Implementation of Value Engineering in Optimizing Project Cost for Sustainable Energy Infrastructure Asset Development
Bibliographic record
Abstract
The purpose of this study is to optimize costs by analyzing a case study of the Energy Utility Company (EUC) project in Malaysia and building the main electric distribution station (MEDS) project specifically. In order to achieve this objective, Value Engineering (VE) technique as one of the proven tools was selected, and three alternatives were applied. The first alternative was to reduce the room sizes of the building. The second alternative was to replace the plaster painting with normal painting, and the last one was to replace painting up to 1.5 meters with 1 meter of the high of walls from the floor. Results approved that the implementation of VE has successfully reduced the cost of the project without compromising the quality of the materials. For the first alternative, 17.1% of the cost was saved. Regarding the second alternative, 69.8% of the total cost for ceiling painting was saved, and for the third alternative, 41.6% of the total cost of walls from the floor was saved. The findings of this research may serve as a guide for engineers, scholars, and constructors to reduce the cost of the building project.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".