A robust metamodel-based optimization design method for improving pedestrian wind comfort in an infill development project

Constructing a new building inevitably modifies the microclimate in its vicinity. We propose a metamodel-based optimization method to devise near optimal designs that minimize building's adverse impacts on nearby pedestrians' wind comfort in hot and cold seasons. Four design parameters, i.e., building width, building depth, building height, and building orientation, are considered for a greatly simplified building design task. Specifically, computational fluid dynamics (CFD) analyses are performed to calculate the mean wind velocity, while all CFD experiment samples are determined through the Box-Behnken design of experiment method. Based on 54 CFD experiments, the relationships between building design variables and the summer and winter mean wind velocities within a specified assessment area are learned using the response surface methodology. Finally, a genetic algorithm with the desirability function as the objective function is applied to identify near optimal design options under the robustness control (i.e., family error rate lambda) for response surface models. The framework is applied to an infill development project to highlight its suitability in a real application. The experiment results show that as lambda gradually decreases from 1 to 0.05, the overall desirability index dwindles from 0.33 to 0.22, consequently generating a more conservative optimal design decision.