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

Archives of Civil and Mechanical Engineering, Journal Year: 2024, Volume and Issue: 25(1)

Published: Nov. 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.

Language: 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, Journal Year: 2025, Volume and Issue: 42, P. 102601 - 102601

Published: Jan. 18, 2025

Language: Английский

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

Computers & Structures, Journal Year: 2025, Volume and Issue: 308, P. 107657 - 107657

Published: Jan. 27, 2025

Language: Английский

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

Results in Engineering, Journal Year: 2025, Volume and Issue: 25, P. 104198 - 104198

Published: Jan. 31, 2025

Language: Английский

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

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 459, P. 139746 - 139746

Published: Jan. 1, 2025

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 112028 - 112028

Published: Feb. 1, 2025

Language: 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, Journal Year: 2025, Volume and Issue: 481, P. 141467 - 141467

Published: May 4, 2025

Language: Английский

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

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 461, P. 139853 - 139853

Published: Jan. 1, 2025

Language: Английский

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

Results in Engineering, Journal Year: 2025, Volume and Issue: 25, P. 104242 - 104242

Published: Feb. 5, 2025

Language: Английский

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

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112017 - 112017

Published: Feb. 1, 2025

Language: Английский

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