From Infrastructure Assets to Infrastructure Intelligence: Reimagining Civil Engineering for an Era of Artificial Intelligence, Climate Uncertainty, and Autonomous Infrastructure Systems

Civil engineering enters the second quarter of the twenty-first century carrying a conceptual inheritance it has rarely interrogated directly. This paper advances a single, falsifiable thesis: the defining failure of twentieth-century civil engineering was not a failure of materials, computation, or design method, but a category error in the treatment of infrastructure as a class of physical assets to be optimized rather than as a class of adaptive, decision-making systems operating under irreducible uncertainty.

We call this inheritance the Asset Paradigm and argue that it persists because it is embedded in codes, procurement, liability law, curricula, and professional identity, not because it remains effective. Building on, and formally sharpening, the emerging literature on infrastructure intelligence and resilience, we contribute: (i) necessary and sufficient conditions that distinguish Infrastructure Intelligence from smart infrastructure, digital twins, and cyber-physical systems, together with a quantitative formalization; (ii) a five-stage Infrastructure Evolution Model, classifying real systems by the locus of run-time decision authority and justified against maturity-model precedents from other engineering domains; (iii) four empirically testable propositions, each with explicit variables, candidate datasets, and validation methods; (iv) an illustrative bridge structural-health-monitoring application; (v) a phased, institutionally feasible transition pathway; and (vi) a ten-item Grand Research Agenda for 2025–2050. We position the framework against complex adaptive systems, socio-technical, resilience, adaptive-governance, and deep-uncertainty theory, and identify precisely where each breaks down under conditions of artificial intelligence, climate non-stationarity, and machine-speed autonomy. We conclude with a set of concrete claims and institutional commitments.

infrastructure intelligence; asset paradigm; complex adaptive systems; climate non-stationarity; adaptive governance

1. M. Amin, “Toward self-healing energy infrastructure systems,” IEEE Computer Applications in Power, vol. 14, no. 1, pp. 20–28, 2001. [Google Scholar] [Crossref]

2. W. B. Arthur, “Complexity and the economy,” Science, vol. 284, no. 5411, pp. 107–109, 1999. [Google Scholar] [Crossref]

3. C. Axenie, M. Akbarzadeh, M. A. Makridis, M. Saveriano, and A. Stancu, Applied Antifragility in Technical Systems: From Principles to Applications. Cham: Springer Nature, 2025. [Google Scholar] [Crossref]

4. R. Bommasani et al., “On the Opportunities and Risks of Foundation Models,” arXiv preprint arXiv:2108.07258, 2021. [Google Scholar] [Crossref]

5. CMMI Product Team, CMMI for Development, Version 1.3 (CMU/SEI-2010-TR-033). Pittsburgh, PA: Software Engineering Institute, Carnegie Mellon University, 2010. [Google Scholar] [Crossref]

6. N. Dalkey and O. Helmer, “An experimental application of the Delphi method to the use of experts,” Management Science, vol. 9, no. 3, pp. 458–467, 1963. [Google Scholar] [Crossref]

7. C. Folke, “Resilience: the emergence of a perspective for social, ecological systems analyses,” Global Environmental Change, vol. 16, no. 3, pp. 253–267, 2006. [Google Scholar] [Crossref]

8. C. Folke, T. Hahn, P. Olsson, and J. Norberg, “Adaptive governance of social-ecological systems,” Annual Review of Environment and Resources, vol. 30, pp. 441–473, 2005. [Google Scholar] [Crossref]

9. F. W. Geels, “From sectoral systems of innovation to socio-technical systems,” Research Policy, vol. 33, no. 6–7, pp. 897–920, 2004. [Google Scholar] [Crossref]

10. M. Haasnoot, J. H. Kwakkel, W. E. Walker, and J. ter Maat, “Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world,” Global Environmental Change, vol. 23, no. 2, pp. 485–498, 2013. [Google Scholar] [Crossref]

11. J. H. Holland, “Complex adaptive systems,” Daedalus, vol. 121, no. 1, pp. 17–30, 1992. [Google Scholar] [Crossref]

12. C. S. Holling, “Resilience and stability of ecological systems,” Annual Review of Ecology and Systematics, vol. 4, pp. 1–23, 1973. [Google Scholar] [Crossref]

13. INCOSE, Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, 4th ed. Hoboken, NJ: Wiley, 2015. [Google Scholar] [Crossref]

14. F. Jiang, L. Ma, T. Broyd, and K. Chen, “Digital twin and its implementations in the civil engineering sector,” Automation in Construction, vol. 130, art. 103838, 2021. [Google Scholar] [Crossref]

