Abstract
In this paper, 871 data were collected from literature and trained by the 4 representative machine learning methods, in order to build a robust compressive strength predictive model for slag and fly ash based alkali activated concretes. The optimum models of each machine learning method were verified by 4 validation metrics and further compared with an empirical formula and experimental results. Besides, a literature study was carried out to investigate the connection between compressive strength and other mechanical characteristics. As a result, the gradient boosting regression trees model and several predictive formulas were eventually proposed for the prediction of the mechanical behavior including compressive strength, elastic modulus, splitting tensile strength, flexural strength, and Poisson's ratio of BFS/FA-AACs. The importance index of each parameter on the strength of BFS/FA-AACs was elaborated as well.
Original language | English |
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Article number | 133933 |
Number of pages | 14 |
Journal | Construction and Building Materials |
Volume | 409 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Elastic modulus
- Machine learning
- Poisson's ratio
- Prediction
- Slag and fly ash-based alkali-activated concrete
- Strength