Fault Assessment of FRC Cracked Beam by using Neuro-Fuzzy Hybrid Technique

Abstract

The present inquire has carried out to identify transverse crack in fibre- reinforced composite (FRC) beam. The vibrational behavior of FRC beam has studied by using experimental investigations in laboratory. Further an artificial intelligence (AI) technique is used as a annul technique to diagnosis the fault in FRC beam. Here, neuro-fuzzy hybrid method is applied as reversed AI technique. The results (first three relative natural frequencies) obtained from experimental investigation and orientations of woven fibre are used as inputs in neural network technique (NNT) to get crack location and crack depth as output. Combining the output and the expected results obtained, by taking inputs in fuzzy logic system from both of NNT and experimental technique, because the aim is to reach more accurate results. Further the output obtained from NNT and the results obtained from experimental became together and taken as inputs in fuzzy logic system (FLS) for aim to reach a more accuracy results. The conclusion from the current investigation is that the fibre orientation has great efficacy on stiffness of FRC beam and neuro-fuzzy hybrid technique which produce more accurate results. Since this reverse technique is a non-destructive approach which can be used as progress form to detect the fault in FRC beam.