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Probability-Based Damage Detection of Structures Using Surrogate Model and Enhanced Ideal Gas Molecular Movement Algorithm

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Advances in Structural and Multidisciplinary Optimization (WCSMO 2017)

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Abstract

Generally, updating a finite element model can be considered as an optimization problem where its physical parameters may be adjusted such that analytically computed features, using the updated FE model, are consistent with those obtained from experimentally. The objective function can be defined as a sum of squared difference between analytically computed and experimentally measured data. To meet this goal in this paper therefore, for efficiently reducing the computational cost of the model during the optimization process of damage detection, the structural response is evaluated using properly a trained surrogate model. Surrogate models have received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output results from such model. Here, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states.

The Cascade Feed Forward Neural Network (CFNN) is chosen as a metamodeling technique and enhanced version of the Ideal Gas Molecular Movement (EIGMM) algorithm is used as the main procedure for updating the model. The developed approach is applied to detect simulated damage in numerical models of a 72-bar space truss and a 120-bar dome truss structures

The simulation results show that the proposed method can implement well in probability-based damage detection of structures with less computational efforts compared to direct finite element model.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran

    Mohammad Reza Ghasemi, Ramin Ghiasi & Hesam Varaee

Authors
  1. Mohammad Reza Ghasemi
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  2. Ramin Ghiasi
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  3. Hesam Varaee
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Corresponding author

Correspondence to Mohammad Reza Ghasemi .

Editor information

Editors and Affiliations

  1. Chair for Optimization of Mechanical Stuctures, Faculty for Mechanical Engineering and Safety Engineering, University of Wuppertal, Wuppertal, Germany

    Axel Schumacher

  2. Institute for Construction Enginnering, Technische Universität Braunschweig, Braunschweig, Germany

    Thomas Vietor

  3. Braunschweig, Germany

    Sierk Fiebig

  4. Chair of Structural Analysis, Technische University of Munich, Munich, Germany

    Kai-Uwe Bletzinger

  5. Engineering Center, ECAE 197, University of Colorado Boulder, Boulder, Colorado, USA

    Kurt Maute

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Ghasemi, M.R., Ghiasi, R., Varaee, H. (2018). Probability-Based Damage Detection of Structures Using Surrogate Model and Enhanced Ideal Gas Molecular Movement Algorithm. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, KU., Maute, K. (eds) Advances in Structural and Multidisciplinary Optimization. WCSMO 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-67988-4_124

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  • DOI: https://doi.org/10.1007/978-3-319-67988-4_124

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67987-7

  • Online ISBN: 978-3-319-67988-4

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