Improving estimate at completion (EAC) cost of construction projects using adaptive neuro-fuzzy inference system (ANFIS)
Bibliographic record
Abstract
Earned value management (EVM) is a well-known technique for measuring project performance and progress. Owing to the EVM's attitude to simultaneously combine cost and time performance, project performance can be forecasted accurately, and this plays a vital role in the future of the projects. In the current study, the authors employed an adaptive neuro-fuzzy inference system (ANFIS) as a powerful prediction tool to forecast the completion cost of the projects considering the percentage of risk for qualitative variables and comparing it with other types of neural networks. Because the network structure is usually tuned based on the obtained results, a network optimization procedure is applied using a conventional method for estimating the cost-caused project breakdown. The results showed that ANFIS had a suitable performance (MSE = 0.0003), and based on the sensitivity analysis, the earned value is recognized as the most sensitive factor in the project. This study improves the general estimate of the completion formula by considering the uncertain conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".