Enhancing Readability in Construction Safety Reports Using a Two-Step Quantitative Analysis Approach
Bibliographic record
Abstract
This study addresses the limitations of South Korea’s Design for Safety (DfS) reports, which are a critical component of construction safety reports (CSRs) but rely heavily on text, limiting readability and visual comprehension. While previous studies have highlighted the readability challenges in construction safety documents, few have quantitatively combined layout and readability assessments using objective metrics. To enhance information delivery, this research proposes an improved CSR format and quantitatively evaluates its effectiveness compared to the conventional format. A two-step analysis was conducted using document layout analysis, pixel-based methods, and the Flesch Reading Ease Score (FRES) to assess layout and readability. The results showed that conventional CSRs consist of nearly 100% text, while the improved format integrates approximately 70% images and 30% text, enhancing visual clarity without altering content. The improved format achieved a higher average FRES score of 50.24 compared to 44.52 for the conventional format, indicating a 1.12-fold increase in readability. These findings suggest that the improved CSR format significantly enhances comprehension and information delivery. The proposed quantitative analysis method offers a practical approach for evaluating and improving document design in construction safety, and it can be applied to other fields to improve the effectiveness of written communication.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".