A smart façade system controller for optimized wind-induced vibration mitigation in tall buildings

Abstract

Wind-induced vibration (WIV) of tall buildings is a major cause of occupant discomfort and potential fatigue damage. Catastrophic failure may also take place at wind speeds that are lower than the design values due to phenomena such as vortex shedding or flutter-induced instabilities. This paper presents a data-driven adaptive control strategy that continuously seeks to minimize WIV for a given average flow condition by independently adjusting the angular orientation of an active façade system composed of a set of plates. The controller utilizes Genetic Algorithm (GA) optimization to determine façade plate angles that minimize time-averaged WIV amplitudes by altering the aerodynamics of the building. The GA is assisted by two artificial neural networks (ANNs). A predictor ANN acts as a regression model that continuously estimates the system dynamics. An optimizer ANN allows the controller to quickly recall what plate angle combination to use for a given wind condition. A 2D fluid-structure-interaction (FSI) model is used to simulate the steady-state response of the building for given wind conditions and façade plate angle combinations. The 2D model is validated by comparing to published data. Initial results show that vibration amplitudes were reduced up to 94% upon enabling the proposed smart façade controller. • An active morphing façade system for mitigating wind-induced vibration in tall buildings is investigated. • An adaptive data-driven active façade control strategy that utilizes machine learning is presented. • A reduced order fluid-structure-interaction building model was simulated via parallel computing. • Simulation results show that a significant reduction in building vibration was achieved.