Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete

Archives of Civil and Mechanical Engineering, Год журнала: 2024, Номер 25(1)

Опубликована: Ноя. 12, 2024

Abstract Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO 2 is produced throughout the cement-making process, which in contrary to current worldwide trend lowering emissions and conserving energy, thus restricting further advancement UHPC. Considering climate change sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC) materials have recently received considerable attention. Following emergence advanced prediction techniques aimed at reducing experimental tools labor costs, this study provides comparative different methods based on machine learning (ML) algorithms propose an active learning-based ML model (AL-Stacked ML) for predicting compressive strength AA-UHPC. A data-rich framework containing 284 datasets 18 input parameters was collected. comprehensive evaluation significance features that may affect AA-UHPC performed. Results confirm AL-Stacked ML-3 with accuracy 98.9% can be used general specimens, been tested research. Active improve up 4.1% enhance Stacked models. In addition, graphical user interface (GUI) introduced validated by tests facilitate comparable prospective studies predictions.

Язык: Английский

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Grey wolf optimizer integrated within boosting algorithm: Application in mechanical properties prediction of ultra high-performance concrete including carbon nanotubes

Applied Materials Today, Год журнала: 2025, Номер 42, С. 102601 - 102601

Опубликована: Янв. 18, 2025

Язык: Английский

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RAGN-R: A multi-subject ensemble machine-learning method for estimating mechanical properties of advanced structural materials

Computers & Structures, Год журнала: 2025, Номер 308, С. 107657 - 107657

Опубликована: Янв. 27, 2025

Язык: Английский

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Machine Learning-Assisted Prediction of Durability Behavior in Pultruded Fiber-Reinforced Polymeric (PFRP) Composites

Results in Engineering, Год журнала: 2025, Номер 25, С. 104198 - 104198

Опубликована: Янв. 31, 2025

Язык: Английский

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Transfer learning framework for modelling the compressive strength of ultra-high performance geopolymer concrete

Construction and Building Materials, Год журнала: 2025, Номер 459, С. 139746 - 139746

Опубликована: Янв. 1, 2025

Язык: Английский

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Explainable ensemble algorithms with grey wolf optimization for estimation of the tensile performance of polyethylene fiber-reinforced engineered cementitious composite

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112028 - 112028

Опубликована: Фев. 1, 2025

Язык: Английский

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Performance optimisation and predictive modelling of rice husk ash recycled concrete under the coupled action of freeze-thaw cycles and chloride erosion: Experimental study and machine learning

Construction and Building Materials, Год журнала: 2025, Номер 481, С. 141467 - 141467

Опубликована: Май 4, 2025

Язык: Английский

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Filament geometry control of printable geopolymer using experimental and data driven approaches

Construction and Building Materials, Год журнала: 2025, Номер 461, С. 139853 - 139853

Опубликована: Янв. 1, 2025

Язык: Английский

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Machine Learning Techniques for Estimating High–Temperature Mechanical Behavior of High Strength Steels

Results in Engineering, Год журнала: 2025, Номер 25, С. 104242 - 104242

Опубликована: Фев. 5, 2025

Язык: Английский

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A review on properties and multi-objective performance predictions of concrete based on machine learning models

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112017 - 112017

Опубликована: Фев. 1, 2025

Язык: Английский

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