Factor Analysis for In Situ Reinforced Concrete Beam Production: A Principal Component Analysis Approach
Bibliographic record
Abstract
The construction industry, a driver for economic development worldwide, faces productivity challenges in Uganda, particularly in labour-intensive activities like in situ concrete beam construction. This study aims to identify and rank the factors influencing the production rate of reinforced in situ concrete beam construction in Kampala and Wakiso districts using principal component analysis (PCA). These factors including but not limited to weather, beam design and site conditions represent the independent variables while the production rate is the dependent variable. These variables were contextualized using a mixed-method approach in which data were collected from 20 construction sites through on-site measurements, a literature review and interviews. PCA was then employed to analyse the data and isolate the most influential factors on production rate, singling out beam length, daily temperature, the number of helpers, and the number of steel fixers, with average coefficients of 0.98, 0.882, 0.78, and 0.36, respectively, as the most significant. Therefore, this study provides an empirical ranking of factors influencing in situ concrete beam construction production rates, offering a foundation for better resource allocation and project management in Uganda’s construction sector.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".