Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives

Journal of Building Engineering, Год журнала: 2023, Номер 83, С. 108369 - 108369

Опубликована: Дек. 29, 2023

Язык: Английский

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Predictive models in machine learning for strength and life cycle assessment of concrete structures

Automation in Construction, Год журнала: 2024, Номер 162, С. 105412 - 105412

Опубликована: Апрель 3, 2024

Язык: Английский

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Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials

Computers & Structures, Год журнала: 2025, Номер 308, С. 107644 - 107644

Опубликована: Янв. 6, 2025

Язык: Английский

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Modified particle packing approach for optimizing waste marble powder as a cement substitute in high-performance concrete

Construction and Building Materials, Год журнала: 2023, Номер 409, С. 133845 - 133845

Опубликована: Окт. 31, 2023

Язык: Английский

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Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches

Structures, Год журнала: 2024, Номер 68, С. 107159 - 107159

Опубликована: Сен. 1, 2024

Язык: Английский

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Prediction of high-performance concrete strength using machine learning with hierarchical regression

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 7(5), С. 4911 - 4922

Опубликована: Май 16, 2024

Язык: Английский

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Data-driven predictive model of coal permeability based on microscopic fracture structure characterization

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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A critical analysis of compressive strength prediction of glass fiber and carbon fiber reinforced concrete over machine learning models

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(3)

Опубликована: Фев. 14, 2025

Язык: Английский

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Soft computing-based prediction models for compressive strength of concrete

Case Studies in Construction Materials, Год журнала: 2023, Номер 19, С. e02321 - e02321

Опубликована: Июль 22, 2023

The complexity of concrete's composition makes it difficult to predict its compressive strength, which is a highly valuable and desired characteristic. Traditional methods for prediction are expensive time-consuming, resulting in limited data availability. However, modern soft-computing models have emerged as reliable solution accurately forecasting strength. research proposes novel Deep Neural Network (DNN), Multivariate Adaptive Regression Splines (MARS) Extreme Learning Machine (ELM) based machine learning (ML) the strength concrete added with various proportions fly ash silica fume. For this purpose, dataset 144 trials, having 8 input parameters taken from literature. performance confirmed using statistical parameters. Rank Analysis reveals that DNN best-performing model (Rank =52, RTR2=0.983 RTs2=0.954), closely followed by MARS =38, RTR2=0.974 RTs2=0.956); while ELM lags behind other two counterparts. results further an error matrix, external validation AIC criteria. visual interpretation provided Taylor diagram. has edge over terms providing user-friendly solution.

Язык: Английский

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Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

REVIEWS ON ADVANCED MATERIALS SCIENCE, Год журнала: 2024, Номер 63(1)

Опубликована: Янв. 1, 2024

Abstract Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including consumption and mitigating environmental effects. The feasibility of RHA on strength was investigated this research by predicting the split tensile (SPT) flexural (FS) (RHAC). study used machine learning (ML) methods such ensemble stacking gene expression programming (GEP). model improved using base learner configurations ML models, as, random forest (RF), support vector regression, gradient boosting regression. proposed models were validated statistical tests external validation criteria. Moreover, effect input parameters Shapley adaptive exPlanations (SHAP) for RF parametric analysis GEP-based models. revealed that integrates predictions demonstrated superior performance, with R values greater than 0.98 0.96. Mean absolute error root mean square both SPT FS 0.23, 0.3, 0.5, 0.7 MPA, respectively. SHAP water, cement, superplasticizer, age influential RHAC strength. Furthermore, can be predicted an acceptable GEP expressions standard design procedure.

Язык: Английский

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