Engineered Cementitious Composites (ECC) exhibit exceptional strain-hardening behavior, yet predicting their tensile properties remains challenging due to complex nonlinear interactions among mix constituents and fiber characteristics. This study presents a comprehensive machine learning framework for predicting both t...
Amro A. Seifelnasr, M. A. Alhaj-Hussein, Jamal A. Abdalla et al.· Proceedings of International...· 0 citations
Concrete-filled steel tubes (CFST) have been used in structural engineering due to the advantages of the ductility of steel and strength of concrete. While a large number of research studies are available for estimating the ultimate load capacity of the CFST cross-sections, the inverse problem of designing the optimum...
Hadia E. M. Ahmed, Jamal A. Abdalla, R. Hawileh· Proceedings of International...· 0 citations
Machine learning (ML) methods are frequently applied for reliable prediction of the shear strength contribution of fiber-reinforced polymer (FRP) strengthened reinforced concrete (RC) beams. While offering high accuracy, prior ML models failed to interpret the rationale behind predictions. Model interpretability is c...
Zahira Sadoun, A. Elnemr, Farid Bouziadi et al.· Journal of reinforced plasti...· 0 citations
Polyethylene (PE) fibers enable engineered cementitious composites (ECCs) to achieve tensile strain-hardening and multiple-cracking behavior through fiber bridging; however, the relatively large crack widths that can develop in PE fiber-reinforced high-strength ECC (HS-ECC) may limit its autogenous self-healing capabil...
Zhi-Gang Zhang, Xiang-Wen Lei, Jamal A. Abdalla et al.· Polymers· 0 citations
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