AI-DRIVEN POLYCRISIS MITIGATION STRATEGIES IN RESIDENTIAL HIGH-RISE BUILDINGS: A CRITICAL LITERATURE ANALYSIS

Abstract

In the context of escalating global polycrises, residential high-rises face unprecedented challenges that threaten infrastructure and occupant safety. This literature review examines the role of Artificial Intelligence (AI) in managing these crises within vertical communities. Covering studies from 2014 to 2024, the review utilized a comprehensive search strategy across Web of Science, Scopus, IEEE Xplore, Google Scholar, and PubMed, focusing on AI, polycrisis, and high-rise buildings. The analysis identified 87 relevant studies showcasing the potential of AI technologies — such as machine learning, natural language processing, and computer vision — to enhance crisis management.  Key findings indicate that AI can significantly improve emergency preparedness, real-time information dissemination, and resource optimization. Computer vision advances hazard detection accuracy, facilitating faster evacuations and better safety. The integration of IoT sensor networks with predictive analytics reduces false alarms and enhances early threat detection. However, the review also highlights challenges, including data privacy, cybersecurity, scalability, and the balance between AI autonomy and human oversight.  Future research should address these limitations by focusing on real-world case studies to assess AI performance across diverse crisis scenarios. Development of more robust AI models and their integration with existing crisis management frameworks is essential. Ethical considerations, particularly regarding privacy and bias, must also be scrutinized to ensure that AI solutions are both effective and equitable. This review provides valuable insights into using AI to bolster urban resilience and support Sustainable Development Goals in high-rise residential settings, emphasizing the need for strategic AI integration and ongoing research to address current gaps and challenges.