A neural network based modelling and sensitivity analysis of damage ratio coefficient

  • Authors:

  • Marijana Hadzima-Nyarko;Emmanuel Karlo Nyarko;Dragan Morić

  • Affiliations:

  • Faculty of Civil Engineering, University J.J. Strossmayer in Osijek, Drinska 16a, 31000 Osijek, Croatia;Faculty of Electrical Engineering, University J.J. Strossmayer in Osijek, K. Trpimira 2B, 31000 Osijek, Croatia;Faculty of Civil Engineering, University J.J. Strossmayer in Osijek, Drinska 16a, 31000 Osijek, Croatia

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

The level of structural damage after an earthquake can often be expressed using the damage ratio (DR) coefficient. This coefficient can be calculated using different formulas. A previously valorised new original formula for damage ratio derived for regular structures is implemented. This formula uses the structure response parameters of a single degree of freedom (SDOF) model. The structure response parameters of the SDOF model are obtained by analyzing a large number of non-linear numeric structure responses using earthquakes of different intensities as load input. In this paper, a multilayer perceptron (MLP) neural network is used to model the relationship between the structure parameters (natural period, elastic base shear capacity, post-elastic stiffness and damping) of an SDOF model and the damage ratio (DR) coefficient. The influence of the individual structure parameters on the damage level of a structure is then determined by performing a sensitivity analysis procedure on the trained MLP neural network.