- author = "Mohsin Ali and Maher Ali Rusho and Li Chen3 and Dany Marcelo Tasan Cruz",
- title = "Advancing Structural Safety: Genetic Programming Approaches to Steel Fiber-Reinforced Concrete ({SFRC)} Blast Response Prediction",
- booktitle = "2025 17th International Conference on Computer and Automation Engineering (ICCAE)",
- year = "2025",
- pages = "183--187",
- month = mar,
- keywords = "genetic algorithms, genetic programming, Training, Accuracy, Loading, Machine learning, Predictive models, Mathematical models, Concrete, Steel, Load modelling, Steel Fiber Reinforced Concrete (SFRC), Blast loading, gene expression programming",
- ISSN = "2154-4360",
-
DOI = "
doi:10.1109/ICCAE64891.2025.10980530",
- abstract = "Steel Fiber-Reinforced Concrete (SFRC) has emerged as a preferred material for blast-resistant structures due to its exceptional mechanical properties and energy absorption capabilities. This study introduces a machine learning-based framework to predict the maximum displacement of SFRC structural members under blast loading. Using 107 experimental data points, split into $\mathbf{7 0 \percent}$ for training and 15 percent each for validation and testing, Gene Expression Programming (GEP) and Multi-Expression Programming (MEP) were applied. The GEP model exhibited superior predictive performance with R -values of 0.964 (training), 0.968 (validation), and 0.960 (testing), while the MEP model achieved reasonable accuracy with $R$-values of 0.922, 0.905, and 0.948, respectively. Additionally, parametric analysis revealed the influence of fiber properties on SFRC behaviour. This approach not only simplifies predictive modelling but also enhances its reliability, offering valuable insights for optimising SFRC design under extreme conditions and contributing to the advancement of resilient structural systems.",
-
notes = "Also known as \cite{10980530}
School of Civil Engineering, Southeast University, Nanjing, China",
