Bibliographic record
Abstract
Fuzzy Clustering-Based Model for Productivity Forcasting Farid Mirahadi, Tarek Zayed Pages 596-607 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Forecasting productivity of construction operations is a difficult but crucial task in planning construction projects. Over the past decades, many models have been developed to forecast productivity for different construction operations. Models made up of several functional relations and controlled by a specific number of control rules are more in line with human reasoning and logic. Neural-Network-Driven Fuzzy Reasoning (NNDFR) structure as one of these models shows a great performance for modeling datasets among which clear clusters are recognizable. Lack of the compatibility of conventional NNDFR with fuzzy clustering algorithms besides the insufficient attention paid to the optimization of number of clusters in this model, created a potential area for further research. The main contribution of the proposed model is to develop a modified NNDFR system to model construction data. To this end, Fuzzy C-Means (FCM) algorithm is substituted for K-means in NNDFR structure, and its parameters such as the number of clusters and weighting exponent are optimized through genetic algorithm. The proposed model is further verified through simulation of a construction operation in which several qualitative and quantitative factors are considered. Its implementation to the case study shows a considerable improvement of model performance with lower Mean Squared Error (MSE). The developed model assists researchers and practitioners in utilizing historical construction data to forecast productivity of construction operations with a high accuracy that could not be obtained by traditional techniques. Keywords: Productivity forecasting, Fuzzy reasoning, Fuzzy clustering, Neural network, Clustering-based model, Genetic algorithm, Multi-dimensional membership function DOI: https://doi.org/10.22260/ISARC2013/0065 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".