ARTIFICIAL NEURAL NETWORKS FOR THE PREDICTION OF SHEAR STRENGTH OF REINFORCED CONCRETE SLENDER BEAM | ||
| International Journal of Advanced Engineering and Business Sciences | ||
| Volume 5, Issue 2, September 2024 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijaebs.2024.313808.1098 | ||
| Author | ||
| Aiman Ezzat Mohamed* | ||
| Civil Eng. Dept. Modern Academy for Engineering and Technology | ||
| Abstract | ||
| There are many important differences between flexure and shear behavior that influences the design procedure. From tests on beams in flexure, a close agreement between the experimental results and numerical predictions is observed. On the other hand, for shear, there is a larger gap between predictions and experimental results, which indicates that the problem is still not yet fully understood. Artificial neural networks are algorithms for learning and optimization. They have the ability to learn the relations and generalize solutions from examples without knowledge of rules. Research in artificial neural networks and their application to structural engineering problems is gaining concern and is growing rapidly. The use of artificial neural networks in structural engineering has evolved as a new important computing pattern, although it is still very limited. The objective of this paper is to use the artificial neural networks to predict the shear strength of reinforced concrete slender beams. | ||
| Keywords | ||
| shear strength; artificial neural networks; reinforcement ratio; slender beams; reinforced concrete | ||
|
Statistics Article View: 73 |
||
