A new methodology to predict the sequence of GFRP layers using machine learning and JAYA algorithm.

In this paper, the best stacking sequence using experimental tests of GFRP composites is investigated. The main objective of this work is to determine the main specification of GFRP composite material, which is represented by its physics-mechanical properties, weight, and cost, before performing a series of experimental tests based on various stacking sequences. Our methodology is divided into three stages. The first stage is characterized by extracting the bending data from mechanical tests of some GFRP composites. In the second stage, the validated numerical model is used to simulate numerous cases of stacking sequences. In the last stage, the extracted data is used to determine the parameters for different stacking sequences using an inverse technique based on ANN and JAYA algorithm. The results provide a good prediction of parameters as well as a good orientation to make decisions on the best GFRP stacking sequence to be used, according to the required specifications of the manufacturer. The experemental data analysis can be found at https://github.com/Samir-Khatir/Sequence-GFRP • A new methodology to predict the sequence of GFRP layers. • Inverse problems and evolutionary machine learning. • Improved numerical and experimental analysis. [ABSTRACT FROM AUTHOR]