Compressive strength of waste-derived cementitious composites using machine learning

Marble cement (MC) is a new binding material for concrete, and the strength assessment of the resulting materials is the subject of this investigation. MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithms can help with...

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Vydané v:Reviews on advanced materials science Ročník 63; číslo 1; s. id. 126 - 573
Hlavní autori: Tian, Qiong, Lu, Yijun, Zhou, Ji, Song, Shutong, Yang, Liming, Cheng, Tao, Huang, Jiandong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: De Gruyter 15.05.2024
Predmet:
gene expression programming
marble cement
multi-expression programming
ISSN:1605-8127, 1605-8127
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Shrnutí:Marble cement (MC) is a new binding material for concrete, and the strength assessment of the resulting materials is the subject of this investigation. MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithms can help with the formulation of better MC-based concrete. ML models that could predict the compressive strength (CS) of MC-based concrete that contained FA and RHA were built. Gene expression programming (GEP) and multi-expression programming (MEP) were used to build these models. Additionally, models were evaluated by calculating values, carrying out statistical tests, creating Taylor’s diagram, and comparing theoretical and experimental readings. When comparing the MEP and GEP models, MEP yielded a slightly better-fitted model and better prediction performance ( = 0.96, mean absolute error = 0.646, root mean square error = 0.900, and Nash–Sutcliffe efficiency = 0.960). According to the sensitivity analysis, the prediction of CS was most affected by curing age and MC content, then by FA and RHA contents. Incorporating waste materials such as marble powder, RHA, and FA into building materials can help reduce environmental impacts and encourage sustainable development.
ISSN:1605-8127
1605-8127
DOI:10.1515/rams-2024-0008