Journal of Computing in Civil Engineering · 2017 · 74 citations · 36 references
ArchitectureArchitectural DesignArchitectural EngineeringBuilding Envelope DesignDaylighting DesignDesign OptimizationBuilding OrientationBuilding PlacementEnergy-efficient DesignLayout OptimizationMulti-objective Design OptimizationOptimization-based DesignBuilding Design OptimizationGenetic Algorithm OptimizationMulti-objective Optimization Of EnergyMultiobjective Genetic AlgorithmsOptimal DesignPareto-based OptimizationSingle-objective OptimizationSpatial Layout
Computational tools for early architectural design must integrate building performance considerations and early decisions about form, spatial layout, orientation, and envelope articulation. This paper introduces the Multiobjective Architectural Design Explorer (MADE) and a Pareto‑based exploration strategy to support performance‑based building design. MADE employs a two‑step genetic optimization: a single‑objective GA first generates layouts that satisfy formal, topological, and placement constraints, followed by a multiobjective GA that optimizes opening sizes for energy and daylighting performance. The results demonstrate the feasibility of MADE and its strategy, showing that multiple objectives can be satisfied through sequential optimization.
Computational tools for early architectural design need to address issues related to building performance and integrally consider early design decisions regarding building form, spatial layout, orientation, and envelope articulation. This paper presents (1) a design optimization tool, Multiobjective Architectural Design Explorer (MADE) that supports performative-based building design, and (2) a design exploration strategy that effectively operationalizes MADE for Pareto-based comparative search. MADE implements genetic optimization in two steps. It first generates building layouts that satisfy formal, topological, and placement constraints with a single-objective genetic algorithm. Then, MADE determines the opening sizes of the generated layout(s) by optimizing the buildings' energy and daylighting performance with a multiobjective genetic algorithm. MADE is tested on the design of a library building and the proposed Pareto-based exploration strategy is demonstrated in the selection of an optimal design. The results point out the feasibility of the presented tool and the design strategy, and that multiple objectives can be satisfied by sequentially optimizing design objectives.
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