Analysis of labour productivity of formwork operations in building construction
Bibliographic record
Abstract
Purpose Labour productivity plays an important role in the successful delivery of engineering, procurement and construction projects. This paper aims to present a field study that determines the effects of a set of variables on daily and/or short‐term jobsite labour productivity, using artificial neural network model. Design/methodology/approach The data used in this paper were collected over a period of ten months, directly from the job sites of two building construction projects in Montreal. A neural network model was used to study a number of factors considered to impact labour productivity on daily basis. These included temperature, relative‐humidity, wind speed, precipitation, gang size, crew composition, height of work, type of work and construction method employed. The data were then analyzed to determine the influence of these parameters on site labour productivity. Findings Among the nine parameters studied, temperature was found to have the most significant impact on productivity, closely followed by the height then by the type of work. Given the range of the collected data available on the variables considered, temperature, humidity and crew composition were found each to have a similar trend, with an optimum value that corresponds to the normalized maximum productivity. Originality/value The findings of this paper will provide awareness and better understanding of parameters that impact labour productivity in building construction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".