A Hybrid Decision Support System for Partition Wall Selection with an Application in Masonry Wall Systems
Bibliographic record
Abstract
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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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".