Machine learning approaches to predict the strength of graphene nanoplatelets concrete: Optimization and hyper tuning with graphical user interface

Alahmari, Turki S.; Arif, Kiran

doi:10.1016/j.mtcomm.2024.109946

Materials Today Communications

2024

DOI: 10.1016/j.mtcomm.2024.109946

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Machine learning approaches to predict the strength of graphene nanoplatelets concrete: Optimization and hyper tuning with graphical user interface

Turki S. Alahmari,

Kiran Arif

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Cited by 2 publications

References 109 publications

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Analysis and prediction of powder particle morphology in the sintered NdFeB powdering process based on machine learning

Zhou,

Zhang,

Sun

et al. 2025

Materials Today Communications

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Analysis and prediction of powder particle morphology in the sintered NdFeB powdering process based on machine learning

Zhou,

Zhang,

Sun

et al. 2025

Materials Today Communications

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Carbon Capture and Storage Optimization with Machine Learning using an ANN model

Kotov,

Sravanthi,

Logabiraman

et al. 2024

E3S Web Conf.

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The purpose of this study is to evaluate the accuracy of predictions regarding the work capacity of CO2 and the selectivity of MOF, using machine learning methodologies in relation to CO2/N2. A dataset was used that includes numerous characteristics of MOFs for the development of a neural network model. The factors that determined the operational capacity of CO2 and the CO2/N2 selectivity included pore size, surface area, chemical composition, among others. The model demonstrated its work capacity by evaluating the selectivity of CO2; the mean absolute errors for the CO2/N2 selectivity were 25 and 0.8 mmol/g, respectively. The correlation Analysis showed a fairly negative correlation (-0.014) between the operational capacity of CO2 and its chemical makeup and a very positive correlation ( 0.029) between the surface area and amount of pore size. Thus, the gas absorbability is not top-dependent exclusively; pore size and surface area of a material contribute to the capacity as well. More research should be carried out to evaluate a machine learning capability on predicting the nature of different Flow Object Models (MOFs) with an aim of increasing efficiency, precision and dependability of the models.

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Machine learning approaches to predict the strength of graphene nanoplatelets concrete: Optimization and hyper tuning with graphical user interface

Cited by 2 publications

References 109 publications

Analysis and prediction of powder particle morphology in the sintered NdFeB powdering process based on machine learning

Analysis and prediction of powder particle morphology in the sintered NdFeB powdering process based on machine learning

Carbon Capture and Storage Optimization with Machine Learning using an ANN model

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