampers are well known simple formulations, but these formulations are only suitable for multiple degree of freedom structures by considering a single mode. If special range limitation of tuned mass dampers and inherent damping of the main structure are considered, the best way to estimate the parameters is to use a numerical method. The numerical method must have a good convergence and computation time. In that case, metaheuristic methods are effective on the problem. Generally, metaheuristic method is inspired from a process of life and it is formulated for several steps in order to reach an optimal goal. Differently from the single tuned mass dampers, double tuned mass dampers can be also used for the reduction of vibrations. In civil structures, earthquake excitation is a major source of vibrations. In this study, optimum double tuned mass dampers are investigated for seismic structures by using a wide range of earthquake records for global optimum. As an optimization algorithm, teaching learning based optimization is employed. In this algorithm, the teaching and learning phases of a class are modified for optimization problems. The optimization of double tuned mass damper is more challenging than the single ones since the number of design variable is doubled and the design constraint about the stroke of the both masses must be considered. The proposed method is compared with the existing approaches and the methodology is feasible for parameter estimation of double tuned mass dampers

Particle swarming of sensor correction filters

Reducing the impact of seismic activity on the motion of suspended optics is essential for the operation of ground-based gravitational wave detectors. During periods of increased seismic activity, low-frequency ground translation and tilt cause the Advanced LIGO observatories to lose 'lock', reducing their duty cycles. This paper applies modern global-optimisation algorithms to aid in the design of the 'sensor correction' filter, used in the control of the active platforms. It is shown that a particle swarm algorithm that minimises a cost-function approximating the differential root mean squared velocity between platforms can produce control filters that perform better across most frequencies in the control bandwidth than those currently installed. These tests were conducted using training data from the LIGO Hanford Observatory seismic instruments and simulations of the Horizontal Access Module Internal Seismic Isolation platforms. These results show that new methods of producing control filters are ready for use at LIGO. The filters were implemented at LIGO's Hanford Observatory, and use the resulting data to refine the cost function. © 2020 IOP Publishing Ltd Printed in the U

Structural optimization in steel structures, algorithms and applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

H∞H_\infty optimization of multiple tuned mass dampers for multimodal vibration control

In this paper, a new computational method for the purpose of multimodal vibration mitigation using multiple tuned mass dampers is proposed. Classically, the minimization of the maximum amplitude is carried out using direct $H_\infty$ optimization. However, as shall be shown in the paper, this approach is prone to being trapped in local minima, in view of the nonsmooth character of the problem at hand. This is why this paper presents an original alternative to this approach through norm-homotopy optimization. This approach, combined with an efficient technique to compute the structural response, is shown to outperform direct $H_\infty$ optimization in terms of speed and performance. Essentially, the outcome of the algorithm leads to the concept of all-equal-peak design for which all the controlled peaks are equal in amplitude. This unique design is new with respect to the existing body of knowledge.Comment: This is a new version of a preprint previously named "All-equal-peak design of multiple tuned mass dampers using norm-homotopy optimization

Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

© 2016 Elsevier B.V. Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator