多学科设计优化
多学科方法
采光
概念设计
系统工程
计算机科学
工程设计过程
互操作性
建筑设计
过程(计算)
结构化
领域(数学)
软件工程
管理科学
建筑工程
工程类
人机交互
机械工程
纯数学
经济
操作系统
社会科学
数学
财务
社会学
作者
Naveen Kumar Muthumanickam,José Pinto Duarte,Timothy W. Simpson
出处
期刊:Artificial intelligence for engineering design, analysis and manufacturing
[Cambridge University Press]
日期:2023-01-01
卷期号:37
被引量:3
标识
DOI:10.1017/s0890060422000191
摘要
Abstract Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. This paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.
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