Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 26, 2025

The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay various influencing factors and guide mix design for improved compressive strength sustainability. Ensemble methods symbolic regression are promising approaches this task due their complementary strengths solving challenges associated with repeated experiments laboratory. Choosing machine learning predictions over repeated, expensive, time-consuming research projects, such as optimizing utilization concrete, presents a paradigm shift how data-driven insights can revolutionize material development. integration ensemble enables researchers derive valuable optimize critical performance parameters efficiently. In work, 235 records were collected from extensive literature search different mixing ratios metakaolin-based at ages. Each record contains MK: content (kg/m3), SHS: Sodium hydroxide solution SHSM: molarity (Mole), SSS: silicate W: Extra water (not including alkaline solutions) W/S: Water Solid ratio (Total / part activator solutions + MK), Na2O/Al2O3: oxide aluminium ratio, SiO2/Al2O3: Silicon H2O/Na2O: CA/FA: Coarse Fine aggregate CAg: coarse aggregates SP: super-plasticizer PCC: 0 no pre-curing, 1 pre-curing 60 °C, 2 80 CT: Curing temperature (°C), Age: age testing (days) CS: Compressive (MPa). portioned into training set (180 records≈75%) validation (55 records≈ 25%) modeled methods. At end model metrics used evaluate models' ability Hoffman Gardener's sensitivity analysis was impact variables on mixed metakaolin. GB KNN became decisive excellent which outclassed others indicated that SHSM, SSS, W/S, Na2O/Al2O3 most influential predicted strength.

Язык: Английский

---

Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning

Journal of Composites Science, Год журнала: 2024, Номер 8(8), С. 287 - 287

Опубликована: Июль 26, 2024

High-Performance Concrete (HPC) and Ultra-High-Performance (UHPC) have many applications in civil engineering industries. These two types of concrete as similarities they differences with each other, such the mix design additive powders like silica fume, metakaolin, various fibers, however, optimal percentages mixture properties element these concretes are completely different. This study investigated between to find better mechanical behavior through parameters concrete. In addition, this paper studied correlation matrix machine learning method predict relationship elements properties. way, Linear, Ridge, Lasso, Random Forest, K-Nearest Neighbors (KNN), Decision tree, Partial least squares (PLS) regressions been chosen best regression types. To accuracy, coefficient determination (R2), mean absolute error (MAE), root-mean-square (RMSE) were selected. Finally, PLS, Lasso had results than other regressions, R2 greater 93%, 92%, respectively. general, present shows that HPC UHPC different designs for predicting

Язык: Английский

---

Strength and durability predictions of ternary blended nano-engineered high-performance concrete: Application of hybrid machine learning techniques with bio-inspired optimization

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 148, С. 110470 - 110470

Опубликована: Март 6, 2025

Язык: Английский

---

Comparative analysis of daily global solar radiation prediction using deep learning models inputted with stochastic variables

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 28, 2025

Photovoltaic power plant outputs depend on the daily global solar radiation (DGSR). The main issue with DGSR data is its lack of precision. potential unavailability for several sites can be attributed to high cost measuring instruments and intermittent nature time series due equipment malfunctions. Therefore, prediction research crucial nowadays produce photovoltaic power. Different artificial neural network (ANN) models will give different predictions varying levels accuracy, so it essential compare ANN model inputs various sets meteorological stochastic variables. In this study, radial basis function (RBFNN), long short-term memory (LSTMNN), modular (MNN), transformer (TM) are developed investigate performances these algorithms using combinations These employ five variables: wind speed, relative humidity, minimum, maximum, average temperatures. mean absolute error input variables as average, minimum temperatures 1.98. outperform traditional in predictive accuracy.

Язык: Английский

---

Next-Generation Cutting-Edge Hybrid AI Frameworks for Predicting Rheological Properties and CO₂ Emissions in Alkali-Activated Concrete

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04754 - e04754

Опубликована: Май 1, 2025

Язык: Английский

---

Harnessing machine learning for accurate estimation of compressive strength of high-performance self-compacting concrete from non-destructive tests: A comparative study

Construction and Building Materials, Год журнала: 2024, Номер 451, С. 138779 - 138779

Опубликована: Окт. 21, 2024

Язык: Английский

---

Harnessing machine learning for accurate estimation of concrete strength using non-destructive tests: a comparative study

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 8(1)

Опубликована: Ноя. 7, 2024

Язык: Английский

---

Machine learning-based prediction of shear strength in interior beam-column joints

Deleted Journal, Год журнала: 2025, Номер 7(5)

Опубликована: Май 8, 2025

Язык: Английский

---

Unlocking precision in hydraulic engineering: machine learning insights into labyrinth sluice gate discharge coefficients

Journal of Hydroinformatics, Год журнала: 2024, Номер 26(11), С. 2883 - 2901

Опубликована: Ноя. 1, 2024

ABSTRACT This study investigates the discharge coefficient (Cd) of labyrinth sluice gates, a modern gate design with complex flow characteristics. To accurately estimate Cd, regression techniques (linear and stepwise polynomial regression) machine learning methods (gene expression programming (GEP), decision table, KStar, M5Prime) were employed. A dataset 187 experimental results, incorporating dimensionless variables internal angle (θ), cycle number (N), water depth contraction ratio (H/G), was used to train evaluate models. The results demonstrate superiority GEP in predicting achieving determination (R2) 97.07% mean absolute percentage error 2.87%. assess relative importance each variable, sensitivity analysis conducted. revealed that H/G has most significant impact on followed by head (θ). (N) found have relatively insignificant effect. These findings offer valuable insights into operation contributing improved resource management flood control.

Язык: Английский

---

Prediction of concrete strength by hierarchical stepwise regression using ultrasonic pulse velocity and Schmidt rebound hammer

Innovative Infrastructure Solutions, Год журнала: 2024, Номер 9(12)

Опубликована: Ноя. 27, 2024

Язык: Английский

---