Elevated Temperature Effects on Geo-Polymer Concrete: An Experimental and Numerical-Review Study
Bibliographic record
Abstract
The manufacture of cement plays a substantial role in the emission of carbon dioxide (CO2) into the atmosphere, hence exacerbating the adverse impacts of global warming.Consequently, the emergence of Geo-Polymer concrete has presented itself as a potentially feasible substitute owing to its commendable environmental sustainability.This manuscript provides a comprehensive analysis of prominent studies investigating the effects of increased temperatures and fire exposure on concrete across its entire operating duration.This study examines the significant impacts on the fundamental physical and mechanical characteristics of concrete, as revealed by laboratory experiments.Furthermore, this review comprehensively examines previous research endeavors that have used machine learning methodologies to predict tangible actions, aiming to optimize resource allocation, time efficiency, and cost-effectiveness in laboratory inquiries.Geo-Polymer concretes have exhibited remarkable resistance to elevated temperatures and severe fires, as evidenced by laboratory and field assessments of cracking, spalling, and strength degradation.Prior studies have demonstrated that both the aggregate type and temperature have a substantial impact on the degradation of compressive strength.Moreover, previous research has indicated that Geo-Polymeric concrete, which is comprised of fly ash, exhibits superior heat resistance compared to conventional concrete using Portland cement, withstanding temperatures of up to 400 degrees Celsius.This research also highlights the widespread adoption of the Artificial Neural Network (ANN) technique in forecasting the compressive strength of conventional concrete.Conversely, alternative approaches such as the Genetic Weighted Pyramid Operation Tree (GWPOT) are preferred for high-performance concrete.The primary objective of this extensive investigation is to establish a fundamental basis for future studies on sustainable alternatives to concrete and approaches for predictive modeling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".