Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/10009640,
  title    = {Introduce Applicability of Multi-Layer Perceptron to Predict the Behaviour of Semi-Interlocking Masonry Panel},
  author    = {O. Zarrin and  M. Ramezanshirazi },
  country   = {Australia},
  institution={University of Newcastle },
  abstract  = {The Semi Interlocking Masonry (SIM) system has been developed in Masonry Research Group at the University of Newcastle, Australia. The main purpose of this system is to enhance the seismic resistance of framed structures with masonry panels. In this system, SIM panels dissipate energy through the sliding friction between rows of SIM units during earthquake excitation. This paper aimed to find the applicability of artificial neural network (ANN) to predict the displacement behaviour of the SIM panel under out-of-plane loading. The general concept of ANN needs to be trained by related force-displacement data of SIM panel. The overall data to train and test the network are 70 increments of force-displacement from three tests, which comprise of none input nodes. The input data contain height and length of panels, height, length and width of the brick and friction and geometry angle of brick along the compressive strength of the brick with the lateral load applied to the panel. The aim of designed network is prediction displacement of the SIM panel by Multi-Layer Perceptron (MLP). The mean square error (MSE) of network was 0.00042 and the coefficient of determination (R2) values showed the 0.91. The result revealed that the ANN has significant agreement to predict the SIM panel behaviour.},
    journal   = {International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering},  volume    = {12},
  number    = {10},
  year      = {2018},
  pages     = {944 - 949},
  ee        = {http://waset.org/publications/10009640},
  url       = {http://waset.org/Publications?p=142},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 142, 2018},
}