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Vol.21, Special Issue, 2021, pp. S69–S73 |
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CRACK DETECTION IN STRUCTURE BY IMPROVED RECURRENT NEURAL NETWORKS APPROACH S.P. Jena1, S. Sahu2 1) Vardhaman College of Engineering, Hyderabad, INDIA 2) School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar, INDIA email: shaktipjena@gmail.com , sasmita.sahufme@kiit.ac.in
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Abstract The current methodology is focused to investigate a crack assessment method for transit mass dynamics problem in the domain of improved Recurrent Neural Networks (RNNs) methodology. A cracked simply support beam under the action of transit mass is considered as a case study for the present analogy. The knowledge-based Elman’s RNNs (ERNNs) approach has been implemented in this problem to find out the position and severity of crack on the beam in a supervised mode. The Levenberg-Merquardt’s (L-M) back propagation mechanism or algorithm has been applied to train the knowledge based ERNNs structure. To ensure the robustness of the anticipated investigation, a numerical problem is prepared and analysed. The entire crack detection method has been performed in a supervised mode. The results obtained from ERNNs approach are compared with numerical ones and found to be well convergent. Keywords: crack, ERNNs, L-M back propagation algorithm |
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