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Using Computer Vision for Monitoring the Quality of 3D-Printed Concrete Structures

Author

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  • Shanmugaraj Senthilnathan

    (Civil Engineering Department, Indian Institute of Technology Madras, Chennai 600036, India)

  • Benny Raphael

    (Civil Engineering Department, Indian Institute of Technology Madras, Chennai 600036, India)

Abstract

Concrete 3D printing has the potential to reduce material and process waste in construction. Thus, it contributes to making the construction industry more sustainable through the use of digital-fabrication technologies. While concrete 3D printing is attractive due to its potential to realize complex designs, practical challenges include an increased chance of defects and deformities. Quality assessment of 3D-printed elements is essential for large-scale implementation. Workability of concrete is known to decrease with printing time and it impacts extrudability. It is usually visible in 3D-printed elements, with the lower layers having a smooth finish, while the top layers have cracks and discontinuities. A computer-vision-based quality assessment method is proposed in this paper using a two-bin Linear Binary Pattern textural analysis. Information entropy is used as the metric for measuring the texture variation within each layer and its changes over the layers are studied. A higher entropy value is found for layers having deformities. Finally, through the error-minimization technique, a threshold entropy value is calculated and, using this, the printed layers can be assessed and corrective actions taken. This paper contributes to developing a non-intrusive quality assessment technique for concrete 3D-printed elements.

Suggested Citation

  • Shanmugaraj Senthilnathan & Benny Raphael, 2022. "Using Computer Vision for Monitoring the Quality of 3D-Printed Concrete Structures," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15682-:d:983865
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    References listed on IDEAS

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    1. Dazhong Wu & Yupeng Wei & Janis Terpenny, 2019. "Predictive modelling of surface roughness in fused deposition modelling using data fusion," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3992-4006, June.
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    Cited by:

    1. Reza Sedghi & Muhammad Saeed Zafar & Maryam Hojati, 2023. "Exploring Fresh and Hardened Properties of Sustainable 3D-Printed Lightweight Cementitious Mixtures," Sustainability, MDPI, vol. 15(19), pages 1-37, October.

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