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Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete
Abstrakt
Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions and conserving energy, thus restricting the further advancement of UHPC. Considering climate change and sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC) materials have recently received considerable attention. Following the emergence of advanced prediction techniques aimed at reducing experimental tools and labor costs, this study provides a comparative study of different methods based on machine learning (ML) algorithms to propose an active learning-based ML model (AL-Stacked ML) for predicting the compressive strength of AA-UHPC. A data-rich framework containing 284 experimental datasets and 18 input parameters was collected. A comprehensive evaluation of the significance of input features that may affect compressive strength of AA-UHPC was performed. Results confirm that AL-Stacked ML-3 with accuracy of 98.9% can be used for different general experimental specimens, which have been tested in this research. Active learning can improve the accuracy up to 4.1% and further enhance the Stacked ML models. In addition, graphical user interface (GUI) was introduced and validated by experimental tests to facilitate comparable prospective studies and predictions.
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Autorzy (4)
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Torkan Shafighfard
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Doo-Yeol Yoo
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Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s43452-024-01067-5
- Licencja
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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Archives of Civil and Mechanical Engineering
nr 25,
ISSN: 1644-9665 - Język:
- angielski
- Rok wydania:
- 2025
- Opis bibliograficzny:
- Kazemi F., Shafighfard T., Jankowski R., Yoo D.: Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete// Archives of Civil and Mechanical Engineering -Vol. 25,iss. 1 (2024),
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s43452-024-01067-5
- Źródła finansowania:
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- Publikacja bezkosztowa
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 55 razy
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