Integrated Risk Management Framework for Tolerance-Based Mitigation Strategy Decision Support in Modular Construction Projects
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
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.
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 | 0.000 | 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