Assessment of Construction Risk Management Maturity Using Hybrid Fuzzy Analytical Hierarchy Process and Fuzzy Synthetic Approach: Iraq as Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Knowing and developing the construction organizations' maturity level in risk management is critical to ensure they achieve their strategic objectives.This paper aims to design a new construction organizations' risk-management maturity model (C.ORM3) using new hybrid techniques and a distinct validation strategy based on global and local experience, to assess risk management maturity level in developing countries.A multi-steps methodology was adopted in this research.The study adopted an excessive systematic literature reviews of 22 previous articles on RM maturity and four standards and guidelines for eliciting model components.These components include five attributes with 26 capabilities; 24 capabilities identified from literature review and 2 from experts.These capabilities are evaluated against five levels: immature, ad-hoc, standard, managed, and optimized.The authors adopted a new strategy for validating the model by three groups of global and local experts and verifying the proposed model in a realistic-world case study.This study is the first to use a hybrid method based on the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Synthetic Evaluation (FSE) techniques in evaluating RM maturity (RMM).Iraqi construction organizations validate the practicality of the model.The results showed that the overall RMM level of the Iraqi construction sector is 1.52, between immature and ad-hoc.The model has been converted into a computer template for ease of use by organizations.This study concluded that the suggested C.ORM3 helpful for construction organisations to evaluate their current state of RM and plan for future development.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it