Emergent Syntax: Machine Learning for the Curation of Design Solution Space

Sjoberg, Christian; Beorkrem, Christopher; Ellinger, Jefferson

doi:10.52842/conf.acadia.2017.552

Cited by 5 publications

(3 citation statements)

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“…These networks were then activated to determine a score, then cluster analysis was conducted through feedback; finally, a new population was produced. Wilkinson et al 23 tried to obtain data using computational fluid dynamics (CFD) by developing wind simulations on tall buildings. As et al 24 used deep learning approaches of ML for generative concept design in architecture.…”

Section: Related Work On Architecture and Machine Learningmentioning

confidence: 99%

“…By using these real data with Gaussian process regression (GPR), predictions were made for informing low energy strategies. Yang et al 31 examined convergence building climate zones and building energy efficiency based on data accuracy. Using a supervised classification algorithm, they provided comparable options with building environmental zones and building energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020

Özerol

Selçuk

2022

International Journal of Architectural Computing

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Through the recent technological developments within the fourth industrial revolution, artificial intelligence (AI) studies have had a huge impact on various disciplines such as social sciences, information communication technologies (ICTs), architecture, engineering, and construction (AEC). Regarding decision-making and forecasting systems in particular, AI and machine learning (ML) technologies have provided an opportunity to improve the mutual relationships between machines and humans. When the connection between ML and architecture is considered, it is possible to claim that there is no parallel acceleration as in other disciplines. In this study, and considering the latest breakthroughs, we focus on revealing what ML and architecture have in common. Our focal point is to reveal common points by classifying and analyzing current literature through describing the potential of ML in architecture. Studies conducted using ML techniques and subsets of AI technologies were used in this paper, and the resulting data were interpreted using the bibliometric analysis method. In order to discuss the state-of-the-art research articles which have been published between 2014 and 2020, main subjects, subsets, and keywords were refined through the search engines. The statistical figures were demonstrated as huge datasets, and the results were clearly delineated through Sankey diagrams. Thanks to bibliometric analyses of the current literature of WOS (Web of Science), CUMINCAD (Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD, and CAAD futures), predictable data have been presented allowing recommendations for possible future studies for researchers.

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Section: Related Work On Architecture and Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020

Özerol

Selçuk

2022

International Journal of Architectural Computing

View full text Add to dashboard Cite

show abstract

“…Ou seja, ele precisa explorar possibilidades e voltar para uma base que possa colocar em perspectiva toda complexidade explorada para definir novos caminhos exploratórios. Isso é importante porque, diferente das ciências naturais, o Design não busca descobrir como as coisas são, mas como as coisas devem ser (SIMON, 2019). Assim, testar diferentes possibilidades amplia as chances de se descobrir novas e melhores soluções, como também problemas que não haviam sido identificados.…”

Section: Modelos De Design Digitalunclassified

Inclusão De Agências Humanas Não Simbólicas Através De Técnicas De Aprendizado Profundo Em Processos De Design Computacional Generativo

RIBEIRO ALVES BARBOZA VIANNA

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Application of machine learning techniques to predict patient’s satisfaction of indoor environmental quality in Jordanian hospitals

Ali

Abdullah

Wedyan

2022

J Ambient Intell Human Comput

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Emergent Syntax: Machine Learning for the Curation of Design Solution Space

Cited by 5 publications

References 0 publications

Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020

Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020

Inclusão De Agências Humanas Não Simbólicas Através De Técnicas De Aprendizado Profundo Em Processos De Design Computacional Generativo

Application of machine learning techniques to predict patient’s satisfaction of indoor environmental quality in Jordanian hospitals

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