Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function Methods
Most of the published literature on concrete containing fly ash was limited to predicting the hardened concrete properties. It is understood that exist so restricted studies focusing on forecasting both fresh and hardened properties of self-compacting concrete (SCC). Hence, it is attempted to develo...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Bilijipub publisher
2022-10-01
|
| Series: | Advances in Engineering and Intelligence Systems |
| Subjects: | |
| Online Access: | https://aeis.bilijipub.com/article_158268_62038c98bf3557319fdca11e9082c137.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856450012708864 |
|---|---|
| author | David Cadasse Antonio Fontana |
| author_facet | David Cadasse Antonio Fontana |
| author_sort | David Cadasse |
| collection | DOAJ |
| description | Most of the published literature on concrete containing fly ash was limited to predicting the hardened concrete properties. It is understood that exist so restricted studies focusing on forecasting both fresh and hardened properties of self-compacting concrete (SCC). Hence, it is attempted to develop some models to predict the fresh and hardened properties of SCC by the optimized radial basis function neural network (RBFNN) method. This study aims to specify RBFNN method key parameters using arithmetic optimization algorithm (AOA) and grasshopper optimization algorithm (GOA). The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and in the hardened phase compressive strength. The results present powerful workability during the prediction process. It is observed that the developed models have performance evaluation indices in reasonable value in the learning and testing section. All in all, the RBFNN model developed by AOA outperforms others, with R2 values at 0.9607 (slump flow), 0.9651 (L-box), 0.9905 (V-funnel test), and 0.9934 (compressive strength), which depicts the capability of this algorithm for determining the optimal parameters of the RBFNN, While, it is worth mentioning than the model developed with GOA algorithm is also powerful. |
| format | Article |
| id | doaj-art-b9c375872a40413c91a0d18195924b30 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-b9c375872a40413c91a0d18195924b302025-02-12T08:46:30ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632022-10-0100103122610.22034/aeis.2022.351734.1029158268Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function MethodsDavid Cadasse0Antonio Fontana1The King's School, Bujumbura, BurundiDepartment of Engineering Enzo Ferrari, University of Modena and Reggio Emilia, Modena, 41121, ItalyMost of the published literature on concrete containing fly ash was limited to predicting the hardened concrete properties. It is understood that exist so restricted studies focusing on forecasting both fresh and hardened properties of self-compacting concrete (SCC). Hence, it is attempted to develop some models to predict the fresh and hardened properties of SCC by the optimized radial basis function neural network (RBFNN) method. This study aims to specify RBFNN method key parameters using arithmetic optimization algorithm (AOA) and grasshopper optimization algorithm (GOA). The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and in the hardened phase compressive strength. The results present powerful workability during the prediction process. It is observed that the developed models have performance evaluation indices in reasonable value in the learning and testing section. All in all, the RBFNN model developed by AOA outperforms others, with R2 values at 0.9607 (slump flow), 0.9651 (L-box), 0.9905 (V-funnel test), and 0.9934 (compressive strength), which depicts the capability of this algorithm for determining the optimal parameters of the RBFNN, While, it is worth mentioning than the model developed with GOA algorithm is also powerful.https://aeis.bilijipub.com/article_158268_62038c98bf3557319fdca11e9082c137.pdfself-compacting concretefly ashrheological propertiescompressive strengthradial basis function neural networkoptimization algorithms |
| spellingShingle | David Cadasse Antonio Fontana Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function Methods Advances in Engineering and Intelligence Systems self-compacting concrete fly ash rheological properties compressive strength radial basis function neural network optimization algorithms |
| title | Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function Methods |
| title_full | Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function Methods |
| title_fullStr | Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function Methods |
| title_full_unstemmed | Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function Methods |
| title_short | Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function Methods |
| title_sort | estimation of fresh and hardened properties of self compacting concrete by optimized radial basis function methods |
| topic | self-compacting concrete fly ash rheological properties compressive strength radial basis function neural network optimization algorithms |
| url | https://aeis.bilijipub.com/article_158268_62038c98bf3557319fdca11e9082c137.pdf |
| work_keys_str_mv | AT davidcadasse estimationoffreshandhardenedpropertiesofselfcompactingconcretebyoptimizedradialbasisfunctionmethods AT antoniofontana estimationoffreshandhardenedpropertiesofselfcompactingconcretebyoptimizedradialbasisfunctionmethods |