An AI-IoT Conceptual Framework for Predictive Risk Management in Smart City Subsurface Infrastructure: Insights from Tehran

Authors

  • Seyed Ali Seyedian * Department of Architecture, University of Mazandaran Babolsar, Mazandaran, Iran.
  • Sara Motevalli Department of Architecture, University of Tehran, Tehran, Iran.

https://doi.org/10.22105/sci.v3i1.53

Abstract

Rapid urbanization and the increasing complexity of underground infrastructure systems in megacities such as Tehran have intensified geotechnical and structural risks, necessitating a transition from reactive monitoring toward predictive and intelligent risk governance. Although recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technologies have transformed smart infrastructure management, significant gaps remain regarding the integration of these technologies within developing urban governance systems. This study proposes a conceptual AI-IoT framework for predictive risk management in smart city subsurface infrastructure using a documentary and descriptive-analytical research approach. The proposed framework consists of three interrelated layers: 1) an IoT-enabled sensing layer for continuous geotechnical and structural monitoring, 2) an intelligent analytics layer employing AI-driven predictive models and Digital Twin simulations for anomaly detection and risk forecasting, and 3) a decision-support layer facilitating adaptive governance and predictive maintenance. Focusing on Tehran as a representative developing megacity, the study analytically examines institutional fragmentation, interoperability limitations, and governance barriers affecting intelligent infrastructure implementation. The findings suggest that integrated AI-IoT governance frameworks can significantly enhance urban infrastructure resilience, predictive decision-making, and adaptive risk management in subsurface smart city systems. The study contributes theoretically to cyber-physical infrastructure governance literature and provides a conceptual foundation for future empirical implementation of predictive smart infrastructure systems in developing urban environments.

Keywords:

Artificial intelligence, Internet of thing, Predictive risk management, Smart city infrastructure, Digital twin, Underground infrastructure, Cyber-physical systems, Tehran.

References

  1. [1] Shen, Y. (2025). Integration of IoT and digital twin for intelligent management of urban underground pipe galleries in smart cities. Informatica, 49(15), 179–192. https://doi.org/10.31449/inf.v49i15.7903
  2. [2] Cong, Y., & Inazumi, S. (2024). Integration of smart city technologies with advanced predictive analytics for geotechnical investigations. Smart Cities, 7(3), 1089–1108. https://doi.org/10.3390/smartcities7030046
  3. [3] Adreani, L., Bellini, P., Fanfani, M., Nesi, P., & Pantaleo, G. (2024). Smart city digital twin framework for real-time multi-data integration and wide public distribution. IEEE Access, 12, 76277–76303. https://doi.org/10.1109/ACCESS.2024.3406795
  4. [4] Caragliu, A., Bo, C. Del, & Nijkamp, P. (2009). Smart cities in Europe. Journal of Urban Technology, 18. https://doi.org/10.1080/10630732.2011.601117
  5. [5] Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., & Portugali, Y. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214(1), 481–518. https://doi.org/10.1140/epjst/e2012-01703-3%0A%0A
  6. [6] Bibri, S. E. (2018). Smart Sustainable Cities of The Future. Springer. https://doi.org/10.1007/978-3-319-73981-6%0A%0A
  7. [7] Masoumi, H., Shirowzhan, S., Eskandarpour, P., & Pettit, C. J. (2023). City digital twins: Their maturity level and differentiation from 3D city models. Big Earth Data, 7(1), 1–36. https://doi.org/10.1080/20964471.2022.2160156
  8. [8] Zali, N., Soltani, A., Najafi, P., Qajari, S. E., & Mehrju, M. (2024). Digital twins for smarter Iranian cities: A future studies perspective. Computational Urban Science, 4(1), 43. https://doi.org/10.1007/s43762-024-00155-9%0A%0A
  9. [9] Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243. https://doi.org/10.1109/TII.2014.2300753
  10. [10] Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), 22–32. https://doi.org/10.1109/JIOT.2014.2306328
  11. [11] Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14. https://doi.org/10.1007/s10708-013-9516-8%0A%0A
  12. [12] Hollands, R. (2008). Will the real smart city stand up? City, 12(3), 302–320. https://doi.org/10.1080/13604810802479126
  13. [13] Shariatpour, F., Behzadfar, M., & Zareei, F. (2022). City information modeling (CIM) and digital twin approach for urban planning, design and management in the smart city age (Case study: Narmak neighborhood of Tehran city, Iran). SSRN Electronic Journal (Elsevier). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4231292
  14. [14] Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/10.1007/s11747-019-00710-5%0A%0A
  15. [15] Gunning, D., & Aha, D. W. (2019). DARPA’s explainable artificial intelligence program. AI Magazine, 40(2), 44–58. https://doi.org/10.1609/aimag.v40i2.2850
  16. [16] Grieves, M., & Vickers, J. (2016). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4%0A%0A
  17. [17] Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber--physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653–661. https://doi.org/10.1016/j.eng.2019.01.014
  18. [18] Kaewunruen, S., Peng, S., & Phil Ebosie, O. (2020). Digital twin aided sustainability and vulnerability audit for subway stations. Sustainability, 12(19), 7873. https://doi.org/10.3390/su12197873
  19. [19] Shariatpour, F., Behzadfar, M., & Zareei, F. (2024). Urban 3d modeling as a precursor of city information modeling and digital twin for smart city era: A case study of the narmak neighborhood of tehran city, Iran. Journal of Urban Planning and Development, 150(2), 4024005. https://doi.org/10.1061/JUPDDM.UPENG-4650
  20. [20] Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in Industry, 101, 1–12. https://doi.org/10.1016/j.compind.2018.04.015
  21. [21] Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
  22. [22] Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027
  23. [23] Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006
  24. [24] Aven, T. (2012). The risk concept—historical and recent development trends. Reliability Engineering & System Safety, 99, 33–44. https://doi.org/10.1016/j.ress.2011.11.006

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Published

2026-01-20

Issue

Vol. 3 No. 1 (2026): Smart City Insights

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Articles

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How to Cite

Seyedian, S. A. ., & Motevalli, S. . (2026). An AI-IoT Conceptual Framework for Predictive Risk Management in Smart City Subsurface Infrastructure: Insights from Tehran. Smart City Insights, 3(1), 1-15. https://doi.org/10.22105/sci.v3i1.53

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