Bibliographic record
Abstract
Uncertainty is an entrenched characteristic of most construction projects. Most research works in simulating construction operations have focused predominantly on modeling and has neglected to study the effect of subjective variables on simulation process. Data mining is used to extract hidden knowledge from a data set, which would not be readily obtained by traditional methods. There is a significant need for a new generation of techniques and tools with the ability to automatically assist humans in analyzing the mountains of available construction data searching for useful knowledge. The presented research develops, using Fuzzy approach, a data mining engine to utilize, analyze, extract and model the hidden patterns of the project data sets to predict the work task durations. The engine depends on finding the relation between quantitative and qualitative variables, which affect the construction processes, and work task durations. It consists of five steps: (1) select the factors that affect the construction process; (2) build Fuzzy sets; (3) generate Fuzzy rules and models; (4) build Fuzzy knowledge base; and (5) validate the effectiveness of the built knowledge base to predict the work task durations. The developed engine is validated and verified using case study with sound and satisfactory results, 92 % average validity percent. The developed research/engine benefits both researchers and practitioners because it provides robust knowledge base for construction processes and a tool to predict the related task durations for construction activities.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".