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International Journal of Next-Generation Engineering and Technology

Open Access Peer Review International
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An AI-Enabled Framework for Polycrisis Risk Mitigation in Residential High-Rise Buildings: A Systematic and Critical Literature Synthesis of Urban Resilience Strategies

DOI https://doi.org/10.55640/ijnget-v03i08-04
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Dr. Nimal Perera 1 , Ms. Ishani Fernando 1 ,
4 Faculty of Engineering, Colombo Institute of Digital Technologies, Colombo, Sri Lanka
4 Department of Information Systems, University of Advanced Science and Innovation, Kandy, Sri Lanka

Abstract

The increasing complexity of urban systems has intensified the exposure of residential high-rise buildings to interconnected and cascading risks commonly conceptualized as a “polycrisis,” where multiple systemic disruptions interact simultaneously across environmental, infrastructural, technological, and socio-economic dimensions. This study develops an AI-enabled conceptual framework for polycrisis risk mitigation in residential high-rise buildings through a systematic and critical synthesis of existing literature on smart buildings, crisis management systems, and artificial intelligence applications in the built environment. The research integrates advancements in Internet of Things (IoT)-enabled infrastructures, predictive analytics, and multi-level crisis governance models to propose an adaptive resilience architecture capable of responding to dynamic urban risk environments.

Findings indicate that current building resilience approaches remain fragmented, with limited integration between real-time data acquisition systems, AI-based decision-making models, and evacuation optimization mechanisms. While smart building technologies demonstrate significant potential for operational efficiency and predictive maintenance, their application to multi-hazard crisis convergence remains underdeveloped. This study identifies critical gaps in interoperability, scalability, and ethical governance within AI-driven urban resilience systems. The proposed framework synthesizes these dimensions into a unified architecture combining predictive risk modeling, adaptive control systems, and multi-agent decision support.

The study contributes to urban resilience theory by bridging AI-enabled computational intelligence with crisis management strategies for high-rise infrastructure. It also highlights the necessity of integrated policy-technology ecosystems to address systemic vulnerabilities amplified under polycrisis conditions, as emphasized in global risk discourse (Torkington, 2023).

Keywords

Polycrisis, artificial intelligence, high-rise buildings, urban resilience

References

S. Torkington, “We’re on the brink of a ‘polycrisis’ – how worried should we be?”, World Economic Forum, 2023. [Online]. Available: https://www.weforum.org/stories/2023/01/polycris is-global-risks-report-cost-of-living/
R. V. Bianchi and C. Milano, “Polycrisis and the metamorphosis of tourism capitalism,” Annals of Tourism Research, vol. 104, p. 103731, 2024.
Economic and Social Commission for Asia and the Pacific (ESCAP), “Upcoming UN development forum to tackle polycrisis impacts in Asia and the Pacific,” 2023. [Online]. Available: https://www.unescap.org/news/upcoming-un-development-forum-tackle-polycrisis-impacts-asia-and-pacific
E. Ronchi and D. Nilsson, Assessment of Total Evacuation Systems for Tall Buildings. New York: Springer, 2014.
M. Jia et al., “Adopting Internet of Things for the development of smart buildings: A review of enabling technologies and applications,” Automation in Construction, vol. 101, pp. 111–126, 2019.
A. Kaklauskas et al., “Crisis management in construction and real estate: Conceptual modeling at the micro-, meso- and macro-levels,” Land Use Policy, vol. 28, no. 1, pp. 280–293, 2011.
A. Temenos et al., “C2A-DC: A context-aware adaptive data cube framework for environmental monitoring and climate change crisis management,” Remote Sensing Applications: Society and Environment, vol. 34, p. 101171, 2024.
B. Ekici et al., “Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: Background, methodology, setup, and machine learning
results,” Solar Energy, vol. 224, pp. 373–389, 2021.
S. Alsharari et al., “AI-enhanced IT governance: Fostering autonomy, decision-making, and human accountability,” Kurdish Studies, vol. 11, no. 3, pp. 482–492, 2023.
B. A. Adewale et al., “A systematic review of the applications of AI in a sustainable building’s
lifecycle,” Buildings, vol. 14, no. 7, p. 2137, 2024.
M. Gusenbauer and N. R. Haddaway, “Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources,” Research Synthesis Methods, vol. 11, no. 2, pp. 181–217, 2020.
V. K. Singh et al., “The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis,” Scientometrics, vol. 126, pp. 5113–5143,
R. Pranckutė, “Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world,” Publications, vol. 9, no. 1, p. 12,
M. K. Sahu, “Scientific research on cutting-edge technology: A scientometric approach on IEEE Xplore Digital Library,” College Libraries, vol. 35, no. IV, pp. 67–81, 2020.
D. Byrne, “A worked example of Braun and Clarke’s approach to reflexive thematic analysis,” Quality & Quantity, vol. 56, no. 3, pp. 1391–1412, 2022.
M. J. Page et al., “PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for systematic reviews,” BMJ, vol. 372, 2021.
A. H. Kelechi et al., “Artificial intelligence: An energy efficiency tool for enhanced high performance computing,” Symmetry, vol. 12, no. 6,
p. 1029, 2020.
X. Ma et al., “An Internet of Things-based lift predictive maintenance system,” IEEE Potentials, 2020.

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