Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete

Ammar Ali, Raja Dilawar Riaz, Umair Jalil Malik, Syed Baqar Abbas, Muhammad Usman*, Mati Ullah Shah, In Ho Kim, Asad Hanif, Muhammad Faizan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m3), cement (Kg/m3), silica fume (Kg/m3), fly ash (Kg/m3), coarse aggregate (Kg/m3 & mm for diameter), fine aggregate (Kg/m3 & mm for diameter), viscosity modifying agent (Kg/m3), fibers (Kg/m3), fiber properties (mm for diameter and MPa for strength), print speed (mm/sec), and nozzle area (mm2), while target properties were the flexural and tensile strength of concrete (MPa data from 25 literature studies were collected. The water/binder ratio used in the dataset ranged from 0.27 to 0.67. Different types of sands and fibers have been used, with fibers having a maximum length of 23 mm. Based upon the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) for casted and printed concrete, the SVM model performed better than other models. All models’ cast and printed flexural strength values were also correlated. The model’s performance has also been checked on six different mix proportions from the dataset to show its accuracy. It is worth noting that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes this study a novel innovation in the field. This model could reduce the computational and experimental effort required to formulate the mixed design of printed concrete.

Original languageEnglish
Article number4149
Issue number11
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.


  • 3D-printed concrete
  • additive manufacturing
  • decision tree
  • flexural strength
  • machine learning
  • predictive models

ASJC Scopus subject areas

  • General Materials Science
  • Condensed Matter Physics


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