Integrating artificial intelligence in urban design: A neural network-driven approach for noise mitigation optimization

Many studies have documented the relationship between urban morphology and noise exposure, emphasizing the role of urban planning and design in mitigating environmental noise exposure. However, there remains a significant gap in both literature and practice concerning noise mitigation through urban design optimization. Addressing this gap, recent advancements in artificial intelligence (AI)—including machine learning, neural network, and evolutionary computation—have been increasingly incorporated into environmental impact assessments and design optimization. In this study, a novel design optimization workflow combines a neural network-based noise prediction model, an urban form generator, and an evolutionary algorithm-based solver has been proposed. Tested on a Hong Kong new town site, it showed building form and layout changes led to significant noise exposure alterations. These results also demonstrate AI's potential in early-stage design to optimize building massing and layouts to reduce noise impact, offering valuable insights for architects and urban planners.