MODELING RISKS IN REAL ESTATE DEVELOPMENT PROJECTS: A CASE FOR EGYPT
Bibliographic record
Abstract
Risk analysis is a vital step in the succession of construction projects. However, no adequate researches have been conducted to assess, and quantify risk events in real estate projects in developing countries, and particularly in Egypt.This research recommends Fuzzy Quantitative Risk Assessment Model to quantify risk factors participated in real estate development projects. Model is composed of two components: 1) Fuzzy Fault Tree (FT) that determines root causes of each risk, probability of its occurrence, and probability of mitigation strategies failure; and 2) Fuzzy Event Tree (ET) that calculates crisp value of Expected Monetary Value (EMV) of allowance of mitigation of the identified risks. Causes of risk are determined through literature review and interviews with experts in field. Risk probability occurrence is determined using five linguistic terms, defined either triangular or trapezoidal membership functions which are developed using modified horizontal approach and an interpolation technique. Two-step Delphi technique is used to achieve consensus on the root causes and logical representation of the Fault Tree. Fuzzy importance analysis is performed to rank different root causes for identified risks according to their criticality to probability of occurrence. A Case Study is presented to evaluate results obtained from model, in terms of Expected Monetary Value (EMV), and fuzzy probability of failure for each risk participated in case study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".