Dynamics of workforce skill evolution in construction projects<sup>1</sup>This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.
Bibliographic record
Abstract
Construction projects are usually labour intensive, and human resource (HR) issues contribute significantly to a project’s final costs. From this perspective, a tool that can help construction managers reduce their HR costs can potentially generate improvement in the project cost. In this paper we propose a simulation-based approach that sheds light on the dynamics of workforce skill evolution as the project progresses, thereby assisting construction managers in adjusting their HR policies. The proposed approach uses a system dynamics (SD) simulation model that dynamically tracks the effects of alternative HR policies. After the development and validation of the SD model, the SD model is extended to capture operational details and their interaction with workforce skill evolution, adopting a hybrid SD and discrete event simulation (DES). The hybrid model has been applied to an experimental case of structural steel fabrication projects, in which we demonstrate that there is a considerable room for cost-saving in HR. The hybrid modeling approach introduced in this paper can be employed by construction managers for possible improvements in HR management, as well as researchers for an in-depth understanding of the dynamics in workforce skill evolution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".