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Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning (2021-11)

10.3390/app112210826

 Izadgoshasb Hamed,  Kandiri Amirreza,  Shakor Pshtiwan,  Laghi Vittoria,  Gasparini Giada
Journal Article - Applied Sciences, Vol. 11, Iss. 22

Abstract

Machine learning is the discipline of learning commands in the computer machine to predict and expect the results of real application and is currently the most promising simulation in artificial intelligence. This paper aims at using different algorithms to calculate and predict the compressive strength of extrusion 3DP concrete (cement mortar). The investigation is carried out using multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN). Given that the accuracy of a machine learning method depends on the number of data records, and for concrete 3D printing, this number is limited to few years of study, this work develops a new method by combining both methodologies into an ANNMOGOA approach to predict the compressive strength of 3D-printed concrete. Some promising results in the iteration process are achieved.

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BibTeX 
@article{izad_kand_shak_lagh.2021.PCSo3PMiSMUML,
  author            = "Hamed Izadgoshasb and Amirreza Kandiri and Pshtiwan N. Shakor and Vittoria Laghi and Giada Gasparini",
  title             = "Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning",
  doi               = "10.3390/app112210826",
  year              = "2021",
  journal           = "Applied Sciences",
  volume            = "11",
  number            = "22",
}
Formatted Citation

H. Izadgoshasb, A. Kandiri, P. N. Shakor, V. Laghi and G. Gasparini, “Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning”, Applied Sciences, vol. 11, no. 22, 2021, doi: 10.3390/app112210826.

Izadgoshasb, Hamed, Amirreza Kandiri, Pshtiwan N. Shakor, Vittoria Laghi, and Giada Gasparini. “Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning”. Applied Sciences 11, no. 22 (2021). https://doi.org/10.3390/app112210826.