Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique
Abstract Polyurethane-based polymer concrete (PUC) has become a popular material for pavement repair. However, its compressive strength (f c) is essential to achieve effective repair work. This study predicted the compressive strength and evaluated the non-destructive test (NDT) properties of the PU...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-08-01
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| Series: | International Journal of Concrete Structures and Materials |
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| Online Access: | https://doi.org/10.1186/s40069-025-00803-2 |
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| author | S. I. Haruna Han Zhu Yasser E. Ibrahim Jian Yang AIB Farouk Jianwen Shao Musa Adamu Omar Shabbir Ahmed |
| author_facet | S. I. Haruna Han Zhu Yasser E. Ibrahim Jian Yang AIB Farouk Jianwen Shao Musa Adamu Omar Shabbir Ahmed |
| author_sort | S. I. Haruna |
| collection | DOAJ |
| description | Abstract Polyurethane-based polymer concrete (PUC) has become a popular material for pavement repair. However, its compressive strength (f c) is essential to achieve effective repair work. This study predicted the compressive strength and evaluated the non-destructive test (NDT) properties of the PUC mixtures, prepared by mixing aggregate-to-polyurethane (PU) at 80/20, 85/15, and 90/10 ratios by weight. The experimental datasets from mechanical and NDT tests were utilized to train machine learning (ML) models, including multilinear regression (MLR), artificial neural network (ANN), support vector machine (SVM), Gaussian regression process (GPR), and stepwise regression (SWR) models for estimating the f c. Moreover, scanning electron microscopy (SEM) was employed to evaluate the microstructure of PUC. Feature selection tools were used to explore optimal input variables for estimating the (f c) of the PUC samples. The PUC-10 specimen revealed a maximum ultrasonic pulse velocity (UPV) value of 3.05 km/h. The microstructure analysis shows micro-voids with crack propagation between the aggregate and PU binder in the specimen containing 10% PU after testing. All the developed models showed high prediction accuracy. In addition, SVM outperformed other models at the training phase with R 2 values of 0.9614, and ANN demonstrated the highest performance at the testing phase with R 2 values of 0.9502. |
| format | Article |
| id | doaj-art-63bcfe73c99e4fd78e8f388b1ce78992 |
| institution | Kabale University |
| issn | 2234-1315 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Concrete Structures and Materials |
| spelling | doaj-art-63bcfe73c99e4fd78e8f388b1ce789922025-08-20T03:45:48ZengSpringerOpenInternational Journal of Concrete Structures and Materials2234-13152025-08-0119112310.1186/s40069-025-00803-2Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning TechniqueS. I. Haruna0Han Zhu1Yasser E. Ibrahim2Jian Yang3AIB Farouk4Jianwen Shao5Musa Adamu6Omar Shabbir Ahmed7Engineering Management Department, College of Engineering, Prince Sultan UniversitySchool of Civil Engineering, Tianjin UniversityEngineering Management Department, College of Engineering, Prince Sultan UniversitySchool of Civil Engineering, Tianjin UniversityInterdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and MineralsSchool of Civil Engineering, Ludong UniversityEngineering Management Department, College of Engineering, Prince Sultan UniversityEngineering Management Department, College of Engineering, Prince Sultan UniversityAbstract Polyurethane-based polymer concrete (PUC) has become a popular material for pavement repair. However, its compressive strength (f c) is essential to achieve effective repair work. This study predicted the compressive strength and evaluated the non-destructive test (NDT) properties of the PUC mixtures, prepared by mixing aggregate-to-polyurethane (PU) at 80/20, 85/15, and 90/10 ratios by weight. The experimental datasets from mechanical and NDT tests were utilized to train machine learning (ML) models, including multilinear regression (MLR), artificial neural network (ANN), support vector machine (SVM), Gaussian regression process (GPR), and stepwise regression (SWR) models for estimating the f c. Moreover, scanning electron microscopy (SEM) was employed to evaluate the microstructure of PUC. Feature selection tools were used to explore optimal input variables for estimating the (f c) of the PUC samples. The PUC-10 specimen revealed a maximum ultrasonic pulse velocity (UPV) value of 3.05 km/h. The microstructure analysis shows micro-voids with crack propagation between the aggregate and PU binder in the specimen containing 10% PU after testing. All the developed models showed high prediction accuracy. In addition, SVM outperformed other models at the training phase with R 2 values of 0.9614, and ANN demonstrated the highest performance at the testing phase with R 2 values of 0.9502.https://doi.org/10.1186/s40069-025-00803-2Machine learningPolyurethanePolymer concreteRepairCompressive strengthPavement |
| spellingShingle | S. I. Haruna Han Zhu Yasser E. Ibrahim Jian Yang AIB Farouk Jianwen Shao Musa Adamu Omar Shabbir Ahmed Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique International Journal of Concrete Structures and Materials Machine learning Polyurethane Polymer concrete Repair Compressive strength Pavement |
| title | Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique |
| title_full | Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique |
| title_fullStr | Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique |
| title_full_unstemmed | Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique |
| title_short | Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique |
| title_sort | sustainable polyurethane based polymer concrete mechanical and non destructive properties with machine learning technique |
| topic | Machine learning Polyurethane Polymer concrete Repair Compressive strength Pavement |
| url | https://doi.org/10.1186/s40069-025-00803-2 |
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