AI-powered material optimisation for an environmentally conscious construction industry

The project intends to develop sustainable construction materials from waste like recycled plastics and natural fibers, reducing negative environmental impacts and promoting resource efficiency. The utilization of local resources like earth and natural fibers are aimed to minimize environmental footprint, transportation costs, respect social and cultural settings, and generate local jobs. The research will develop machine learning models for accurate assessment of complex earth-based material performance, bridging the gap between existing testing methods and real-world behavior. The integration of AI-driven design with earth-based matrices, leading to innovative, structurally efficient, and environmentally responsive buildings optimized for natural light and ventilation. By combining AI-powered prediction and design with waste valorization and local sourcing, this project aims to accelerate the transition to sustainable construction materials, unlock the full potential of earth-based matrices, minimize environmental impact and resource extraction within the construction sector. It aims at improving building energy efficiency and occupant health by reducing exposure to harmful chemicals and foster innovation in eco-friendly printing materials and techniques for sustainable construction. This research paves the way for a more environmentally friendly and efficient construction industry, harnessing the potential of earth-based matrices and AI for a greener future.