Diego Ferreño
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Full Professor of Materials Science and Metallurgical Engineering
Diego Ferreño Blanco holds a degree in Civil Engineering (Ingeniero de Caminos, Canales y Puertos) from the University of Cantabria (2000), a Master’s in Numerical Methods Applied to Engineering from the Polytechnic University of Catalonia (2004), and a PhD in Civil Engineering from the University of Cantabria (2008), with a dissertation on the Structural Integrity of Nuclear Vessels Based on the Master Curve Obtained Using Reconstructed Specimens. He also holds a degree in Physics from the University of Cantabria (2011) and a Master’s in Data Science from the same institution (2018).
Since 2023, he has been a Full Professor in the area of Materials Science and Metallurgical Engineering at the University of Cantabria. He served as Deputy Director of International Relations and Exchange Program Coordinator at the School of Civil Engineering between 2011 and 2014.
His research career has focused on the evaluation of the structural integrity of components and the modeling of neutron embrittlement in nuclear reactor vessels. He has developed advanced numerical simulation techniques using the Finite Element Method to study mechanical, thermomechanical, and fracture processes. In 2009, he received the Research Award from the Social Council of the University of Cantabria in the field of Engineering.
He has published over 60 scientific articles in journals indexed in the Journal Citation Reports (JCR) and four books, in addition to participating in more than 40 national and international scientific conferences. His research spans from modeling the behavior of biological materials to the development of Machine Learning and Deep Learning models applied to structural analysis in manufacturing processes and the prediction of neutron embrittlement in nuclear reactor vessels.
Research Lines
- Evaluation of the structural integrity of materials using advanced analysis techniques.
- Application of Machine Learning in the study of material behavior.
- Characterization and optimization of materials for use in critical infrastructure.
- Numerical modeling of fracture processes and crack propagation in structural materials.
Featured Publications
- ‘Machine learning assessment of the importance of unirradiated yield strength as a variable in embrittlement trend forecasting.’ Ferreño, D., Erickson, M., Kirk, M., Sainz-Aja, J.A.. International Journal of Pressure Vessels and Piping, 2025, 214, 105444.
- ‘Classification of Cast Iron Alloys through Convolutional Neural Networks Applied on Optical Microscopy Images.’ Bárcena, M., Lloret Iglesias, L., Ferreño, D., Carrascal, I.. Steel Research International, 2024, 95(12), 2400120.
- ‘Parametric analysis of railway infrastructure for improved performance and lower life-cycle costs using machine learning techniques.’ Sainz-Aja, J.A., Ferreño, D., Pombo, J., Diego, S., Castro, J.. Advances in Engineering Software, 2023, 175, 103357.
- ‘Shannon entropy as a reliable score to diagnose human fibroelastic degenerative mitral chords: A micro-ct ex-vivo study.’ Ferreño, D., Revuelta, J.M., Sainz-Aja, J.A., Silva, J., Gutiérrez-Solana, F.. Medical Engineering and Physics, 2022, 110, 103919.
- ‘Application of machine learning algorithms for the optimization of the fabrication process of steel springs to improve their fatigue performance.’ Ruiz, E., Ferreño, D., Cuartas, M., Rivas, I., Gutiérrez-Solana, F.. International Journal of Fatigue, 2022, 159, 106785.
Active Projects
