Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381930101> ?p ?o ?g. }
- W4381930101 endingPage "132179" @default.
- W4381930101 startingPage "132179" @default.
- W4381930101 abstract "The prediction of the compressive strength (CS) of graphene oxide reinforced cement composites (GORCCs) is crucial for accelerating their potential application in civil engineering. However, traditional experimental and theoretical modelling suffer from problems such as time-consuming, costly, and inefficient, etc. It is also challenging to consider the effects of multiple coupling factors. In this work, machine learning (ML) approaches are developed as the first attempt to explore the complex relationships between the CS of GORCCs and the multiple coupling factors. A comprehensive dataset of 260 experimental results is collected to train and test the ML models. It is demonstrated that the developed model can accurately predict the CS of GORCCs. The feature importance analysis reveals that dispersion of sonicating GO in polycarboxylate superplasticizer solution is the most favorable dispersion method for achieving good dispersion. Among the ML models used, it is found that the AutoGluon-Tabular (AGT) model not only demonstrates the highest confidence in predictions but also offers better interpretability of the results. Moreover, users can train AGT models more efficiently compared to traditional ML workflows, avoiding the time-consuming process of hyperparameter tuning." @default.
- W4381930101 created "2023-06-25" @default.
- W4381930101 creator A5010736360 @default.
- W4381930101 creator A5051438534 @default.
- W4381930101 creator A5067493344 @default.
- W4381930101 creator A5072296652 @default.
- W4381930101 creator A5075286699 @default.
- W4381930101 creator A5077269879 @default.
- W4381930101 creator A5082635448 @default.
- W4381930101 creator A5084648633 @default.
- W4381930101 date "2023-08-01" @default.
- W4381930101 modified "2023-10-16" @default.
- W4381930101 title "Comparison of traditional and automated machine learning approaches in predicting the compressive strength of graphene oxide/cement composites" @default.
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- W4381930101 doi "https://doi.org/10.1016/j.conbuildmat.2023.132179" @default.
- W4381930101 hasPublicationYear "2023" @default.