Data Management for Construction Processes Using Fuzzy Approach
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it