Jeffrey Hung’s research is focused on development of an artificial neural network (ANN) for analyzing the shear strength of PGM walls. He started his MSc in September 2016. The behaviour of partially grouted masonry (PGM) shear walls is complex, due to the inherent anisotropic properties of masonry materials and nonlinear interactions between the mortar, grouted cells, ungrouted cells, and reinforcing steel. Since PGM shear walls are often part of lateral force resisting systems in masonry structures, it is crucial that its shear behaviour is well understood, and its shear strength is accurately predicted.
ANNs have the unique ability to address highly complex problems and the potential to predict accurate results without a defined algorithmic solution. By providing an ANN with a dataset of multiple inputs and a corresponding output, it can be trained to determine the weighted effect of each input parameter and describe nonlinear relationships that may exist among the variables. A combined database of experimental results and finite element models of PGM shear walls is used as input for the ANN analysis model.