Dynamic and Proactive Risk-Based Methodology for Managing Excessive Geometric Variability Issues in Modular Construction Projects Using Bayesian Theory
Why this work is in the frame
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Bibliographic record
Abstract
Managing excessive geometric variability risks (i.e., out-of-tolerance and out-of-alignment issues) represents a major challenge in modular construction projects, owing to lack of accurate data on modularization process capabilities for fabrication, transportation, and erection at the early design phase. Unrealistic and insufficient modularization process capability data typically convey a misleading risk profile and result in suboptimal mitigation solutions, which can in turn lead to cost overruns, schedule delays, quality issues, and owner dissatisfaction. Current modularization practices and previously developed risk management frameworks apply static risk assessment and management techniques, which do not enable updating of the generic information and initial assessment of the risk profile, when more realistic data become available. To address this persistent challenge in modular construction projects, this paper aims to introduce a systematic methodology that employs Bayesian inference theory for the dynamic assessment and proactive management of excessive geometric variability issues. The developed methodology includes a practical process for continual (1) updating of initial estimates of the performance of tolerance-based mitigation strategies based on real-time data, (2) reassessment of the risk profile, and (3) refinement of risk response decisions. The results of the case study described subsequently in this paper demonstrate how key project stakeholders and modular construction managers (e.g., designers, fabricators, and contractors) can use this methodology to efficiently reduce uncertainty in tolerance-related risk estimates and proactively manage impacts to improve modularization performance and maximize its benefits.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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