Risk identification and assessment of modular construction utilizing fuzzy analytic hierarchy process (AHP) and simulation
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
Modular construction brings improved safety and mitigates risks of hazard and injury. However, modular construction technology is also challenged with a degree of uncertainty resulting from such internal and external factors as engineering, occupational, cultural, socio-economic, and financial. Since modular construction is by nature distinct from conventional construction, existing risk management research for onsite construction cannot be directly applied to modular construction. This paper describes research on the risk management associated with modular construction, focusing on: (1) identifying risk factors and (2) assessing the impacts of the identified risk factors on project cost and duration. The primary risk factors associated with modular construction are identified, and fuzzy analytic hierarchy process (AHP) is utilized to rank these factors; simulation techniques are employed to assess the risks of projects. The risk identification and ranking are evaluated by a focus group of experts from the modular construction industry; t-distribution and chi-squared distribution are applied to analyze the results. The case of a project in Edmonton, Canada is presented to illustrate application of the proposed methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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