A Hybrid Decision Support System for Partition Wall Selection with an Application in Masonry Wall Systems
Notice bibliographique
Résumé
Masonry construction offers a range of benefits as it serves multiple purposes in a single system. It is cost-effective, long-lasting, and provides an aesthetically pleasing appearance. Moreover, its design flexibility and reasonable construction costs make it even more appealing. Masonry wall systems include several types. In this research, the focus is specifically directed towards masonry partition walls. Recent trends indicate a decreased preference for masonry construction. Literature and industry reports show several reasons behind this decline including: lack of masonry design knowledge among architects, labor-intensive execution, and intricate nature of masonry wall design and detailing. It is evident that the utilization of modern technological design advancements in this sector are not widespread. In addition to the general challenges identified in this field, masonry partition walls are being overdesigned. Also, there is no systematic selection method for wall type selection in partition wall design. This motivates the solution proposed in this study to develop a hybrid decision support system (DSS) for partition wall design. The proposed DSS includes two parts. The first part is a multi-criteria decision-making tool based on the choosing by advantage (CBA) method which facilitates the process of partition wall type selection, highlighting the advantages of masonry partition walls. Wall alternatives and wall selection criteria are determined based on the National Building Code of Canada and experts’ opinion. As the second part of the DSS, a computational design tool is developed to facilitate the design process by automatically controlling the structural soundness of unreinforced masonry partition walls. It also aims at improving designers' comprehension of unreinforced masonry partition walls by automating the design process, ensuring compliance with structural requirements of Masonry Code, and offering clear visualization and simplified design iteration. The integration of design model into a Building Information Modeling (BIM) environment addresses the need to encourage the utilization of digital tools in masonry design. The proof of concept of the proposed model is conducted through the implementation of two different hypothetical case studies. Wall selection using the CBA method shows the significant impact of the developed DSS in clearly comparing wall options, highlighting the advantages of masonry systems, and guiding a well-informed decision considering all design requirements. The computational design model integrated in the BIM model streamlines the design process, enables architects to automatically check structural design requirements of masonry partition walls, while saving both time and cost. According to RSMeans cost database, 3-11% savings are achieved in constructing unreinforced masonry partition instead of reinforced walls. In conclusion, the proposed DSS can substantially improve both the partition wall design process and the adoption of masonry wall systems, with a notable potential for extension to other categories of masonry walls.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, pas un consensus.
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