Exploratory Structural Equation Modeling Of Influencing Factors For Concrete Curing
Bibliographic record
Abstract
Concrete curing is a comprehensive construction activity that varies in duration and is critical to the quality and strength of the material when it hardens. An essential challenge of this activity is to choose the appropriate curing and testing methods for a wide assortment of concrete designs because the material is affected by multiple factors (e.g., temperature and moisture) and requires the collaboration of workers, engineers, and inspectors. This research proposes to explore the influencing factors and create a data model to describe the relationships of the factors, which can help project teams to understand the key elements of concrete curing and enhance the quality control of the construction activity. In this research, a questionnaire survey was designed, reviewed, and approved to collect the information from the departments of transportation in the U.S. and Canada with the purpose to understand the current curing practice of on-site concrete. The survey was delivered and managed using an online tool called Qualtrics and the received data was analyzed using an exploratory structural equation modeling (SEM) method. The analysis will reveal the underlying factors that cause patterns and also study indicators or actors to explain these factors. Next, a SEM will be built to assess the latent variables that cannot be observed but rather inferred based on prevailing factors and group these factors into sections based on their characteristics. After the model formation, the research will examine Cronbach's alpha to estimate the internal consistency of the identified factors and generalize the results.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".