Research on 3D printing concrete mechanical properties prediction model based on machine learning
This study proposes an effective machine learning-based prediction method to satisfy the urgent requirement to anticipate the mechanical properties of 3D-printed concrete. The goal is to support the accurate use of 3D printing technology in the building sector. We have successfully created machine l...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525000531 |
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| _version_ | 1832592795890614272 |
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| author | Yonghong Zhang Suping Cui Bohao Yang Xinxin Wang Tao Liu |
| author_facet | Yonghong Zhang Suping Cui Bohao Yang Xinxin Wang Tao Liu |
| author_sort | Yonghong Zhang |
| collection | DOAJ |
| description | This study proposes an effective machine learning-based prediction method to satisfy the urgent requirement to anticipate the mechanical properties of 3D-printed concrete. The goal is to support the accurate use of 3D printing technology in the building sector. We have successfully created machine learning models that can predict compressive strength and flexural strength by combining experimental data from a variety of 3D printed concrete samples and carefully preparing the data. Our study explores the fundamentals and practicality of several models, such as artificial neural networks, decision trees, random forests, support vector regression, and linear regression. We have made sure that our prediction findings are reliable and scientifically sound by implementing stringent model training and validation procedures. With a correlation coefficient between 0.96 and 0.98 with real values, experimental results demonstrate the random forest model's remarkable predicted accuracy, greatly beyond that of conventional prediction techniques. The practical use of 3D printed concrete in engineering projects is strengthened by this work, which also opens up new avenues for investigation and highlights the enormous potential of machine learning to improve the prediction of mechanical properties of building materials. |
| format | Article |
| id | doaj-art-77507755f85b4cca95f1f53ba04a4b9b |
| institution | Kabale University |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-77507755f85b4cca95f1f53ba04a4b9b2025-01-21T04:13:07ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e04254Research on 3D printing concrete mechanical properties prediction model based on machine learningYonghong Zhang0Suping Cui1Bohao Yang2Xinxin Wang3Tao Liu4Department of Materials Science and Engineering, Beijing University of Technology, China; SpaceDicon Technologies Company, China; Corresponding author at: Department of Materials Science and Engineering, Beijing University of Technology, China.Department of Materials Science and Engineering, Beijing University of Technology, China; Corresponding author.SpaceDicon Technologies Company, ChinaSpaceDicon Technologies Company, ChinaSpaceDicon Technologies Company, ChinaThis study proposes an effective machine learning-based prediction method to satisfy the urgent requirement to anticipate the mechanical properties of 3D-printed concrete. The goal is to support the accurate use of 3D printing technology in the building sector. We have successfully created machine learning models that can predict compressive strength and flexural strength by combining experimental data from a variety of 3D printed concrete samples and carefully preparing the data. Our study explores the fundamentals and practicality of several models, such as artificial neural networks, decision trees, random forests, support vector regression, and linear regression. We have made sure that our prediction findings are reliable and scientifically sound by implementing stringent model training and validation procedures. With a correlation coefficient between 0.96 and 0.98 with real values, experimental results demonstrate the random forest model's remarkable predicted accuracy, greatly beyond that of conventional prediction techniques. The practical use of 3D printed concrete in engineering projects is strengthened by this work, which also opens up new avenues for investigation and highlights the enormous potential of machine learning to improve the prediction of mechanical properties of building materials.http://www.sciencedirect.com/science/article/pii/S22145095250005313D printing concreteMachine learningMechanical properties predictionRandom forest modelData preprocessing |
| spellingShingle | Yonghong Zhang Suping Cui Bohao Yang Xinxin Wang Tao Liu Research on 3D printing concrete mechanical properties prediction model based on machine learning Case Studies in Construction Materials 3D printing concrete Machine learning Mechanical properties prediction Random forest model Data preprocessing |
| title | Research on 3D printing concrete mechanical properties prediction model based on machine learning |
| title_full | Research on 3D printing concrete mechanical properties prediction model based on machine learning |
| title_fullStr | Research on 3D printing concrete mechanical properties prediction model based on machine learning |
| title_full_unstemmed | Research on 3D printing concrete mechanical properties prediction model based on machine learning |
| title_short | Research on 3D printing concrete mechanical properties prediction model based on machine learning |
| title_sort | research on 3d printing concrete mechanical properties prediction model based on machine learning |
| topic | 3D printing concrete Machine learning Mechanical properties prediction Random forest model Data preprocessing |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525000531 |
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