Machine learning prediction of 28-day compressive strength of CNT/cement composites with considering size effects. (15th March 2023)

• Record Type: Journal Article
• Authors: Yang, Jinlong; Fan, Yucheng; Zhu, Fan; Ni, Zhi; Wan, Xili; Feng, Chuang; Yang, Jie
• Is Part Of: Composite structures. Volume 308(2023)
• Publication Date: 2023-03-15
• Subjects: Machine learning -- CNT/cement composite -- Compressive strength -- Size effect; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2023.116713
• Languages: English
• ISSNs: 0263-8223

Abstract

Abstract: It is challenging for either traditional modelling or experiments to capture the complex relationship between the strength of cement composites and the many influencing factors. Machine learning (ML) with powerful data analysis provides an elegant solution. This work adopts random forest (RF), AutoGluon-Tabular (AGT) and artificial neural network (ANN) to predict the 28-day compressive strength (CS) of carbon nanotube (CNT) reinforced cement composites (CNTRCCs), in which the effects of the specimen size is incorporated for the first time. In addition, this work introduces a Gaussian function to account for the distribution of CNT dimensions. An adaptive training strategy is proposed to improve the performance of the ML models. Specimen size is found to have significant effect on the 28-day CS of the CNTRCCs. ANN is evidenced to have the best performance whereas it requires intensive experience and computation. In contrast, AGT demonstrates improved efficiency and flexibility with satisfying predicted results.