Advancing Construction 3D Printing with Predictive Interlayer Bonding Strength: A Stacking Model Paradigm

Autor:	Dinglue Wu, Qiling Luo, Wujian Long, Shunxian Zhang, Songyuan Geng
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	intelligent prediction 3D printing concrete interlayer bonding strength machine learning stacking strategy Technology Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Zdroj:	Materials, Vol 17, Iss 5, p 1033 (2024)
Druh dokumentu:	article
ISSN:	1996-1944
DOI:	10.3390/ma17051033
Popis:	To enhance the quality stability of 3D printing concrete, this study introduces a novel machine learning (ML) model based on a stacking strategy for the first time. The model aims to predict the interlayer bonding strength (IBS) of 3D printing concrete. The base models incorporate SVR, KNN, and GPR, and subsequently, these models are stacked to create a robust stacking model. Results from 10-fold cross-validation and statistical performance evaluations reveal that, compared to the base models, the stacking model exhibits superior performance in predicting the IBS of 3D printing concrete, with the R2 value increasing from 0.91 to 0.96. This underscores the efficacy of the developed stacking model in significantly improving prediction accuracy, thereby facilitating the advancement of scaled-up production in 3D printing concrete.
Databáze:	Directory of Open Access Journals
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