ENHANCEMENT OF BID DECISION-MAKING IN CONSTRUCTION PROJECTS: A RELIABILITY ANALYSIS APPROACH
Bibliographic record
Abstract
Various risks significantly influence pricing of bids and a wide range of factors impact bid pricing risks. Of these, client’s reputation and the record of projects owned by a client have vital contribution on the issue. Current practices however fail to capture the impacts of client-related factors. There is a need for developing a practical quantitative approach, which enables estimators to process bid risk allocation easily. Through reliability analysis, the developed method proposed in this study enables practitioners to make informed bid/no-bid decisions based on estimating the probabilities of schedule and cost overruns. Estimating the probability of project failure enables estimators to quantify the risk element of bid price. In addition, schedule and cost overrun cumulative probability distributions can be used to estimate the expected value of these variables. The practicability of this proposed method is tested by empirical data obtained from 40 university construction projects of one client, for estimating the bid price of a low-rise building. For researchers, findings provide illuminating insight into the potential of using reliability analysis as a valuable tool for bid decision-making practices. So too, the proposed method offers a blueprint for estimating and calculating time and contingency – and managing associated risks – in planning construction projects. The contribution of this study for the world of practice lies in providing a simple, rapid and cost-effective method for bid decision-making processes.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".