Browsing by Author "Alam, M. Shahria"
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Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
Wakjira, Tadesse G.; Ibrahim, Mohamed; Ebead, Usama; Alam, M. Shahria ( Elsevier , 2022 , Article)This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of ... -
Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites
Wakjira, Tadesse Gemeda; Ebead, Usama; Alam, M. Shahria ( Elsevier , 2022 , Article)The application of inorganic composites has proven to be an effective strengthening technique for shear-critical reinforced concrete (RC) beams. However, accurate prediction of the shear capacity of RC beams strengthened ... -
Plastic hinge length of rectangular RC columns using ensemble machine learning model
Wakjira, Tadesse G.; Alam, M. Shahria; Ebead, Usama ( Elsevier , 2021 , Article)It is critical to properly define the plastic hinge region (the region that is exposed to maximum plastic deformation) of reinforced concrete (RC) columns to assess their performances in terms of ductility and energy ...