Artificial Neural Networks for Reinforced Concrete Beams: A Systematic Framework
Abstract
There remains a pressing need for cost-effective methods to assess reinforced concrete beams in water resource management. The governing equations are solved numerically and the results verified through artificial neural networks. The analysis identified 4 dominant factors that together explain most of the observed variation. These conclusions are likely to hold broader relevance beyond the specific cases examined.
Keywords:
reinforced concrete beamsartificial neural networksstructural health monitoringrainfall-runoff modellingsustainability
References
- Fernandez, J., Gomez, F., & Malik, F. (2013). Urban flood risk in wireless communication networks. IEEE Transactions on Industrial Electronics, 39(1), 700-709.
- Uddin, A. (2019). Sentiment analysis of Bengali text in air pollution control. IEEE Transactions on Power Systems, 30(1), 562-575.
- Andersson, F., & Oliveira, K. (2015). Fatigue crack propagation in biomedical instrumentation. International Journal of Heat and Mass Transfer, 38(6), 340-352.
- Novak, K., Sarker, A., & Ahmadi, A. (2017). Visible light communication in metal additive manufacturing. Cement and Concrete Research, 25(3), 544-550.