Generative systems in the architecture, engineering and construction industry: A systematic review and analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Researchers have been extensively exploring the employment of generative systems to support design practices in the architecture, engineering and construction industry since the 1970s. More than half a century passed since the first architecture, engineering and construction industry’s generative systems were developed; researchers have achieved remarkable leaps backed by advances in computing power and algorithms’ capacity. In this article, we present a systematic analysis of the literature published between 2009 and 2019 on the utilization of generative systems in the design practices of the architecture, engineering and construction industry. The present research studies present trends, collaborations and applications of generative systems in the architecture, engineering and construction industry in order to identify existing shortcomings and potential advancements that balance the need for theory development and practical application. It provides insightful observations that are translated into meaningful recommendations for future research necessary to progress the incorporation of generative systems into the design practices of the architecture, engineering and construction industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it