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In performance-oriented architectural design, the use of advanced computational simulation tools may provide valuable insight during design. However, the use of such tools is often a bottleneck in the design process, given that computational requirements are usually high. This is a fact that mostly affects the early conceptual stage of design, where crucial decisions mainly occur, and available time is limited. In order to deal with this, decision-makers frequently resort to drawing conclusions from experience, and, as such, valuable insight that advanced computational methods have to offer is lost. This paper explores an alternative approach, which builds on machine-learning algorithms that inductively learn from simulation-derived data, yielding models that approximate to a good degree and are orders of magnitude faster. We focus on visual comfort of office spaces. This is a type of space that specifically requires visual comfort more than others. Three machine-learning methods are compared with respect to applicability in approximating daylight autonomy and daylight glare probability. The comparison focuses on accuracy and time cost of training and estimation. Results demonstrate that machine-learning-based approaches achieve a favourable trade-off between accuracy and computational cost, and provide a worthwhile alternative for performance evaluations during architectural conceptual design.
Chatzikonstantinou et al. (Fri,) studied this question.