AI-Driven multi-objective optimization of pavement and roofing strategies for enhanced thermal comfort in urban Indian apartments

A significant portion of the Indian population resides in urban areas, primarily driven by employment opportunities. The high population density in these cities has led to an increasing preference for apartments as primary residences. In India’s tropical climates, where many residents cannot afford air conditioning to regulate indoor temperatures, it is crucial to design buildings, rooftops, and pavements that maximize thermal comfort. Given that roofs and pavements cover substantial urban surfaces, their surface reflectivity and material properties significantly influence surrounding temperatures, impacting both outdoor and indoor environments.

This study investigates optimizing paving and rooftop properties to enhance thermal comfort in multi-story apartment buildings. A multi-objective optimization technique, using AI-based algorithms, balances multiple conflicting objectives such as thermal comfort, cost and environmental sustainability. The research focuses on reducing indoor temperatures and improving the Tropical Summer Index (TSI) while ensuring economic feasibility and the long-term performance of materials.

The study evaluates pavement materials commonly used in Indian cities, including grass, sandstone, red bricks, granite, and limestone, as well as adaptive roofing strategies such as solar panels, rooftop vegetation, and pergolas. Simulations in DesignBuilder, validated with measured data, are conducted to assess the thermal comfort performance across building floors. Results demonstrate that higher-albedo pavements offer enhanced thermal comfort for lower floors, while optimized passive rooftop strategies improve the comfort of upper floors. This AI-driven multi-objective optimization offers a comprehensive approach to balancing thermal comfort, affordability, and sustainability in apartment building designs for India’s urban areas.