Multiobjective Design Optimization of Building Space Layout, Energy, and Daylighting Performance

Dino I. G., Üçoluk G.

Journal of Computing in Civil Engineering, vol.31, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31
  • Publication Date: 2017
  • Doi Number: 10.1061/(asce)cp.1943-5487.0000669
  • Journal Name: Journal of Computing in Civil Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Building energy and daylighting optimization, Architectural layout optimization, Genetic algorithms, Design exploration, Pareto front, GENETIC ALGORITHM APPROACH, EVOLUTIONARY ALGORITHMS, SIMULATION, EXPLORATION, FRAMEWORK, SYSTEMS
  • Middle East Technical University Affiliated: Yes

Abstract

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.

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.