Calculating cumulative inefficiency using earned value management in construction projects
Bibliographic record
Abstract
Delay is one of the major factors in the cost overruns that affect construction projects. Furthermore, delay may cause a cumulative impact or ripple effect on productivity. Even though there are various methods proposed in previous researches that are considered applicable for analyzing the damages resulting from delay, there are some limitations to previous approaches. Notably, they do not consider the realistic production rates of activities over time. Moreover, they do not reflect the ripple effects on the performance of the work remaining, after the completion of delay events. This paper, therefore, proposes a method, which is referred to as the COME method (combination of measured mile analysis and earned value management (EVM) incorporating probable production rates) that can reasonably calculate the cumulative damages due to delay, considering the feasible rates of production over time, and the ripple effects on productivity. The COME method includes the ‘learning curve’, and the ‘earned value analysis’ as research methodologies. Earned value management was utilized, to demonstrate and calculate the effects of the cumulative loss of productivity on the remaining work, as well as on the impacted work due to delay. An example analysis showed that the COME method is a feasible choice for damages calculation, considering probable production rates over activity progress, and indirect impacts on performance changes, after the completion of delay events. It is noted, however, that the COME method relies on the use of a subjective or availability of an estimated production rate for the estimate to complete calculations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".