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The last 5 uploaded publications
Single and ensemble explainable machine learning-based prediction of membrane flux in the reverse osmosis process
Mohammed Talhami, Tadesse G. Wakjira, Tamara Alomar, Sohila Fouladi, Fatima Fezouni, Usama Ebead, Ali Altaee, Maryam Al‐Ejji, Probir Das, Alaa H. Hawari (2023). Single and ensemble explainable machine learning-based prediction of membrane flux in the reverse osmosis process. , 57, DOI: https://doi.org/10.1016/j.jwpe.2023.104633.
Article249 days agoSensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars
Abathar Al-Hamrani, Tadesse G. Wakjira, Wael Alnahhal, Usama Ebead (2022). Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars. , 305, DOI: https://doi.org/10.1016/j.compstruct.2022.116473.
Article249 days agoExplainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
Tadesse G. Wakjira, Mohamed Ibrahim, Usama Ebead, M. Shahria Alam (2022). Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM. , 255, DOI: https://doi.org/10.1016/j.engstruct.2022.113903.
Article249 days agoFAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model
Tadesse G. Wakjira, Abdelrahman Abushanab, Usama Ebead, Wael Alnahhal (2022). FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model. , 33, DOI: https://doi.org/10.1016/j.mtcomm.2022.104461.
Article249 days agoShear capacity prediction of FRP-RC beams using single and ensenble ExPlainable Machine learning models
Tadesse G. Wakjira, Abathar Al-Hamrani, Usama Ebead, Wael Alnahhal (2022). Shear capacity prediction of FRP-RC beams using single and ensenble ExPlainable Machine learning models. , 287, DOI: https://doi.org/10.1016/j.compstruct.2022.115381.
Article249 days ago