Predictive Modeling of Carbon Footprint in Hybrid Structural Components Using AI and Mathematical Algorithms

Abstract

Sustainable engineering requires a precise assessment of the carbon footprint of hybrid structural elements. To evaluate the lifespan emissions of materials such as composites and fiber-reinforced polymers, this study proposes a predictive modeling approach that blends mathematical optimization with Artificial Intelligence (AI) approaches, such as neural networks and regression algorithms. The model provides precise and understandable carbon footprint estimates by examining data on material characteristics, energy use, and processing techniques. The strategy promotes more environmentally friendly material selections and structural layouts, which are consistent with international net-zero goals.