Bibliographic record
Abstract
Introduced in the early twentieth century, mass production created the conditions for rapid expansions in modularity and repetition to the building industry. Since the 1960s, and with the integration of information technologies, digitization brought the possibility of customization. Diversity, difference, and individuality could now be implemented with the same ease as mass production. Accordingly, building components could be mass customized, allowing for optimal variances to respond to differing local conditions, and enabling the production of uniquely shaped and sized structural components. Ever a vital sector in the building industry, housing has witnessed a renewed surge of interest in the last two decades, especially following these new approaches to modes of design and production. While this interest has taken many forms and constituencies, digital design and manufacturing strategies have inspired the most diverse research and pragmatic solutions to contemporary industry challenges. However, there is marked gap between proposed research approaches and current production practices, specifically in the prefabricated housing industry, which otherwise represents an ideal model to adopt mass customization. Although unrecognized within standard housing production, current research acknowledges the need for advanced computer applications for enabling mass customization in the housing industry. This thesis thus proposes a novel framework and a systematic group of methodologies for constructing a computational design system that could support homebuyers' participation in the design of their dwellings. This framework derives its novelty by analyzing mass customization theories, technological enablers, various research endeavours in housing, and the standards currently adopted by the prefabricated housing industry. The aim of this framework is to redefine the traditional relationship between homebuyer, architect, and manufacturer. Consequently, this thesis proposes not only a computational tool, but also a comprehensive approach for customization. The framework is simulated by two case studies dedicated to customization in the early design stages. This leads to the development of both an advanced configuration system and a generative tool-based customization system. These simulations arise from an analysis of the profile and practice of a leading prefabricated housing company in Quebec, and thus create a platform that is intimately responsive to the contemporary needs of the industry. The proposed framework provides a rigorous method of customizing prefabricated housing, particularly through an advanced configuration system that builds on existing industry applications. However, the relevance and engagement of a generative tool-based system may still be questioned, especially depending on the available degree of automation and the system operator, among other factors. Implementation requires a fundamentally multi-disciplinary approach to technological dialogue; one that the prefabricated housing industry requires further time and effort to assimilate.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".