ComPara: A corpus linguistics in English of computation in architecture dataset

Horvath, Anca-Simona

doi:10.1016/j.dib.2022.108169

Cited by 4 publications

(2 citation statements)

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“…On the other hand, maps of geographies that produced successful eVolo submissions can be found in Ref. 52 but also under Refs. 37,53,54.…”

Section: Findings: Evolo Skyscraper Competition (2006–2020)mentioning

confidence: 99%

How we talk(ed) about it: Ways of speaking about computational architecture

Horvath

2022

International Journal of Architectural Computing

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If we understand architecture as a three-part system formed by the building, its image, or drawings and images describing buildings, and the critical discourse around architecture, then the texts or ways of speaking about architecture play a key role in understanding the field and its development. By analysing a corpus of around 4.6 million words from texts written between 2005 and 2020 that form a part of critical discourse in computational architecture (understood as the result of the intense digitalization of the field), this paper aims to map ways of speaking about computational architecture. This contributes to architectural theory and might help gain a better understanding of the evolution of the digitalization of construction in general. Findings show that computational architecture is surrounded by a specific way of speaking, hybridized with words from fields such as biology, neuroscience, arts and humanities, and engineering. While some topics such as ‘sustainability’ or ‘biology’ come up consistently in the discourse, others, such as ‘people’ or ‘human’, have periods when they are more and less popular. After highlighting open research questions, the paper concludes by presenting a map of periodic and recurring topics in ways of speaking about computational architecture over the last 15 years, thus tracking and documenting long-term trends, and illuminating patterns in the broader field of digital construction.

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“…On the other hand, maps of geographies that produced successful eVolo submissions can be found in Ref. 52 but also under Refs. 37,53,54.…”

Section: Findings: Evolo Skyscraper Competition (2006–2020)mentioning

confidence: 99%

How we talk(ed) about it: Ways of speaking about computational architecture

Horvath

2022

International Journal of Architectural Computing

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“…In the context of architecture, generative models that learn from annotated datasets have considerable potential, and a lot of valuable and intriguing work has come out of using text prompts to generate imagery (Del Campo et al, 2022). 10 Discussing new connections between language and architecture in the context of new tools, as done, for example, in Markus and Cameron (2002), 11 or Horvath (2022a) 12 , Horvath (2022b) 13 , Horvath et. al (2021) 14 , takes new significance in this context.…”

Section: Introductionmentioning

confidence: 99%

Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks

Pouliou

Horvath

Palamas

2023

International Journal of Architectural Computing

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The process of architectural design aims at solving complex problems that have loosely defined formulations, no explicit basis for terminating the problem-solving activity, and where no ideal solution can be achieved. This means that design problems, as wicked problems, sit in a space between incompleteness and precision. Applying digital tools in general and artificial intelligence in particular to design problems will then mediate solution spaces between incompleteness and precision. In this paper, we present a study where we employed machine learning algorithms to generate conceptual architectural forms for site-specific regulations. We created an annotated dataset of single-family homes and used it to train a 3D Generative Adversarial Network that generated annotated point clouds complying with site constraints. Then, we presented the framework to 23 practitioners of architecture in an attempt to understand whether this framework could be a useful tool for early-stage design. We make a three-fold contribution: First, we share an annotated dataset of architecturally relevant 3D point clouds of single-family homes. Next, we present and share the code for a framework and the results from training the 3D generative neural network. Finally, we discuss machine learning and creative work, including how practitioners feel about the emergence of these tools as mediators between incompleteness and precision in architectural design.

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