Analysing delay factors in construction projects using Z-number approach: insights from Pakistan
Bibliographic record
Abstract
Delays are a pervasive challenge in the global construction industry, requiring more consideration to address project failure. This research proposes an improved methodology for analysing delays in the construction industry. Through a literature review, a set of 85 delay factors was identified and classified into five categories aligned with the various project phases: initiation, design, procurement, execution, and closeout. A method based on the Z-number theory was developed for the evaluation and prioritization of delay factors in the context of Pakistan. This approach incorporates expert judgment, confidence level, and experience to enhance the reliability of the evaluation. The research findings underscore the substantial influence of several critical factors on project delays. Specifically, factors such as cost inflation, contractor financial issues, inadequate project supervision and management, adverse weather conditions, insufficient skilled labour, delays in government document approvals, and unforeseen cost escalations during construction emerged as the foremost contributors to project delays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".