Guiding early building design towards lower carbon emissions through set-based design and genetic algorithm optimisation
低炭素排出を目指す初期建物設計のためのセットベースデザインと遺伝的アルゴリズム最適化の活用 (AI 翻訳)
Linda Cusumano, Mats Granath, N. Olsson, Rasmus Rempling
🤖 gxceed AI 要約
日本語
本研究は、セットベースデザインと遺伝的アルゴリズム（NSGA-II）を組み合わせ、建物の初期設計段階でコストと炭素排出を同時に最適化する手法を提案する。参照建物への適用で、遺伝的アルゴリズム単独では炭素排出35%削減（コスト9%増）、セットベースデザイン統合で最大42%削減（同等のコスト増）を達成し、初期設計における優位性を示した。
English
This study integrates set-based design with genetic algorithm (NSGA-II) optimization to simultaneously minimize cost and embodied carbon in early building design. Applied to a reference building, the genetic algorithm alone reduced carbon by 35% with a 9% cost increase; incorporating set-based design achieved up to 42% carbon reduction with comparable cost increase, demonstrating advantages for early-stage design decision-making.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の建築分野ではZEB・ZEHの推進や建設時のCO2排出削減が急務であり、本手法は初期設計段階での環境パフォーマンス向上に直接貢献する。SSBJ基準への対応や、建築物のライフサイクルカーボン評価に応用可能な知見を提供する。
In the global GX context
Globally, buildings account for ~40% of carbon emissions; this method enables early design trade-off analysis between cost and embodied carbon, supporting net-zero targets. It can be integrated into BIM and green building certification systems like LEED or BREEAM.
👥 読者別の含意
🔬研究者:The novel combination of set-based design and genetic algorithm offers a new perspective for multi-objective optimization in early building design.
🏢実務担当者:Building designers and engineers can use this approach to explore cost-carbon trade-offs and make data-informed decisions early in the design process.
📄 Abstract（原文）
The materials used in the structural system account for a significant share of a building's total embodied carbon emissions. Thus, to mitigate the climate impact of construction projects, it is essential to consider carbon emissions in early conceptual design and understand how structural decisions affect costs. Therefore, this study explores integrating set-based design with genetic algorithm optimisation to evaluate a wide range of building designs in terms of cost and carbon performance. First, databases of structural building assemblies, including their capacities, costs, and embodied carbon, were developed. The NSGA-II algorithm, combined with set-based design, was then applied to a reference building to minimise its cost and carbon emissions. The genetic algorithm alone identified solutions that reduced carbon emissions by 35% but with a 9% increase in cost. When set-based design was incorporated, carbon emissions were reduced by up to 42% with a comparable increase in cost. The findings demonstrate that integrating set-based design offers comparative advantages over using genetic algorithms alone, providing valuable insights for early-stage building design practice. This paper proposes a novel design approach that combines genetic algorithm optimisation with set-based design to explore a broader solution space and support data-informed decision-making to reduce carbon emissions in buildings.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1108/sasbe-06-2025-0296first seen 2026-06-29 06:46:06
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。