A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting Concrete
It is observed from the published literature that there were so few studies concentrating on predicting both fresh and hardened properties of self-compacting concrete (SCC). Hence, it is tried to develop models for predicting the fresh and hardened properties of SCC by the optimized radial basis fun...
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Bilijipub publisher
2022-04-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_148305_6afcd4a8c0c370a913dd30ce7ef2a1f9.pdf |
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| author | Zhangabay Nurlan |
| author_facet | Zhangabay Nurlan |
| author_sort | Zhangabay Nurlan |
| collection | DOAJ |
| description | It is observed from the published literature that there were so few studies concentrating on predicting both fresh and hardened properties of self-compacting concrete (SCC). Hence, it is tried to develop models for predicting the fresh and hardened properties of SCC by the optimized radial basis function neural network (RBFNN) method. The RBFNN method's key parameters are optimized using ant-lion optimization (ALO) and biogeography optimization (BBO) algorithms. The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and compressive strength (CS) in the hardened phase. Results demonstrate powerful potential in the learning section as well as approximation in the testing phase. It means that the correlation between observed and predicted properties of SCC from hybrid models is acceptable so that it represents high accuracy in the training and approximating process. Regarding D flow, L-box, Vfunnel, and CS, the results of ALO-RBFNN were better than BBO-RBFNN and the literature. Overall, the RBFNN model developed by ALO outperforms others, which depicts the capability of the ALO algorithm for determining the optimal parameters of the considered method. |
| format | Article |
| id | doaj-art-dfc9c226451c4913b239c49cff26085b |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-dfc9c226451c4913b239c49cff26085b2025-02-12T08:46:07ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632022-04-0100101506410.22034/aeis.2022.148305148305A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting ConcreteZhangabay Nurlan0Department of Industrial, Civil and Road Construction, M. Auezov South Kazakhstan State University, Shymkent, 160012, KazakhstanIt is observed from the published literature that there were so few studies concentrating on predicting both fresh and hardened properties of self-compacting concrete (SCC). Hence, it is tried to develop models for predicting the fresh and hardened properties of SCC by the optimized radial basis function neural network (RBFNN) method. The RBFNN method's key parameters are optimized using ant-lion optimization (ALO) and biogeography optimization (BBO) algorithms. The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and compressive strength (CS) in the hardened phase. Results demonstrate powerful potential in the learning section as well as approximation in the testing phase. It means that the correlation between observed and predicted properties of SCC from hybrid models is acceptable so that it represents high accuracy in the training and approximating process. Regarding D flow, L-box, Vfunnel, and CS, the results of ALO-RBFNN were better than BBO-RBFNN and the literature. Overall, the RBFNN model developed by ALO outperforms others, which depicts the capability of the ALO algorithm for determining the optimal parameters of the considered method.https://aeis.bilijipub.com/article_148305_6afcd4a8c0c370a913dd30ce7ef2a1f9.pdfself-compacting concretefly ashrheological propertiescompressive strengthradial basis function neural network |
| spellingShingle | Zhangabay Nurlan A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting Concrete Advances in Engineering and Intelligence Systems self-compacting concrete fly ash rheological properties compressive strength radial basis function neural network |
| title | A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting Concrete |
| title_full | A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting Concrete |
| title_fullStr | A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting Concrete |
| title_full_unstemmed | A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting Concrete |
| title_short | A Novel Hybrid Radial Basis Function Method for Predicting the Fresh and Hardened Properties of Self-Compacting Concrete |
| title_sort | novel hybrid radial basis function method for predicting the fresh and hardened properties of self compacting concrete |
| topic | self-compacting concrete fly ash rheological properties compressive strength radial basis function neural network |
| url | https://aeis.bilijipub.com/article_148305_6afcd4a8c0c370a913dd30ce7ef2a1f9.pdf |
| work_keys_str_mv | AT zhangabaynurlan anovelhybridradialbasisfunctionmethodforpredictingthefreshandhardenedpropertiesofselfcompactingconcrete AT zhangabaynurlan novelhybridradialbasisfunctionmethodforpredictingthefreshandhardenedpropertiesofselfcompactingconcrete |