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Artificial Neural Network Model for Analysis of In-Plane Shear Strength of Partially Grouted Masonry Shear Walls

  • Author / Creator
    Hung, Jeffrey R
  • The behaviour of partially grouted (PG) masonry shear walls is complex, due to the inherent anisotropic properties of masonry materials and nonlinear interactions between the mortar, blocks, grouted cells, ungrouted cells, and reinforcing steel. Since PG 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. This study presents the development of an artificial neural network (ANN) model for analyzing the shear strength of PG walls. 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 corresponding outputs, it can be trained to describe nonlinear relationships that may exist among the variables and provide insight into the influence of each input parameter. An experimental dataset of PG shear walls is used as input for the ANN analysis model. It is necessary to assemble the dataset from multiple experimental studies using meta-analysis, given that no single experimental study contains enough information to build and validate a constitutive model for the shear strength and behaviour of PG walls. Finite element (FE) modelling is shown to be a viable option for addressing gaps in input values which exist in the dataset. The effect of previously unaccounted parameters in code-based approaches is discussed, as well as the influence of different types of ANN analysis options and input size on the model predictions. The ANN model results are compared against currently available design codes and equations to predict the in-plane shear strength of PG shear walls.

  • Subjects / Keywords
  • Graduation date
    Spring 2018
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3QN5ZS6M
  • License
    Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.