15. Y. Jiang, J. Wang, X. Shen, and K. Dai, “Large language model for post-earthquake structural damage assessment of buildings,” Computer-Aided Civil and Infrastructure Engineering, vol. 40, no. 31, pp. 6324–6342, 2025. [Google Scholar] [Crossref]

16. J. H. Kwakkel, M. Haasnoot, and W. E. Walker, “Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world,” Climatic Change, vol. 132, no. 3, pp. 373–386, 2015. [Google Scholar] [Crossref]

17. E. A. Lee, “Cyber physical systems: design challenges,” in Proc. 11th IEEE Int. Symp. Object and Component-Oriented Real-Time Distributed Computing, 2008, pp. 363–369. [Google Scholar] [Crossref]

18. R. J. Lempert, S. W. Popper, and S. C. Bankes, Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. Santa Monica, CA: RAND Corporation, 2003. [Google Scholar] [Crossref]

19. R. G. Little, “Controlling cascading failure: understanding the vulnerabilities of interconnected infrastructures,” Journal of Urban Technology, vol. 9, no. 1, pp. 109–123, 2002. [Google Scholar] [Crossref]

20. J. Liu, Z. Geng, R. Cao, L. Cheng, P. Bocchini, and M. Cheng, “A large language model-empowered agent for reliable and robust structural analysis,” Structure and Infrastructure Engineering, 2026. [Google Scholar] [Crossref]

21. J. Markard, R. Raven, and B. Truffer, “Sustainability transitions: an emerging field of research and its prospects,” Research Policy, vol. 41, no. 6, pp. 955–967, 2012. [Google Scholar] [Crossref]

22. J. Michael, J. Blankenbach, J. Derksen, B. Finklenburg, R. Fuentes, T. Gries, S. Hendiani, S. Herlé, S. Hesseler, M. Kimm, et al., “Integrating models of civil structures in digital twins: state-of-the-art and challenges,” Journal of Infrastructure Intelligence and Resilience, vol. 3, no. 3, art. 100100, 2024. [Google Scholar] [Crossref]

23. P. Michailidis, I. Michailidis, C. R. Lazaridis, and E. Kosmatopoulos, “Traffic signal control via reinforcement learning: a review on applications and innovations,” Infrastructures, vol. 10, no. 5, art. 114, 2025. [Google Scholar] [Crossref]

24. P. C. D. Milly, J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer, “Stationarity is dead: whither water management?” Science, vol. 319, no. 5863, pp. 573–574, 2008. [Google Scholar] [Crossref]

25. National Institute of Standards and Technology (NIST), AI Risk Management Framework (AI RMF 1.0). Gaithersburg, MD: U.S. Department of Commerce, 2023. [Google Scholar] [Crossref]

26. T. Nyokum and Y. Tamut, “Artificial intelligence in civil engineering: emerging applications and opportunities,” Frontiers in Built Environment, vol. 11, art. 1622873, 2025. [Google Scholar] [Crossref]

27. A. O’Connor, Rethinking Infrastructure as Assets: Policy, Regulation and Design. London: Routledge, 2019. [Google Scholar] [Crossref]

28. E. Ostrom, “Beyond markets and states: polycentric governance of complex economic systems,” American Economic Review, vol. 100, no. 3, pp. 641–672, 2010. [Google Scholar] [Crossref]

29. SAE International, J3016_202104: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. Warrendale, PA: SAE International, 2021. [Google Scholar] [Crossref]

30. H. Shokravi, M. Vafaei, B. Samali, and N. Bakhary, “In-fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data,” Computer-Aided Civil and Infrastructure Engineering, vol. 39, no. 14, pp. 2122–2139, 2024. [Google Scholar] [Crossref]

31. N. N. Taleb, Antifragile: Things That Gain from Disorder. New York: Random House, 2012. [Google Scholar] [Crossref]

32. M. Torzoni, M. Tezzele, S. Mariani, A. Manzoni, and K. E. Willcox, “A digital twin framework for civil engineering structures,” Computer Methods in Applied Mechanics and Engineering, vol. 418, art. 116584, 2024. [Google Scholar] [Crossref]

33. E. L. Trist and K. W. Bamforth, “Some social and psychological consequences of the longwall method of coal-getting,” Human Relations, vol. 4, no. 1, pp. 3–38, 1951. [Google Scholar] [Crossref]

34. S. Youwai, D. Phim, V. G. Murcia, and R. C. Onas, “Large language model-based multi-agent systems for automated foundation design: router-driven task classification and expert selection framework,” AI in Civil Engineering, vol. 5, no. 1, art. 5, 2026. [Google Scholar] [Crossref]

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