Tool for evolutionary aided architectural design. Hybrid Evolutionary Algorithm applied to Multi-Objective Automated Floor Plan Generation

Nisztuk, Maciej; Myszkowski, Paweł B.

doi:10.5151/proceedings-ecaadesigradi2019_453

Cited by 4 publications

(2 citation statements)

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“…Besides such works on the visualization and interpretation of adjacencies, there are many works on automating the transformation of the adjacency graph into new floorplan geometry (Nisztuk et al 2019, Gavrilov et al 2020. There are also applications for matching adjacencies into an existing geometry (Wurzer et al 2019, von Both et al 2021.…”

Section: Literature Reviewmentioning

confidence: 99%

Space Matcher - An interactive toolbox for assisting in spatializing & testing office programmes using graph centralities

Fuchkina¹,

Bielik²,

Schneider³

et al. 2022

eCAADe Proceedings

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Graph-based representations of functional requirements (adjacencies, bubble diagram) are a common and useful method that supports architects in the conceptual phase of planning. However, the task of specifying the functional requirements through an adjacency graph can be challenging due to a quadratic growth of complexity in relation to the number of spaces. In turn, this increase of complexity challenges the designer searching for solutions that fulfill these functional requirements. There are systems that aim to address the difficulties related to graph-based space allocation. They, for instance, use fuzzy logic to weight the edges of a graph (i.e., specify relations between spaces) and spring systems (Newtonian gravitation model) to visually clarify the resulting proximity of all spaces according to the rules. Nevertheless, the problem of specifying large-scale adjacencies itself is omitted due to the assumption that such matrices are correctly filled in some previous steps. Moreover, the translation of the resulting graph into a spatial configuration is rarely supported. This work addresses these limitations and proposes a set of tools to assist the designer when defining the adjacency requirements and searching for design solutions that fulfill these requirements. Our approach aims to reduce the complexity of the design task by using graph centrality-based design heuristics. We discuss these heuristics and show their application in a scenario where a new spatial program needs to be allocated into an existing building.

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Section: Literature Reviewmentioning

confidence: 99%

Space Matcher - An interactive toolbox for assisting in spatializing & testing office programmes using graph centralities

Fuchkina¹,

Bielik²,

Schneider³

et al. 2022

eCAADe Proceedings

View full text Add to dashboard Cite

show abstract

“…Lastly, the exploration of solution spaces, as discussed by del Campo, highlights the broad potential of AI in navigating complex architectural challenges [40]. A subset of solutions is also based on evolutionary algorithms, such as generating functional layouts of single-family houses [41]. There is a growing list of commercial applications that use AI in architectural design.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Digital Tools for Assisting Low-Carbon Architectural Design: Merging the Use of Machine Learning, Large Language Models, and Building Information Modeling for Life Cycle Assessment Tool Development

Płoszaj-Mazurek,

Ryńska

2024

Energies

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The construction sector is a significant contributor to global carbon emissions and a major consumer of non-renewable resources. Architectural design decisions play a critical role in a building’s carbon footprint, making it essential to incorporate environmental analyses at various design stages. Integrating artificial intelligence (AI) and building information modeling (BIM) can support designers in achieving low-carbon architectural design. The proposed solution involves the development of a Life Cycle Assessment (LCA) tool. This study presents a novel approach to optimizing the environmental impact of architectural projects. It combines machine learning (ML), large language models (LLMs), and building information modeling (BIM) technologies. The first case studies present specific examples of tools developed for this purpose. The first case study details a machine learning-assisted tool used for estimating carbon footprints during the design phase and shows numerical carbon footprint optimization results. The second case study explores the use of LLMs, specifically ChatGPT, as virtual assistants to suggest optimizations in architectural design and shows tests on the suggestions made by the LLM. The third case study discusses integrating BIM in the form of an IFC file, carbon footprint analysis, and AI into a comprehensive 3D application, emphasizing the importance of AI in enhancing decision-making processes in architectural design.

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Automatic Generation of Architectural Plans with Machine Learning

Babakhani

2023

Technology|Architecture + Design

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Tool for evolutionary aided architectural design. Hybrid Evolutionary Algorithm applied to Multi-Objective Automated Floor Plan Generation

Cited by 4 publications

References 0 publications

Space Matcher - An interactive toolbox for assisting in spatializing & testing office programmes using graph centralities

Space Matcher - An interactive toolbox for assisting in spatializing & testing office programmes using graph centralities

Artificial Intelligence and Digital Tools for Assisting Low-Carbon Architectural Design: Merging the Use of Machine Learning, Large Language Models, and Building Information Modeling for Life Cycle Assessment Tool Development

Automatic Generation of Architectural Plans with Machine Learning

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