A Hybrid Decision Support System for Partition Walls
Bibliographic record
Abstract
Partition walls play a crucial role in buildings, influencing their aesthetics, functionality, and integration with other architectural elements. However, the selection process for partition wall types is often challenging due to the multitude of options available, varying decision criteria, and inadequate decision-making practices in the Architecture, Engineering, and Construction (AEC) industry. To address these challenges and improve decision-making, a hybrid Decision Support System (DSS) named PartitionWall Pro is proposed. This tool combines both document-driven and model-driven approaches to assist in the selection and design of partition walls. The document-driven aspect utilizes a choosing-by-advantages (CBA) model to compare the advantages of different partition wall options, while the model-driven component employs computational design models to analyze the structural integrity of unreinforced masonry partition walls. Validation procedures ensure the reliability and accuracy of the DSS in practical applications. Through case studies involving a warehouse and a school, the study demonstrates how the DSS simplifies decision-making processes and encourages the adoption of cost-effective partition wall solutions. The results underline the potential of the DSS to enhance efficiency, foster stakeholder discussions, and improve communication in building design projects, thereby offering valuable insights for researchers and industry professionals alike, ultimately transforming partition wall design practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".