Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».