Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm

Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm

Sun, Yubo (Universiteit Gent)
Cheng, H. (TU Delft Concrete Structures)
Zhang, Shizhe (TU Delft Materials and Environment)
Mohan, Manu K. (Universiteit Gent)
Ye, G. (TU Delft Materials and Environment; Universiteit Gent)
De Schutter, Geert (Universiteit Gent)

2023

Alkali-activated concrete (AAC) is regarded as a promising alternative construction material to reduce the CO₂ emission induced by Portland cement (PC) concrete. Due to the diversity in raw materials and complexity of reaction mechanisms, a commonly applied design code is still absent to date. This study attempts to directly correlate the AAC mix design parameters to their performances through an artificial intelligence approach. To be specific, 145 fresh property data and 193 mechanical strength data were collected from laboratory tests on 52 AAC mixtures, which were used as inputs for the machine learning algorithm. Five independent random forest (RF) models were established, which are able to predict fresh and hardened properties (in terms of compressive strength, slump values, static/dynamic yield stress, and plastic viscosity) of AAC with equivalent accuracy reported in the literature. Moreover, an inverse optimization was performed on the RF model obtained to reduce the sodium silicate dosages, which may further mitigate the environmental impact of producing AAC. The present RF model gives practical information on AAC mix design cases.

Alkali-activated concrete
Machine learning
Mix design
Optimization
Prediction
Random forest

http://resolver.tudelft.nl/uuid:7a5919f9-3b2d-4d34-87d9-71f656dd0133

https://doi.org/10.1016/j.conbuildmat.2023.131519

2023-10-27

0950-0618

Construction and Building Materials, 385

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-care Otherwise 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.

Institutional Repository

journal article

© 2023 Yubo Sun, H. Cheng, Shizhe Zhang, Manu K. Mohan, G. Ye, Geert De Schutter