Improving But-For Delay Analysis and Concurrency Assessment
Bibliographic record
Abstract
But-For analysis is one of the popular techniques for apportioning the responsibility for project delays among the project parties (owner, contractor, and third party).Despite its acceptance by courts, one of its known drawbacks is that it produces conflicting results when adopting different party's viewpoints.Moreover, But-For analysis is not able to identify the concurrent delays caused by multiple parties.Despite some literature modifications to address those shortcomings, Modified But-For (MBF) analysis persistently does not consider event chronology and thus can produce wrong results.This paper thus discusses the concurrency assessment method of the MBF and introduces implementation improvements to divide the analysis into multiple windows to increase the analysis resolution, account for critical path fluctuations, and consider the chronology of different-party events, which is a requirement by recent delay analysis guidelines of professional bodies such as AACE and ASCE.A case study is used to show a detailed procedure for applying multiple-window MBF analysis to produce more accurate and repeatable delay analysis, considering concurrent delays.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".