Damage Prediction for RC Slabs under Near-Field Blasts using Artificial Neural Network

This article discusses how spalling and cratering are common modes of failure in concrete structures subjected to blast load detonations. There are limited data and models available on the prediction of the spalling/cratering of reinforced concrete members under blast loads. Engineers currently rely on empirical curves to assess the level of damage represented by spalling and/or breach experienced by a reinforced concrete (RC) slab under near-field detonations. A novel Artificial Neural Network (ANN) model capable of predicting the damage (spalling/cratering) size is developed and evaluated in this paper. The ANN model is trained and tested using data sets produced using the numerical hydrocode LS-DYNA. The numerical model, which was verified using field experiments, is reliable for detecting the highly dynamic response of the RC slab under severe blast loading. The ANN model is capable of predicting the damage size under different loading parameters. The predicted damage size showed good agreement with available extensive design charts used by the engineering community.


  • English

Media Info

  • Media Type: Print
  • Features: Figures; Photos; References; Tables;
  • Pagination: pp 315-332
  • Serial:

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Filing Info

  • Accession Number: 01356842
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 7 2011 11:22AM