Prediction of blast-induced ground vibration using artificial neural network

Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach

Evaluation of effect of blasting pattern parameters on back break using neural networks

Multiple cracks effects and identification

An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm

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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Asish Saha

Papers

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