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Damage assessment in structure from changes in static parameter using neural networks

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Abstract

Damage to structures may occur as a result of normal operations, accidents, deterioration or severe natural events such as earthquakes and storms. Most often the extent and location of damage may be determined through visual inspection. However, in some cases this may not be feasible. The basic strategy applied in this study is to train a neural network to recognize the behaviour of the undamaged structure as well as of the structure with various possible damaged states. When this trained network is subjected to the measured response, it should be able to detect any existing damage. This idea is applied on a simple cantilever beam. Strain and displacement are used as possible candidates for damage identification by a back-propagation neural network. The superiority of strain over displacement for identification of damage has been observed in this study

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Authors and Affiliations

  1. Civil Engineering Department, Indian Institute of Technology — Guwahati, 781 039, Guwahati, India

    Damodar Maity & Asish Saha

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  1. Damodar Maity
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  2. Asish Saha
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Correspondence to Damodar Maity.

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Maity, D., Saha, A. Damage assessment in structure from changes in static parameter using neural networks. Sadhana 29, 315–327 (2004). https://doi.org/10.1007/BF02703781

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  • DOI: https://doi.org/10.1007/BF02703781

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