Significance ranking of parameters impacting construction labour productivity
Bibliographic record
Abstract
Purpose Construction labour productivity is often influenced by variations in work conditions and management effectiveness. It is substantially important to understand the nature and extent to which individual parameters affect productivity. The purpose of this paper is to focus on providing insight on parameters that affect daily job-site labour productivity by investigating their relative significance and influence on work output. Design/methodology/approach The methodology is based on the illustration and use of three different data analysis techniques to rank parameters that affect a certain process. These techniques include Fuzzy Subtractive Clustering, Neural Network Modelling and Stepwise Variable Selection Procedure. The first one belongs to inferential statistics, while the other two are artificial intelligence based techniques. The collection of field information, spanning over a time period of ten months, comprised of daily real time observations of job-site operations, work progress information collected from project managers and supervisors by using customized forms, and daily weather condition recorded through internet sources. Nine parameters are considered in the study presented in this paper. The data on these parameters is examined and their relative influence and contribution in productivity estimates are assessed. The approach was to consider a limited set of parameters relating to daily job-site productivity. The methodology presented in this paper provides insight on the relative impact of parameters, affecting labour productivity on short term or daily basis. The results based on each of the three methods are analyzed and transformed into a final ranking of parameters. Findings The three most important parameters are identified in the same order by the fuzzy logic and neural networks methods. Regression analysis, however, provided somewhat different results. Originality/value This research investigates the contribution of a set of parameters towards the variations in daily job-site labour productivity. For practitioners such as site engineers, this is of practical importance for making daily work plans. On the other hand, the structured approach presented to perform significance ranking of parameters relevant to an engineering process, may also be of interest to other researchers and practitioners.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".