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AI-based Generative Design : A Study on stadium mass optimization based on simulation of sunlight analysis and genetic algorithms

원문정보

초록

영어

Advances in digital tools and building structure technologies have enabled more flexible architectural design, with AI-based performance design gaining considerable attention as a new design methodology. Stadium design must consider the two primary elements of sports events: athletes and spectators. Given that the facade of a stadium directly impacts solar energy efficiency, it is essential to incorporate environmental performance considerations from the initial design phase. This study employs an AI-based Generative Design process to generate a facade form that efficiently manages solar radiation and daylight, satisfying two conflicting performance objectives: max- imizing sunlight for turf growth in the pitch zone and minimizing direct sunlight exposure in the stadium seating zone. The optimal solution derived ranks 331st for pitch zone sunlight and 408th for stadium seating sunlight out of a dataset of 1,000 models. While this solution does not represent the absolute best for either individual objective, it is evaluated as the most balanced alternative, achieving the goal of maximizing sunlight in the pitch zone and minimizing it in the seating zone

목차

ABSTRACT
1. 서론
1.1 연구배경
1.2 상호 보완적인 경기장 디자인
1.3 연구목표
2. 관련 연구 분석 및 동향
2.1 분석 방법 일조 시간 분석
2.2 AI 기반 알고리즘
3. 연구 방법
3.1 연구 프로세스
3.2 대상지 및 경기장 규모 설정
3.3 시뮬레이션 모델링 설정
3.4 최적화 목표 설정
3.5 시뮬레이션
4. 연구 결과
4.1 유전 알고리즘을 통한 최적화 (NSGA-Ⅱ)
4.2 Cluster centroid 의 정량적 분석과 최적화 솔루션
5. 결론 및 제언
참고문헌

저자정보

  • Soochul Shin The graduate school of advanced imaging science, multimedia & film, Chung-Ang University
  • Wonhyung Lee The graduate school of advanced imaging science, multimedia & film, Chung-Ang University
  • Youngho Chai The graduate school of advanced imaging science, multimedia & film, Chung-Ang University

참고문헌

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