Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches
The application of waste materials in concrete is gaining more popularity for sustainable development. The adaptation of this approach not only reduces the environmental risks but also fulfills the requirement of concrete material. This study used the novel algorithms of machine learning (ML) to for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/12/5/569 |
| _version_ | 1827669549510557696 |
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| author | Yongzhong Zhu Ayaz Ahmad Waqas Ahmad Nikolai Ivanovich Vatin Abdeliazim Mustafa Mohamed Dina Fathi |
| author_facet | Yongzhong Zhu Ayaz Ahmad Waqas Ahmad Nikolai Ivanovich Vatin Abdeliazim Mustafa Mohamed Dina Fathi |
| author_sort | Yongzhong Zhu |
| collection | DOAJ |
| description | The application of waste materials in concrete is gaining more popularity for sustainable development. The adaptation of this approach not only reduces the environmental risks but also fulfills the requirement of concrete material. This study used the novel algorithms of machine learning (ML) to forecast the splitting tensile strength (STS) of concrete containing recycled aggregate (RA). The gene expression programming (GEP), artificial neural network (ANN), and bagging techniques were investigated for the selected database. Results reveal that the precision level of the bagging model is more accurate toward the prediction of STS of RA-based concrete as opposed to GEP and ANN models. The high value (0.95) of the coefficient of determination (R<sup>2</sup>) and lesser values of the errors (MAE, MSE, RMSE) were a clear indication of the accurate precision of the bagging model. Moreover, the statistical checks and k-fold cross-validation method were also incorporated to confirm the validity of the employed model. In addition, sensitivity analysis was also carried out to know the contribution level of each parameter toward the prediction of the outcome. The application of ML approaches for the anticipation of concrete’s mechanical properties will benefit the area of civil engineering by saving time, effort, and resources. |
| first_indexed | 2024-03-10T03:05:46Z |
| format | Article |
| id | doaj.art-6c4d4410de614d10982b45c10f18131d |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-03-10T03:05:46Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-6c4d4410de614d10982b45c10f18131d2023-11-23T10:33:48ZengMDPI AGCrystals2073-43522022-04-0112556910.3390/cryst12050569Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning ApproachesYongzhong Zhu0Ayaz Ahmad1Waqas Ahmad2Nikolai Ivanovich Vatin3Abdeliazim Mustafa Mohamed4Dina Fathi5Hunan Institute of Technology, School of Design and Art, Hengyang 421001, ChinaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanPeter the Great St. Petersburg Polytechnic University, 195291 St. Petersburg, RussiaDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaStructural Engineering and Construction Management Department, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11845, EgyptThe application of waste materials in concrete is gaining more popularity for sustainable development. The adaptation of this approach not only reduces the environmental risks but also fulfills the requirement of concrete material. This study used the novel algorithms of machine learning (ML) to forecast the splitting tensile strength (STS) of concrete containing recycled aggregate (RA). The gene expression programming (GEP), artificial neural network (ANN), and bagging techniques were investigated for the selected database. Results reveal that the precision level of the bagging model is more accurate toward the prediction of STS of RA-based concrete as opposed to GEP and ANN models. The high value (0.95) of the coefficient of determination (R<sup>2</sup>) and lesser values of the errors (MAE, MSE, RMSE) were a clear indication of the accurate precision of the bagging model. Moreover, the statistical checks and k-fold cross-validation method were also incorporated to confirm the validity of the employed model. In addition, sensitivity analysis was also carried out to know the contribution level of each parameter toward the prediction of the outcome. The application of ML approaches for the anticipation of concrete’s mechanical properties will benefit the area of civil engineering by saving time, effort, and resources.https://www.mdpi.com/2073-4352/12/5/569concreterecycled aggregateenvironment-friendly materialsplitting tensile strengthmachine learning |
| spellingShingle | Yongzhong Zhu Ayaz Ahmad Waqas Ahmad Nikolai Ivanovich Vatin Abdeliazim Mustafa Mohamed Dina Fathi Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches Crystals concrete recycled aggregate environment-friendly material splitting tensile strength machine learning |
| title | Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches |
| title_full | Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches |
| title_fullStr | Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches |
| title_full_unstemmed | Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches |
| title_short | Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches |
| title_sort | predicting the splitting tensile strength of recycled aggregate concrete using individual and ensemble machine learning approaches |
| topic | concrete recycled aggregate environment-friendly material splitting tensile strength machine learning |
| url | https://www.mdpi.com/2073-4352/12/5/569 |
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