Using structural equation modeling to analyze relationships among key causes of delay in construction
Why this work is in the frame
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Bibliographic record
Abstract
Schedule delays frequently occur in construction projects. The first step in resolving delay problems is to identify the main causes of delay. Previous studies identifying significant causes of delay have not examined how different causes work together to influence project schedule delays. Structural equation modeling (SEM) of causes of delay in construction has been developed for describing and quantifying the influence of different causes. Although this empirical study is based on a survey in Taiwan’s construction environment, the proposed model is applicable to construction industries in other countries. The analytical results clearly show the correlations among key causes of delay, which is the basis for resolving future schedule delays. This study proved that SEM is capable of quantifying the comprehensive relationships among investigated factors. Additionally, SEM has a high potential to resolve experience-oriented problems in the construction industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it