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This work presents a control scheme for wind-induced vibration mitigation for tall buildings based on a gated recurrent unit (GRU) encoder-decoder model which operates using readings from multiple sensors to define a unique system state. The sensors include a distributed network of pressure probes installed on surrounding buildings, accelerometers installed on the principal building, and atmospheric conditions. The encoder-decoder GRU is trained from timeseries sensor readings to construct a unique internal representation (hidden state) of the evolving wind and building conditions. A 1:400-scale aeroelastic building model with motorized plates acting as aerodynamic control surfaces is used in wind tunnel experiments to conduct this study. An online genetic reinforcement learning (GRL) algorithm uses a series of multilayer perceptron (MLP) networks to determine optimum actuator orientations for different flow conditions. The algorithm stores previously discovered solutions in the MLPs sorted by their fitness. The GA operates by obtaining a solution from each of the MLPs and performing GA operations on them to choose the next combination of plate angles to try. A chance also exists for trying completely random plate angles to prevent the GA from stalling. The MLPs are continuously trained during online optimization using findings obtained from new trials. The system eliminates the need for holding wind conditions, which are uncontrollable, constant during online training but still uses a pseudo-random search technique to obtain global optimum solutions. Results show a considerable reduction in building RMS acceleration when compared with a large collection of results with random constant plate angle orientations. 

Tall and slender buildings often endure disturbances resulting from winds composed of various mean and fluctuating velocities. These disturbances result in discomfort for the occupants as well as accelerated fatigue life cycles and premature fatigue failures in the building. This work presents the development of a smart morphing façade (Smorphaçade) system that dynamically alters a buildings’ external shape or texture to minimize the effect of wind-induced vibrations on the building. The Smorphacade system is represented in this work by a series of plates that vary their orientation by means of a central controller module. To validate the simulation, a simple NACA0012 airfoil is simulated in a stream of air at a Reynolds number (RE) of 2 million. The pressure and viscous force profiles are captured to plot the variation of the lift force for different angles of attack that are then validated using published experimental airfoil data. After validation, the airfoil is attached to a linear spring-damper combination and is allowed to translate vertically without rotation according to the force profile captured from the surrounding air stream. A PID controller is developed to equilibrate the vertical position of the airfoil by altering its angle of attack. The model and its utility functions are implemented as an OpenFOAM® module (MSLSolid). Thereafter, the model is expanded to handle a planar case of a building floor carrying 4 controllable plates. The forces on the building profile are summed at the centroid of the building and the windward rigid body motion of the floor is estimated by reflecting the horizontal force component on a Finite Element (FE) model of the building. The time series information of the force acting on the building and the resulting oscillations are captured for exhaustive combinations of the plate angles. This data is used to build a lookup table that gives the best plate configuration for a given wind condition. A controller operates in real-time by searching the lookup table using readings of the wind condition. Preliminary results show a 94% reduction in the amplitudes of wind-induced vibrations.