Risk management in Egyptian construction: Comparative analysis of general and banking sectors using advanced quantitative methods
Bibliographic record
Abstract
To effectively manage the challenges associated with meeting schedules, costs, and quality requirements in construction projects, it is crucial to address potential risk factors. Despite extensive research on risk management in construction, a significant gap persists concerning the uniformity of risk factors and their effects across various construction project types, geographical locations, and cultural contexts. This research presents a structured three-step approach to risk management, covering risk identification, analysis, and response. The methodology is applied to both the General Construction (GC) and Banking Construction (BC) sectors in Egypt, involving industry professionals through surveys. Quantitative analysis using the Relative Importance Index (RII) and Fuzzy-set theory gauges the influence of each risk factor, while Spearman Ranked Correlation tests differentiate risk profiles between sectors. A comparative analysis highlights Egypt-specific risk factors versus regional or global factors. Key risk factors with high centrality scores are identified, and optimal risk reduction strategies are selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The study reveals significant sector-specific risk differences and highlights the need for tailored risk management strategies. Key contributions include identifying vital risk factors, comparing them globally and regionally, and proposing effective mitigation strategies to enhance project timelines, costs, and quality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".