Artificial neural network based delamination prediction in composite plates using vibration signals

Abstract

Dynamic loading on composite components may induce damages such as cracks, delaminations, etc. and development of an early damage detection technique for delaminations is one of the most important aspects in ensuring the integrity and safety of composite components. The presence of damages such as delaminations on the composites reduces its stiffness and further changes the dynamic behaviour of the structures. As the loss in stiffness leads to changes in the natural frequencies, mode shapes, and other aspects of the structure, vibration analysis may be the ideal technique to employ in this case. In this research work, the supervised feed-forward multilayer back-propagation Artificial Neural Network (ANN) is used to determine the position and area of delaminations in GFRP plates using changes in natural frequencies as inputs. The natural frequencies were obtained by finite element analysis and results are validated by experimentation. The findings show that the suggested technique can satisfactorily estimate the location and extent of delaminations in composite plates.

Impact of earthquake’s epicenter distance to failure of the embankment – A seismic prediction

“…Artificial neural networks (ANN) can significantly support geotechnical engineering designs as they can be used for the integration, prediction, and classification of results [49][50][51][52]. ANN is also commonly used for results of investigation forecasting, categorization outcome of a phenomenon study, association of data, and filtering interpreted data for solving several engineering problems [53][54][55][56][57]. An ANN is created from input, hidden, and output layers, with neurons performing at each layer [52].…”

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

“…In recent years, many methods have been developed to identify the location and estimate the severity of the damage to structures. Artificial neural networks, optimisation techniques, and other artificial intelligence tools have been used in damage detection (Tran-Ngoc et al, 2019;Sreekanth et al, 2022aSreekanth et al, , 2022b. Feedforward neural networks were used to detect damages such as cracks and delamination in composite laminates (Senthilkumar et al, 2022;Sreekanth et al, 2021).…”