Level of visualization support for project communication in the Turkish construction industry: A quality function deployment approach
Bibliographic record
Abstract
Quality of communication is a key factor in the success of construction projects. Visualization technologies can play an important role in improving the quality of data by improving human comprehension and increasing the depth of the information delivered. Visualization has for some time been identified as one of the major technology themes allowing development of construction processes. However, visualization has not been embraced as a strategic tool by construction companies, and they generally fail to take full advantage of available visualization tools. This paper aims to evaluate the extent of visualization as a communication tool in the construction industry and to determine potential benefits to be gained through implementation of visualization. The current state of the use of visualization for communication in Turkish architecture, engineering, and construction companies is mapped through surveys and interviews. Information flow contents and types are analysed to determine the types of information in the construction process that may benefit from visual representation. According to the priorities of the expectations ranked by the users and the potential of different visualization tools, the level of visualization required for each data flow is determined by a quality function deployment (QFD) based approach.Key words: visualization, construction, visual communication, quality.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".