A STRATEGIC ROADMAP FOR AI-DRIVEN ARCHITECTURAL SUSTAINABILITY

Abstract

This dissertation takes a unique approach to exploring the potential of AI in enhancing sustainability practices within architecture. It uses the Singapore Smart City initiative as a focal case study. Integrating artificial intelligence (AI) in architecture and urban planning represents a transformative approach to addressing the pressing challenges of sustainability and efficiency in the built environment. By leveraging AI technologies such as machine learning, neural networks, and computer vision, the research investigates how AI can optimise energy consumption, enhance building efficiency, and facilitate the use of sustainable materials. The study employs a method research design to comprehensively understand AI's impact on sustainable architecture. As a primary case study, the Singapore Smart Nation initiative integrates AI across various domains, including transportation, energy management, and environmental monitoring. This initiative demonstrates AI's capacity to reduce energy consumption through intelligent grid management and optimise real-time resource allocation. AI-driven systems significantly enhance public health and safety by improving air quality surveillance and optimising waste management processes, providing concrete examples of AI's practical applications in urban sustainability initiatives. The research identifies several barriers to the widespread adoption of AI in sustainable architecture. These include high initial costs, a need for more standardisation, and specialised technical expertise. Additionally, the study highlights ethical concerns related to data privacy and potential algorithmic biases, which must be addressed to build public trust and ensure equitable access to AI benefits; overcoming these barriers is crucial for successfully integrating AI into sustainable architecture. Strategic analyses, including PESTEL, Porter's FIVE FORCES, and SWOT, with LCA and SCINARIO ANALYSIS as analysis tools, provide a macro-environmental perspective on the factors influencing AI integration. These analyses underscore the importance of regulatory frameworks, economic incentives, and collaborative efforts between governments, industry, and academia to foster AI adoption. The dissertation proposes policy recommendations such as financial incentives, subsidies for AI research and development, and the establishment of ethical guidelines to govern AI applications in architecture. The role of continuous professional development is emphasised, advocating for the inclusion of AI and sustainability modules in architectural education and training programs. The industry can overcome resistance to technological adoption and drive innovation by equipping professionals with the necessary skills and knowledge. The dissertation also suggests the establishment of collaborative learning platforms to facilitate knowledge sharing and the development of best practices. In conclusion, the dissertation posits that AI has the potential to revolutionise sustainable architecture by providing intelligent solutions to complex environmental challenges. However, realising this potential requires overcoming significant technical, economic, and ethical barriers. Through comprehensive policy support, professional training, and international collaboration, AI can be harnessed to create resilient, eco-friendly urban spaces that meet the demands of future generations.