Measurement of Information Loss and Transfer Impacts of Technology Systems in Offsite Construction Processes
Bibliographic record
Abstract
Offsite construction, also known as industrialized/prefabricated construction, is an alternative approach to delivering construction projects, compared to the on-site stick-built/built-on-site construction approach. Today’s offsite construction processes often use technology systems (e.g., digital design tools and automated machinery) to increase productivity and improve product quality. These systems operate collectively and rely on information generated in different operating environments. Therefore, information interoperability is critical to achieving overall construction efficiency and economics. This creates a need to study the impacts of information loss and transfer due to information interoperability, specifically for offsite construction processes. Given this, the paper uses a qualitative (case study) approach to document the offsite construction processes and the information requirements for each process. The efforts spent on information generation and transfer are taken as inputs for calculating the information loss and transfer impact using a quantitative (Monte Carlo simulation) approach. It contributes to the body of knowledge with (1) documentation of the current offsite construction processes based on wood panelized construction and the information requirements for all involved technology systems; (2) a case study approach that can be generically applied in other offsite construction companies to capture the information efficiency due to information interoperability; and (3) a generic simulation-based approach to measure the impacts of information loss and transfer between processes. As a result, the paper proposes phases of technology adoption and strategies for offsite construction companies.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".