Estimating post- and pre-mitigation contingency in construction
Bibliographic record
Abstract
Contingency is necessary to mitigate and control risk associated with construction projects.Successful contingency estimation and risk mitigation strategies can help project managers to effectively control cost and schedule.Some practitioners mitigate risk by transferring it to another party with less effort and minimum cost.However, this may lead to undesirable results such as; useless depletion of contingency, cost overrun, and project delay.This paper differentiates between two types of project contingency; pre-mitigation, and post-mitigation.It also proposes a new estimation method for pre-mitigation and post mitigation contingencies using fuzzy set theory.The proposed pre-mitigation contingency estimation makes use of qualitative and quantitative assessment of risks associated with projects.Post mitigation contingency (POSTMC) estimation makes use of newly introduced planned efficiency factor (PEF).That factor is calculated using mitigation strategy cost, pre-mitigation contingency (PREMC) and several sub-factors such as; mitigation efficiency on probability (MEFP), mitigation efficiency on consequences (MEFC), and mitigation efficiency (MEF).This paper provides a decision support tool; expected to help project managers in estimating and evaluating pre-mitigation and post mitigation contingencies using a set of strategies during project life cycle.The evaluation of post mitigation efficiency allows user to update the risk mitigation plan (i.e.risk response plan) for future projects.In addition to that, it allows users to maximize profit and minimize cost without compromising the efficiency of the selected risk mitigation strategies.Numerical example is presented to illustrate the application and capabilities of proposed method in estimation the pre-mitigation and post mitigation contingency.It also
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".