Sanjida Ferdousi
Papers
1
Total Citations
28
H-Index
1
About
Sanjida Ferdousi is a researcher advancing the understanding of interfacial mechanics in composite materials through the innovative application of data-driven machine learning. Her key research areas include computational mechanics, interface characterization, and the integration of artificial intelligence with materials science. Ferdousi’s most notable contribution is her work on characterizing traction–separation (T–S) relations and interfacial imperfections using machine learning models, as detailed in her highly cited 2021 paper (28 citations). This study provides a powerful framework for evaluating structural reliability in critical applications such as vehicle structures, soft robotics, and aerospace, where composite interfaces are paramount. By replacing traditional, often labor-intensive experimental methods with efficient, predictive algorithms, Ferdousi’s research enables faster and more accurate assessments of material performance and failure. Her work bridges the gap between classical fracture mechanics and modern computational intelligence, offering practical tools for engineers designing safer, more resilient composite systems. With her growing citation impact, Ferdousi is establishing herself as a key contributor to the future of smart materials characterization and structural health monitoring.
Research Focus
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Top Papers
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