The threat of wind-related hazards to vulnerable coastal locations necessitates the development of economical approaches to design and construct resilient buildings. This study investigates using a cyber-physical systems (CPS) approach as a replacement for traditional trial-and-error methods for civil infrastructure design for wind loads. The CPS approach combines the accuracy of boundary layer wind tunnel (BLWT) testing with the efficiency of numerical optimization algorithms. The approach is autonomous: experiments are executed in a BLWT, sensor feedback is monitored and analyzed, and optimization algorithms dictate physical changes to the model through actuators. The cyberinfrastructure for this project was developed with the collaboration of multiple researchers at the University of Florida Experimental Facility (UFEF) under the Natural Hazard Engineering Research Infrastructure (NHERI) program.

A proof-of-concept was developed to optimally design the parapet wall of a low-rise building. Parapet walls nominally reduce suction loads on the roof but lead to an increase in positive roof pressure and base shear. A mechatronic low-rise building model was created with a parapet wall of adjustable height for BLWT testing. Various single-objective optimization algorithms were implemented to minimize the magnitude of roof wind pressures. Multi-objective optimization was used to simultaneously minimize both the magnitude of roof suction pressures and building base shear. A multi-objective procedure can consider the competing objectives of multiple stakeholders often present in engineering design.

The CPS approach was extended to optimize the performance of a landmark tall building for wind loads. A 1:200 multi-degree-of-freedom (MDOF) aeroelastic model was created to represent the building in a BLWT. Aeroelastic models directly simulate the scaled dynamic behavior of the building including effects of aerodynamic damping, vortex shedding, coupling within modes, and higher modes. The model was equipped with a series of variable stiffness devices (adjustable leaf springs) in the base to enable quick adjustments to the model’s dynamics. Additionally, the model was equipped with an active fin system (AFS) consisting of individually controllable fins installed at the four corners to modify the building aerodynamics and suppress vortex-induced vibrations. Multiple design problems were explored where the model’s dynamics and aerodynamics were refined using heuristic optimization algorithms to minimize costs while satisfying acceleration and drift limits.

The traditional design process for wind requires lengthy collaboration between designers and wind tunnel operators. This process may include the construction of a limited set of building models, leading to a non-exhaustive exploration of potential designs. Using mechatronic models guided by optimization algorithms enables optimum designs to be attained quicker than conventional methods. In future work, the proposed cyber-physical framework can be expanded to integrate machine learning and other computational tools to improve efficiency and reduce the reliance on experimental testing.