People

Dr. Sameer Al-Dahidi

Professor

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Education

  • Ph.D. in Energy and Nuclear Science and Technology, Politecnico di Milano, Italy, 2016. With Honors. Thesis: Development of Data-Driven Methods for Prognostics and Health Management under Variable Operational Conditions in Industrial Equipment.
  • M.Sc. in Nuclear Energy – Operations, École Centrale Paris and Université Paris-Sud XI, France, 2012. Very Good – Ranked 1st.
  • B.Sc. in Electrical and Computer Engineering, The Hashemite University, Jordan, 2008. With Honors – Ranked 1st.

Teaching

  • ISE 291: Introduction to Data Science

Research Interests

  • Data-driven modeling and optimization for diverse industrial challenges
  • Reliability, Availability, Maintainability, and Safety (RAMS), Prognostics and Health Management (PHM), and maintenance decision support
  • Energy efficiency, sustainability, and techno-economic analysis

Selected Publications

  • S. Al-Dahidi and M. S. Sari “Critical Buckling Load Prediction in Tapered Axially Functionally Graded Nanobeams using Machine Learning under Different Boundary Conditions,” Advances in Mechanical Engineering, vol. 18, no. 1. 2026. https://doi.org/10.1177/16878132251411
  • S. Al-Dahidi, H. Alahmer, B. Rinchi, A. Bani-Abdullah, M. Alrbai, O. Ayadi, L. Al-Ghussain, “Multistep PV power forecasting using deep learning models and the reptile search algorithm,” Results in Engineering, vol. 27, Article ID 106265. 2025. https://doi.org/10.1016/j.rineng.2025.106265.
  • S. Al-Dahidi, B. Rinchi, R. Dababseh, O. Ayadi, and M. Alrbai “A Geographic Multi-Scale Machine Learning Framework for Predicting Solar Irradiation on Tilted Surfaces,” Energy, vol. 313, 133767, 2024. https://doi.org/10.1016/j.energy.2024.133767
  • S. Al-Dahidi, P. Baraldi, M. Fresc, E. Zio, and L. Montelatici “Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant,” Energies, vol. 17, no. 10, 2424, 2024. https://doi.org/10.3390/en17102424
  • S. Al-Dahidi, M. Alrbai, L. Al-Ghussain, A. Alahmer, and HS. Hayajneh, “Data-Driven Analysis and Prediction of Wastewater Treatment Plant Performance: Insights and Forecasting for Sustainable Operations,” Bioresource Technology, vol. 391, Part A, 129937, 2023. https://doi.org/10.1016/j.biortech.2023.129937
  • M. Xu, P. Baraldi, S. Al-Dahidi, and E. Zio, “Fault Prognostics by an Ensemble of Echo State Networks in Presence of Event Based Measurements,” Engineering Applications of Artificial Intelligence, vol. 87, Article ID103346, 2020. https://doi.org/10.1016/j.engappai.2019.103346
  • Z. Yang, S. Al-Dahidi, P. Baraldi, E. Zio, and L. Montelatici, “A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments,” IEEE Trans. Neural Networks Learn. Syst., vol. 31, no. 1, pp. 309-320, 2020. 10.1109/TNNLS.2019.2900956
  • S. Al-Dahidi, F. Di Maio, P. Baraldi, E. Zio, and R. Seraoui, “A Framework for Reconciliating Data Clusters from a Fleet of Nuclear Power Plants Turbines for Fault Diagnosis,” Applied Soft Computing, vol. 69, pp. 213-231, 2018. https://doi.org/10.1016/j.asoc.2018.04.044
  • P. Baraldi, F. Di Maio, S. Al-Dahidi, E. Zio, and F. Mangili, “Prediction of Industrial Equipment Remaining Useful Life by Fuzzy Similarity and Belief Function Theory,” Expert Systems with Applications, vol. 83, pp. 226–241, 2017. https://doi.org/10.1016/j.eswa.2017.04.035
  • S. Al-Dahidi, F. Di Maio, P. Baraldi, and E. Zio, “Remaining Useful Life Estimation in Heterogeneous Fleets Working under Variable Operating Conditions,” Reliability Engineering & System Safety, vol. 156, pp. 109-124, 2016. https://doi.org/10.1016/j.ress.2016.07.019
  • S. Al-Dahidi, F. Di Maio, P. Baraldi, E. Zio, and R. Seraoui, “A Novel Ensemble Clustering for Operational Transients Classification with Application to a Nuclear Power Plant Turbine,” International Journal of Prognostics and Health Management, vol. 6, no. SP3, pp. 1–21, 2015. https://doi.org/10.36001/ijphm.2015.v6i3.2267
Dr. Sameer Al-Dahidi

Professor