| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Albostami, Asad S.
Liverpool John Moores University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2024Data-driven predictive modeling of steel slag concrete strength for sustainable constructioncitations
- 2024Data-driven predictive modeling of steel slag concrete strength for sustainable constructioncitations
- 2024An optimized prediction of FRP bars in concrete bond strength employing soft computing techniquescitations
- 2024An optimized prediction of FRP bars in concrete bond strength employing soft computing techniquescitations
- 2023Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniquescitations
- 2023Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniquescitations
- 2019Assessment of Cross-Laminated Timber Panels by the State Space Approachcitations
- 2017Structural Behaviour of Cross-Laminated Timber Panels by the State Space Approachcitations
Places of action
| Organizations | Location | People |
|---|
article
An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques
Abstract
The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars’ bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly Gene Expression Programming (GEP) and the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR).<br/><br/>The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete.