An effective two-stage optimization method on aerodynamic measures to mitigate wind loads considering uncertainties in wind direction and velocity

At present, deterministic optimization methods on aerodynamic measures are usually employed to mitigate wind loads at one certain wind direction, ignoring the effects of the uncertainties in wind direction and velocity on wind loads. The uncertain optimization method with the consideration of the joint probability distribution (JPD) of wind speed and direction is hardly to conduct because it is very time-consuming and costly. In order to overcome these shortcomings, this study proposes an effective two-stage uncertain optimization method considering uncertainties in wind direction and velocity to achieve a considerable reduction of wind loads. In the first stage, the exceedance probability distribution of wind loads is determined based on the JPD and the wind load coefficients on the building, and the wind loads in the most unfavorable range of wind directions are chosen as the optimization objective. In the second stage, the optimal values of the design variables are determined by an interval optimization algorithm using the surrogate model and non-dominated sorting genetic algorithm II in the most unfavorable range of wind directions, where the surrogate model is constructed by the radial basis function method to relate the wind load coefficients to the design variables of aerodynamic measures, and can significantly improve the efficiency of the optimization algorithm. To validate the effectiveness of the proposed method, a flat-roof building with four spoilers is investigated and the rotation angles of spoilers are set as the design variables. The results show that the proposed method reduces the uplift wind loads with a 50-years return period up to 15.48% compared to the deterministic optimization method for one certain wind direction.