Assessment of Sustainable High-Rise Architecture through AI and Green Standards
Keywords:
Sustainable architecture, Artificial intelligence, Green building standards, High-rise buildings, LEED certificationAbstract
This research investigates the integration of
artificial intelligence (AI) technologies with
green building standards in the assessment
and optimization of sustainable high-rise
architecture. The study employs a
quantitative methodology to analyze the
effectiveness of AI-driven tools in enhancing
building performance metrics across LEED
and BREEAM certified high-rise structures.
Through systematic data collection from 150
high-rise buildings globally, this research
evaluates energy consumption patterns,
carbon footprint reduction, and
sustainability compliance using machine
learning algorithms. The findings
demonstrate that AI-integrated assessment
systems achieve 35% higher accuracy in
predicting building performance compared
to traditional evaluation methods. Buildings
incorporating AI-driven optimization show
30-40% energy reduction and 25% lower
water consumption. The study reveals
significant correlations between AI
implementation and improved green
certification scores, with 78% of AIenhanced
buildings achieving LEED Gold or
Platinum ratings. These results indicate that
AI technologies substantially enhance the
effectiveness of green building standards in
promoting sustainable high-rise
architecture. The research concludes that
integrated AI-green standards approach
represents a paradigm shift toward more
efficient, data-driven sustainable building
practices, offering substantial environmental
and economic benefits for future urban
development.
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