eCAADe proceedings · 2018 · 12 citations · 0 references
ArchitectureArchitectural DesignArchitectural EngineeringBuilding StructuresDesign AutomationDesign OptimizationArchitectural ModelingConceptual Massing ModelsMulti-objective Design OptimizationMulti-objective OptimizationMultiobjective OptimizationOptimization-based Design3D Massing ModelingConceptual Design OptimizationConvolutional Neural NetworksMassing OptimizationSynthetic Massing Models
Architectural design problems are often multi-objective in nature, involving both qualitative and quantitative objectives. Previous research has focused exclusively on the development of multi-objective optimization algorithms that work with multiple quantitative objectives. No previous research has looked at the topic of multi-objective qualitative optimization (MOQO), in which multiple qualitative objectives are optimized simultaneously. This research addresses MOQO through the development of a unique multi-objective optimization algorithm for the conceptual design phase that uses three-dimensional convolutional neural networks (3D CNNs) to measure user-defined qualities in architectural massing models.