AI-Driven Predictive Composite Infrastructure for Energy-Efficient Smart Buildings

Abstract

Highly sustainable, lightweight, and energy-efficient building infrastructures that can dynamically interact with smart city grids are required due to the acceleration of urban intelligence. High strength-to-weight ratios and thermal resistance are features of advanced composite materials, but clever computational frameworks are needed to optimize their structural and energy performance in real time. The integration of modern composite materials with artificial intelligence in smart building ecosystems is reviewed systematically in this research. We conduct a comparative analysis of data-driven prediction models, such as Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN), used for thermal optimization, energy consumption forecasting, and structural health monitoring. Through the perspective of cyber-physical urban systems, the relationship between dynamic thermal loading and composite material qualities is assessed. The results show that predictive analytics based on machine learning greatly improves the management and design of smart building fabrics. In the end, combining cutting-edge materials with cognitive AI infrastructure offers a scalable route to sustainable, self-optimizing, and resilient smart cities.