Statistical Hypothesis Test Approach Considering Workers’ Thermal Comfort to Determine Nonworking Days in Different Climate Zones
Bibliographic record
Abstract
Construction duration is one of the three major elements in the construction industry, and accurate prediction is essential to minimize project variations stemming from inherent uncertainties. When evaluating construction duration, it is necessary to differentiate between working days and nonworking days, the latter influenced by holidays and weather conditions. While holidays are fixed, weather-related nonworking days introduce significant uncertainty due to varying climate characteristics, often evaluated using a single factor. This can lead to misestimation of nonworking days, increasing overall project uncertainties. Therefore, this study aims to propose a risk level based on outdoor thermal comfort and to provide nonworking day estimation criteria suitable for each climate zone. The study consists of three main phases: (1) data collection and classification from South Korea; (2) analysis of estimated risk levels considering outdoor thermal comfort; and (3) analysis of nonworking days by climate zone considering the estimated risk levels. The results of the study are as follows. When considering four different climate zones, the highest average number of nonworking days was observed for each zone as follows: Cfa (temperate climate with no dry season) (approximately 4.65 days, August), Cwa (temperate climate with dry winter) (approximately 3.92 days, August), Dfa (subarctic climate with no dry season) (approximately 4.61 days, August), and Dwa (subarctic climate with dry winter) (approximately 3.45 days, August). This study provides criteria for nonworking days specific to each climate zone in the construction industry, allowing for more accurate estimation of nonworking days by considering climate-specific characteristics. Furthermore, by comprehensively evaluating various weather conditions and incorporating worker safety levels, the results can serve as a quantitative index for establishing safety management measures. Finally, this study contributes to creating a safer working environment for workers by reducing construction accidents and mitigating delays due to weather-related nonworking days, thus preventing economic losses and protecting workers’ lives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".