A Holistic Framework for Assessing the Quality of Building Construction in Saudi Arabia
Bibliographic record
Abstract
In order to make sure that structures adhere to the necessary norms and laws, it is essential to evaluate the quality of building construction. According to several frameworks, the quality of a building’s construction can be assessed in a number of different circumstances. In order to provide building projects with excellent quality and competitive pricing, hard work is required. To raise the standard of building construction, a model was created. The goal of this project is to provide a model for raising building construction quality. This study used the analytical hierarchy process (AHP) technique, which includes the determination of consistency ratios, pairwise comparisons, normalization, and a comparison matrix. The difficulty of implementing quality was determined for each task using the AHP technique. This was multiplied by the quality factor to obtain the final quality level. The model was tested in three different situations, and it was discovered that quality application is challenging across all building operations. Using a quality evaluation technique, this study assessed the building construction quality in Saudi Arabia. Additionally, a pairwise comparison, normalization, and a comparison matrix were used to calculate the consistency ratio. The ultimate quality level was determined by multiplying the difficulty level for each activity, as determined using the AHP approach, by the quality factor. This study will therefore be helpful to those involved in Saudi Arabian building, including architects, engineers, quality experts, and others. Additionally, the tool aids in the decision making process for enhancing construction quality.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".