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08: Integration of Computational and parametric Design towards Sustainable Construction
The construction industry faces mounting pressure to adopt sustainable practices in response to escalating energy consumption and environmental concerns. Buildings are responsible for a significant share of global energy use, necessitating innovative approaches to improve efficiency. This study introduces an AI-driven parametric design framework aimed at optimizing architectural parameters for energy-efficient and sustainable construction. Leveraging environmental data from 150 diverse locations across India, the research develops predictive models to determine optimal building orientation, window-to-wall ratio (WWR) and material selection tailored to regional climates. Advanced machine learning algorithms are integrated with building information modeling (BIM) to provide a data-driven approach, achieving energy savings of up to 40%. This framework enables informed decision-making by correlating environmental conditions with design strategies. By addressing regional variations and integrating sustainability metrics, the study highlights the transformative potential of AI in redefining sustainable construction and fostering energy-efficient architectural practices globally.
Keywords: Sustainability, Machine Learning, Building Orientation."
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