Predicting High-Strength Concrete's Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology.
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| Title: | Predicting High-Strength Concrete's Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology. |
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| Authors: | Li, Tianlong1,2 (AUTHOR), Yang, Jianyu1 (AUTHOR) jianyuy@csust.edu.cn, Jiang, Pengxiao3 (AUTHOR), AlAteah, Ali H.4 (AUTHOR) ali.alateah@uhb.edu.sa, Alsubeai, Ali5 (AUTHOR) subeaia@rcjy.edu.sa, Alfares, Abdulgafor M.6 (AUTHOR) aalfares@uhb.edu.sa, Sufian, Muhammad7 (AUTHOR) jianyuy@csust.edu.cn |
| Source: | Materials (1996-1944). Sep2024, Vol. 17 Issue 18, p4533. 37p. |
| Subjects: | Artificial neural networks, Computer input design, Compressive strength, Response surfaces (Statistics), Construction materials |
| Abstract: | Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict the compressive strength of high-strength concrete (HSC) using different methods. To achieve this purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), and response surface methodology (RSM) were used as ensemble methods. Using an ANN and ANFIS, high-strength concrete (HSC) output was modeled and optimized as a function of five independent variables. The RSM was designed with three input variables: cement, and fine and coarse aggregate. To facilitate data entry into Design Expert, the RSM model was divided into six groups, with p-values of responses 1 to 6 of 0.027, 0.010, 0.003, 0.023, 0.002, and 0.026. The following metrics were used to evaluate model compressive strength projection: R, R2, and MSE for ANN and ANFIS modeling; R2, Adj. R2, and Pred. R2 for RSM modeling. Based on the data, it can be concluded that the ANN model (R = 0.999, R2 = 0.998, and MSE = 0.417), RSM model (R = 0.981 and R2 = 0.963), and ANFIS model (R = 0.962, R2 = 0.926, and MSE = 0.655) have a good chance of accurately predicting the compressive strength of high-strength concrete (HSC). Furthermore, there is a strong correlation between the ANN, RSM, and ANFIS models and the experimental data. Nevertheless, the artificial neural network model demonstrates exceptional accuracy. The sensitivity analysis of the ANN model shows that cement and fine aggregate have the most significant effect on predicting compressive strength (45.29% and 35.87%, respectively), while superplasticizer has the least effect (0.227%). RSME values for cement and fine aggregate in the ANFIS model were 0.313 and 0.453 during the test process and 0.733 and 0.563 during the training process. Thus, it was found that both ANN and RSM models presented better results with higher accuracy and can be used for predicting the compressive strength of construction materials. [ABSTRACT FROM AUTHOR] |
| Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 179966422 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting High-Strength Concrete's Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Tianlong%22">Li, Tianlong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jianyu%22">Yang, Jianyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jianyuy@csust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Pengxiao%22">Jiang, Pengxiao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22AlAteah%2C+Ali+H%2E%22">AlAteah, Ali H.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> ali.alateah@uhb.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Alsubeai%2C+Ali%22">Alsubeai, Ali</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> subeaia@rcjy.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Alfares%2C+Abdulgafor+M%2E%22">Alfares, Abdulgafor M.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> aalfares@uhb.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Sufian%2C+Muhammad%22">Sufian, Muhammad</searchLink><relatesTo>7</relatesTo> (AUTHOR)<i> jianyuy@csust.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Sep2024, Vol. 17 Issue 18, p4533. 37p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+input+design%22">Computer input design</searchLink><br /><searchLink fieldCode="DE" term="%22Compressive+strength%22">Compressive strength</searchLink><br /><searchLink fieldCode="DE" term="%22Response+surfaces+%28Statistics%29%22">Response surfaces (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Construction+materials%22">Construction materials</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict the compressive strength of high-strength concrete (HSC) using different methods. To achieve this purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), and response surface methodology (RSM) were used as ensemble methods. Using an ANN and ANFIS, high-strength concrete (HSC) output was modeled and optimized as a function of five independent variables. The RSM was designed with three input variables: cement, and fine and coarse aggregate. To facilitate data entry into Design Expert, the RSM model was divided into six groups, with p-values of responses 1 to 6 of 0.027, 0.010, 0.003, 0.023, 0.002, and 0.026. The following metrics were used to evaluate model compressive strength projection: R, R2, and MSE for ANN and ANFIS modeling; R2, Adj. R2, and Pred. R2 for RSM modeling. Based on the data, it can be concluded that the ANN model (R = 0.999, R2 = 0.998, and MSE = 0.417), RSM model (R = 0.981 and R2 = 0.963), and ANFIS model (R = 0.962, R2 = 0.926, and MSE = 0.655) have a good chance of accurately predicting the compressive strength of high-strength concrete (HSC). Furthermore, there is a strong correlation between the ANN, RSM, and ANFIS models and the experimental data. Nevertheless, the artificial neural network model demonstrates exceptional accuracy. The sensitivity analysis of the ANN model shows that cement and fine aggregate have the most significant effect on predicting compressive strength (45.29% and 35.87%, respectively), while superplasticizer has the least effect (0.227%). RSME values for cement and fine aggregate in the ANFIS model were 0.313 and 0.453 during the test process and 0.733 and 0.563 during the training process. Thus, it was found that both ANN and RSM models presented better results with higher accuracy and can be used for predicting the compressive strength of construction materials. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/ma17184533 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 37 StartPage: 4533 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Computer input design Type: general – SubjectFull: Compressive strength Type: general – SubjectFull: Response surfaces (Statistics) Type: general – SubjectFull: Construction materials Type: general Titles: – TitleFull: Predicting High-Strength Concrete's Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Tianlong – PersonEntity: Name: NameFull: Yang, Jianyu – PersonEntity: Name: NameFull: Jiang, Pengxiao – PersonEntity: Name: NameFull: AlAteah, Ali H. – PersonEntity: Name: NameFull: Alsubeai, Ali – PersonEntity: Name: NameFull: Alfares, Abdulgafor M. – PersonEntity: Name: NameFull: Sufian, Muhammad IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 19961944 Numbering: – Type: volume Value: 17 – Type: issue Value: 18 Titles: – TitleFull: Materials (1996-1944) Type: main |
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