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A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions

Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a...

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Autores principales: Chen, Shuzhao, Zhou, Mengmeng, Shi, Xuyang, Huang, Jiandong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297668/
https://www.ncbi.nlm.nih.gov/pubmed/37367105
http://dx.doi.org/10.3390/gels9060434
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author Chen, Shuzhao
Zhou, Mengmeng
Shi, Xuyang
Huang, Jiandong
author_facet Chen, Shuzhao
Zhou, Mengmeng
Shi, Xuyang
Huang, Jiandong
author_sort Chen, Shuzhao
collection PubMed
description Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.
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spelling pubmed-102976682023-06-28 A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions Chen, Shuzhao Zhou, Mengmeng Shi, Xuyang Huang, Jiandong Gels Article Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667. MDPI 2023-05-24 /pmc/articles/PMC10297668/ /pubmed/37367105 http://dx.doi.org/10.3390/gels9060434 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Shuzhao
Zhou, Mengmeng
Shi, Xuyang
Huang, Jiandong
A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions
title A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions
title_full A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions
title_fullStr A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions
title_full_unstemmed A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions
title_short A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions
title_sort novel mbas-rf approach to predict mechanical properties of geopolymer-based compositions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297668/
https://www.ncbi.nlm.nih.gov/pubmed/37367105
http://dx.doi.org/10.3390/gels9060434
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