Improved ANN technique combined with Jaya algorithm for crack identification in plates using XIGA and experimental analysis

This paper presents an effective method for crack identification to improve the training of Artificial Neural Networks (ANN) parameters using Jaya algorithm. Dynamic and static datasets are introduced using eXtended IsoGeometric Analysis (XIGA) to improve the accuracy of the proposed application based on the frequency and strain measurements. Based on the concept used in our previous works, XIGA provided more accurate results for fracture mechanics applications than other modelling techniques. Therefore, XIGA datasets of cracked plate are used to improve ANN technique for static and dynamic analyses. Model updating of the cracked plate is considered by introducing the mass of accelerometers and identifying Young's modulus of the plate and stiffness of springs using Jaya algorithm. The difference between measured and calculated frequencies is used as an objective function to calibrate the XIGA model. The crack length is predicted using an adaptive approach without any previous knowledge based on the data provided from a numerical model. Jaya algorithm is used to optimize the most important parameters of ANN. Several numerical examples with different crack scenarios and different boundary conditions are studied in order to evaluate the proposed approach. The results show that the proposed application is able to predict all considered scenarios and accurately identify the crack length. Experimental data of cracked plates are used to validate the numerical predictions. Hence, this application is found to be robust and accurate for crack identification in plates.