Abstract

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 levels.

Highlights

  • Earthquakes are a natural phenomena that has one of the most the severe impact on the structural integrity of buildings

  • This work will show that the Brain–emotional learning-based intelligent control (BELBIC) controllers can be used for vibration reduction for non-collocated civil structure systems where its parameters can be obtained numerically by using a particle swarm optimization (PSO) algorithm

  • Since emotional behaviour is a key aspect in species robustness and adaptability, it would be central to translate this feature into machines. It was in this framework that [32], inspired by the limbic mathematical model developed by [33], devises a new control systems paradigm designated by brain–emotional learning-based intelligent control (BELBIC)

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Summary

Introduction

Earthquakes are a natural phenomena that has one of the most the severe impact on the structural integrity of buildings. Besides requiring less external energy, in general semi-active vibration control leads to increased overall stability compared to active control [1,2] For those reasons, the use of semi-active control systems is a trending research theme and can be found applied in many distinct engineering areas such as vehicles suspensions and smart structures just to name a few [3,4,5,6,7,8,9,10,11,12,13,14]. This work will show that the BELBIC controllers can be used for vibration reduction for non-collocated civil structure systems where its parameters can be obtained numerically by using a particle swarm optimization (PSO) algorithm. This latter method will be used to define the parameters of a BELBIC controller such as to mitigate the vibration on a three-storey building due to ground motion caused by an earthquake signal.

Problem Statement
The Building Mathematical Model
Magneto-Rheological Damper Model
The BEL-Based Control System
Control System Architecture
The PSO Optimization Algorithm
Simulation Results
Discussion
Conclusions
Full Text
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