Toward a Qualitative RFIs Content Analysis Approach to Improve Collaboration Between Design and Construction Phases
Bibliographic record
Abstract
Requests for Information (RFIs) are formal processes, used in the industry of Architecture, Engineering and Construction (AEC), to obtain information not contained or inferable in the contract documents between the design and construction phases.RFIs produce rich, precise and structured sources of information.Analysis of RFIs content can help identify recurrent problems.The goal of this article is to present a method to identify problem areas during the construction phase of AEC projects through the analysis of RFI documents.Recent advances in the qualitative analysis of document content make this quest possible and fast.This article proposes to the scientific communities and AEC industry professionals, a systematic method based on the qualitative analysis of RFIs in order to propose some types of information to consider for a design more adapted to the construction phase.An example of the application in a steel construction project demonstrates the feasibility of this method and proposes some points to consider to improve the design of steel structures.
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.
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 | 0.039 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".