Development and field based validation of a predictive model for concrete compressive strength using fresh state properties in a large scale construction project
Bibliographic record
Abstract
Estimation of a compressive strength of concrete earlier than for a standard 28 day test remains a highly challenging issue in construction industry on a large scale, where the quality control is merged with efficient scheduling and sustainable construction. This work presents a predictive model validated under real field conditions, through the processing of data collected in 4872 concrete cylinders produced in an infrastructure work with a total cast volume of 25,000 m³. The models are based on fresh state parameters temperature, slump, air content, and density that are commonly tested in the field, and are used to predict 7 and 28 days compressive strength for 35 and 40 MPa designs. Selected were three statistical methods including multiple linear regression as the base method, principal component analysis for dimensionality reduction, and LASSO regularization with interaction terms. Cross validation results showed the robust performance and the adjusted R² value was as high as 0.71 and RMSE was less than 3.4 MPa. While LASSO tightened model parsimony and did not reduce prediction efficiency, PCA increased precision in the context of multicollinearity and only slightly at the cost of precision. A strong correlation between early and late strength (0.84 and 0.74 for 35 MPa and 40 MPa, respectively) supports the use of early age results as practical predictors. The compressive strength prediction module was validated with real project data, providing ±RMSE confidence bands for operational use. These findings demonstrate that statistical modeling can be integrated into quality control workflows, enabling data driven decisions in concrete production and placement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".