Qualitative analysis of Request For Information to identify design flaws in steel construction projects
Bibliographic record
Abstract
Abstract Request for information (RFI) is a formal process used in the Architecture, Engineering and Construction industry to address design flaws that affect communication between designers and contractors. A large number of RFIs are a sign of a lack of precision or coordination in the design documents. However, RFIs produce rich, precise, and structured information. Analyzing their content can help to identify recurring problems between designers and construction teams and better tailor future projects to the working context of the contractors. This article presents a method for identifying recurring issues during the design phase of steel construction projects through the analysis of the contents of RFIs. It is original in using a qualitative content analysis tool that can analyze large quantities of RFIs rapidly. Identifying the recurrent problems of contractors will allow the establishment of rules to be taken into consideration during the design phase of future steel construction projects. A case study of 26 steel construction projects demonstrates the feasibility of this method. This case study shows that, given the same designers and construction teams, recurring problems shown in RFIs do not differ according to the scale of the projects. In this case, the main issue between designers and contractors is the lack and inadequate presentation of information related to the connection of steel components. Identifying these problems can pave the way for initiatives to improve the design phase and can be an essential step in making contractors’ knowledge available to designers early in the projects.
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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 | 0.032 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".