AI in Sustainable Energy and Environment

en Using Machine Learning Models for Optimization and Prediction of Mechanical propeties in Lowland- Bamboo, Glass Fiber Hybrid Composites with Al2O3 Nanoparticle Reinforcement عمومى General پژوهشي Research Abstract: This study investigates the flexural properties and hardness of hybrid composites reinforced with lowland bamboo, and glass fibers, incorporating Al 2O3 nanoparticles as fillers. Key factors examined include fiber orientation (0°, 45°, and 90°), fiber placement (1, 2, and 3 layers), and Al2O3 content (3%, 4%, and 5%). Response Surface Methodology was employed to analyze interactions between these parameters, while Artificial Neural Networks (ANN) and Generative Adversarial Networks (GANs) were utilized for predictive modeling. Experimental results highlighted that the optimal combination for maximizing flexural strength and hardness involved 90° fiber orientation, a fiber sequence and 5% Al2O3 content. The study emphasizes the importance of fiber alignment, strategic layer stacking, and nanoparticle reinforcement in enhancing composite properties. The findings not only confirm the mechanical superiority of the optimized hybrid composite but also validate the use of advanced predictive models like GANs and FFNN for material property estimation. This work contributes to developing high-performance hybrid composites with tailored mechanical properties for advanced engineering applications.   Keywords: Natural Resource, Artificial Neural Network, Aluminium Oxide, Sustainable 0 0 http://aisesjournal.com/browse.php?a_code=A-10-373-1&slc_lang=en&sid=1 Firankor Teshome Daba Firankorta@gmail.com 1003194753284600251 1003194753284600251 Yes Addis Ababa Science and Technology University Weynishet D. Kelbessa weynishet.dhaabaa1@gmail.com 1003194753284600252 1003194753284600252 No Assosa university Serawit Demeke serawitdas@gmail.com 1003194753284600253 1003194753284600253 No Assosa University