Design exploration is a vital part of the building design process that aims at identifying the best-performing design with regard to the requirements of the client and building regulations. Building performance simulation can support this “explorative” process, its potential however being restricted by the fact that all design parameters are subject to uncertainty. In addition, while the need for an efficient exploration of the design space has resulted in the integration of optimization into the design process, the majority of existing research treats uncertainty quantification and optimization as separate processes. Finally, candidate designs are commonly evaluated with respect to only one or two design criteria, while the multi-dimensionality of real-world problems calls for integrated design solutions that meet several – often-conflicting – objectives.
A new approach is thus developed that aims to help designers identify robust Pareto-optimal solutions that satisfy several design criteria, while remaining optimal regardless of the uncertainty in boundary conditions. Through its implementation to a real-world case-study building, the novel approach is found to be able to identify optimum solutions that preserve their optimality over the entire range of uncertain performance scenarios
