Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance
In this study, a comprehensive dataset containing 358 data points was collected from the literature, focusing on the compressive strength, split tensile strength, and modulus of elasticity of concrete made with recycled concrete aggregate (RCA). An Artificial Neural Network was used machine to predi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Materials Research Express |
| Online Access: | https://doi.org/10.1088/2053-1591/adb003 |
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| author | Aneel Manan Zhang Pu Ali Majdi Wael Alattyih S K Elagan Jawad Ahmad |
| author_facet | Aneel Manan Zhang Pu Ali Majdi Wael Alattyih S K Elagan Jawad Ahmad |
| author_sort | Aneel Manan |
| collection | DOAJ |
| description | In this study, a comprehensive dataset containing 358 data points was collected from the literature, focusing on the compressive strength, split tensile strength, and modulus of elasticity of concrete made with recycled concrete aggregate (RCA). An Artificial Neural Network was used machine to predict mechanical properties of RCA concrete. Furthermore, K-fold cross validation was utilized to validate the model’s reliability, and sensitivity analysis was performed to identify the most influential input parameters among the independent variables. The model demonstrated strong performance during training, achieving R ^2 values of 0.93 for compressive strength, 0.92 for split tensile strength, and 0.99 for modulus of elasticity with corresponding RMSE of 2.55, 3.85, and 0.37, respectively. The MAE and MAPE values during training were 0.68 and 0.03 for compressive strength, 0.71 and 0.03 for split tensile strength, and 0.08 and 0.01 for modulus of elasticity, respectively. Testing results revealed R ^2 values of 0.75 for compressive strength, 0.78 for split tensile strength, and 0.67 for modulus of elasticity, with RMSE values of 8.57, 5.03, and 3.83, respectively. Moreover, the sensitivity analysis indicated that the cement percentage and water-to-cement ratio were the main input parameters which significantly influence RCA concrete strength. |
| format | Article |
| id | doaj-art-8e3314c72d3448d481e7dbf8a100f01c |
| institution | OA Journals |
| issn | 2053-1591 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Materials Research Express |
| spelling | doaj-art-8e3314c72d3448d481e7dbf8a100f01c2025-08-20T02:13:36ZengIOP PublishingMaterials Research Express2053-15912025-01-0112202550410.1088/2053-1591/adb003Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performanceAneel Manan0https://orcid.org/0000-0002-2933-3939Zhang Pu1Ali Majdi2Wael Alattyih3S K Elagan4Jawad Ahmad5School of Civil Engineering, Zhengzhou University , Zhengzhou, 450001, People’s Republic of ChinaSchool of Civil Engineering, Zhengzhou University , Zhengzhou, 450001, People’s Republic of ChinaDepartment of Buildings and Construction Techniques Engineering, Al-Mustqbal University , 5100, Babylon, IraqDepartment of Civil Engineering, College of Engineering, Qassim University , Buraydah 51452, Saudi ArabiaDepartment of Mathematics and Statistics, College of Science, Taif University , Taif 21944, Saudi ArabiaSchool of Civil Engineering, National University of Science and Technology , Islamabad, 44000, PakistanIn this study, a comprehensive dataset containing 358 data points was collected from the literature, focusing on the compressive strength, split tensile strength, and modulus of elasticity of concrete made with recycled concrete aggregate (RCA). An Artificial Neural Network was used machine to predict mechanical properties of RCA concrete. Furthermore, K-fold cross validation was utilized to validate the model’s reliability, and sensitivity analysis was performed to identify the most influential input parameters among the independent variables. The model demonstrated strong performance during training, achieving R ^2 values of 0.93 for compressive strength, 0.92 for split tensile strength, and 0.99 for modulus of elasticity with corresponding RMSE of 2.55, 3.85, and 0.37, respectively. The MAE and MAPE values during training were 0.68 and 0.03 for compressive strength, 0.71 and 0.03 for split tensile strength, and 0.08 and 0.01 for modulus of elasticity, respectively. Testing results revealed R ^2 values of 0.75 for compressive strength, 0.78 for split tensile strength, and 0.67 for modulus of elasticity, with RMSE values of 8.57, 5.03, and 3.83, respectively. Moreover, the sensitivity analysis indicated that the cement percentage and water-to-cement ratio were the main input parameters which significantly influence RCA concrete strength.https://doi.org/10.1088/2053-1591/adb003 |
| spellingShingle | Aneel Manan Zhang Pu Ali Majdi Wael Alattyih S K Elagan Jawad Ahmad Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance Materials Research Express |
| title | Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance |
| title_full | Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance |
| title_fullStr | Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance |
| title_full_unstemmed | Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance |
| title_short | Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance |
| title_sort | sustainable optimization of concrete strength properties using artificial neural networks a focus on mechanical performance |
| url | https://doi.org/10.1088/2053-1591/adb003 |
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