Recent Advancements in AIoT for Predicting GFRP-Properties

The outstanding mechanical features of glass fiber-reinforced polymer (GFRP) composites, including their high strength-to-weight ratio, corrosion resistance, and durability, have made them widely used in a variety of engineering applications. For these composites to function and be reliable, it is essential to predict their mechanical fracture and thermal properties. Previously, these characterizations were based on experimental, numerical, or analytical procedures. Recent breakthroughs in Artificial Intelligence of Things (AIoT) have helped greatly to improve the accuracy and efficiency of forecasting these qualities. The chapter addresses the application of AIoT in predicting mechanical fracture and thermal properties of GFRPs per current demands. The chapter has nine sections. The first section entails introduction and relevance of recent developments in AIoT on prediction of these qualities of GFRP. The other seven sections address the advances in AIoT applications which will include the machine learning models, finite element analysis (FEA) integration, sensor integration and IoT connectivity, digital twin technology, materials informatics along with multiscale modeling and simulation for greater accuracy and efficiency in forecasting these qualities followed by implications of studying this chapter for academicians and practitioners. Finally, a summary of the contents with emerging trends in research and developments in GFRPs for further reliability and proper applications will be highlighted at the last section for the future generation.