Integrated Risk Management Framework for Tolerance-Based Mitigation Strategy Decision Support in Modular Construction Projects
Bibliographic record
Abstract
Managing excessive geometric variability risks in modular components and assemblies represents a major challenge in construction projects because of incompatibility between process capabilities and desired tolerance levels. The current modular practices usually apply strict tolerances, ad hoc strategies, or trial-and-error solutions for geometric variability management. The consequences of improper assessment and reactive management of such unique risks can result in extensive site-fit rework, cost overruns, schedule delays, and quality issues. To address this persistent challenge in modular construction (MC), this paper presents a systematic risk management framework for the proactive management of unique modularization risks. The developed framework includes identification and evaluation of tolerance-related issues and unique modularization risks in a quantitative manner, identification of the optimum geometric variability (using either a strict or relaxed tolerance approach) by addressing the trade-offs between offsite and onsite costs, evaluation of mitigation strategy effectiveness based on tolerance theory, and representation of the results in two- and three-dimensional graphs to support decision making with respect to the optimum selection of a mitigation strategy. A case study is used to demonstrate the proposed framework, and the results show that it can be used to effectively support industry practitioners to improve modularization performance and maximize its benefits.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".