Structural damage detection in moving load problem using JRNNs based method

Abstract

Damage detection in a structure using the vibration signature is a quiet smart method for condition monitoring of the structure. In this problem, the Recurrent Neural Networks (RNNs) based method has been implemented for damage detection in the moving load problem as an inverse method. A multi-cracked simply supported beam under a traversing load has been considered for the present problem. The localization and severities of the supervised cracks on the structure are determined using the adapted Jordan's Recurrent Neural Networks (JRNNs) approach. The mechanism of Levenberg-Marquardt's back propagation algorithm has been implemented to train the networks. To check the adoptability of the proposed JRNNs method, numerical analyses along with laboratory test verifications have been conducted and found to be well emerged.

On the way to fault detection method in moving load dynamics problem by modified recurrent neural networks approach

“…The results obtained from the modified RNNs analogy vary with approximately 3.5% with the numerical values for relative crack locations while the results for crack severities vary about 3.9% approximately with the numerical values which are considered as good convergent. In the earlier works, the authors have also applied the analogy of the modified ERNNs [12] and JRNNs [13] based approaches individually with L-M back propagation algorithm.…”

“…The L-M (Levenberg-Marquardt) back propagation algorithm by Yu and Wilamski [21] is implemented to the proposed network to estimate the crack locations and depth of the structures. The training of the network model has been conducted with the same procedures of authors [12,13] earlier works and Yu and Wilamski [21]. Due to the fast and steady convergence properties, the L.M back propagation algorithm is applied for the proposed network model.…”

“…'j' is the combination coefficient. The value of 'j' approximate to zero, then equation (13) will follow the mechanism of Gauss-Newton and, if that of 'j' is quiet greater value, then that will follow the mechanism of steepest descent methods. j = (1/n), 'n' is the step size or training constant.…”

“…The numerical, FEA and experimental works which are already validated with each other earlier by the authors [8][9][10] are considered as forward problems, while the modified RNNs analogy is considered as inverse method in the present formulation. The present method has been also compared with author's previous works [12,13].…”

“…They have determined the structure's response at various damage and moving load parameters. In the later part, Jena and Parhi [12,13] have also developed damage detection procedures for structure under traverse load as inverse methods using the concepts of different types of rule-based recurrent neural networks. (ERNNs and JRNNs).…”