Deni Velagić

My research is focused on the use of FRPs with the NSM method in the strengthening of concrete beams in shear. Due to the complex nature of shear failures, predicting shear strength contributions from NSM FRP has proven difficult. Using machine learning, experimental work and numerical methods, my aim is to improve prediction capabilities and contribute to the creation of design guidelines.

My research is focused on the use of Fiber Reinforced Polymers (FRPs) with the Near Surface Mounted (NSM) method in the strengthening of concrete beams in shear. FRPs are high strength composite materials made of various fibers, such as glass and carbon, and the NSM method is a strengthening technique where grooves are cut into concrete elements and then filled with epoxy that has an FRP rod embedded in it. Over the years there have been many prediction equations for shear strength provided by the NSM FRP, but they are generally only accurate within certain ranges. In my work, I aim to leverage a comprehensive database of NSM FRP shear strengthened beams with novel neural network techniques to help identify more critical parameters and their influence on NSM FRP shear strengthening. This information will then be used to prepare further experimental tests and then numerical models which can be used for parametric studies. Finally, the end goal is to produce design guidelines which can be used by design engineers.