Integrating Machine Learning with 3D Printing Concrete for Marine Structures: Mix Design, Strength Assessment, and Carbon Footprint Prediction
Issued Date
2026-01-01
Resource Type
ISSN
17551307
eISSN
17551315
Scopus ID
2-s2.0-105035517320
Journal Title
Iop Conference Series Earth and Environmental Science
Volume
1604
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Iop Conference Series Earth and Environmental Science Vol.1604 No.1 (2026)
Suggested Citation
Punurai W., Liu Y. Integrating Machine Learning with 3D Printing Concrete for Marine Structures: Mix Design, Strength Assessment, and Carbon Footprint Prediction. Iop Conference Series Earth and Environmental Science Vol.1604 No.1 (2026). doi:10.1088/1755-1315/1604/1/012012 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116263
Title
Integrating Machine Learning with 3D Printing Concrete for Marine Structures: Mix Design, Strength Assessment, and Carbon Footprint Prediction
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Author's Affiliation
Corresponding Author(s)
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Abstract
A well-designed mix ratio for 3D printing concrete (3DPC) is essential for successful application of this technology in marine structures. Adjusting the material mixes through physical experiments poses challenges for sustainability regarding waste production, energy consumption and greenhouse gas emissions. To develop better 3DPC mixes, this study employs the XGBoost machine learning algorithm to construct the predicted model. A dataset of 126 unique mix design datasets was collected from the literature and utilized in model training and development to forecast the 3DPC strength and carbon footprint. To demonstrate the effectiveness of the predicted model, visualization and assessment metrics such as scatter plots, Shapley Additive Explanations (SHAP), R-squared values, and mean absolute errors were reported. Based on the outcome of this study, the XGBoost model displayed accuracy with a high coefficient of determination. It was found that the cement content, printing speed, and admixtures were critical components that directly impact the 3DPC mixes. Future studies can consider other advanced ML models, hyperparameter adjustments, and a larger dataset to achieve better forecasts and interpretability.