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

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.

adaptive governance; autonomous infrastructure; deep uncertainty; decision-making under deep uncertainty;

antifragility; digital twins

A Discipline at the Limit of Its Own Assumptions

mistake it for reality.

That model is the treatment of infrastructure as a portfolio of assets: discrete physical objects, designed once

to a specification, commissioned, depreciated, maintained against a predictable decay curve, and eventually

renewed. The Asset Paradigm is so deeply naturalized that the phrase “asset management” is taught, regulated,

and procured as though it were a neutral description of what infrastructure is, rather than one historically

contingent way of construing it [1, 27]. This paper’s central claim is that this construal is a category error

whose costs were tolerable in the twentieth century and are becoming catastrophic in the twenty-first.

The argument is organized around a thesis stated plainly so that it can be attacked: the fundamental failure of

twentieth-century civil engineering was not a failure of materials, computation, or design methods, but a

category error treating infrastructure as a class of physical assets to be optimized rather than as a class of

The label “infrastructure intelligence” is not introduced here for the first time, and we do not claim that prior

research has overlooked artificial intelligence in civil systems the opposite is true, and Section 6 surveys a

fast-moving literature on exactly this. What that literature has generally supplied, however, is technology-

or component-level: specific digital-twin architectures, specific sensing or learning methods, specific AI

applications within a single asset class [22, 14, 20, 34].

“Infrastructure intelligence” has, in parallel, emerged as an organizing label for this activity, most visibly

through a dedicated Elsevier journal of that name launched in 2022. What has not, to our knowledge, been

supplied is a systems-level paradigm: a domain-general account, expressed as necessary and sufficient

conditions, of what makes any of these technologies constitute intelligence in our sense, independent of

which sensor, model, or control method is used. That is the gap Section 4 addresses.

Section 4 defines Infrastructure Intelligence, including a variable-based formalization. Section 5 develops

the Infrastructure Evolution Model and relates it to established maturity-model precedents. Section 6

reframes enabling technologies, including recent foundation-model and agentic-AI developments, as

expressions of a deeper shift. Section 7 presents the conceptual figures. Section 8 works through an

illustrative bridge structural-health-monitoring application. Section 9 formulates falsifiable research

questions and testable propositions with candidate variables, datasets, and validation methods. Section 10

examines domain-specific implications, transition barriers, and a phased transition pathway. Section 11

addresses ethics, governance, and cybersecurity. Section 12 critiques the profession itself and evaluates

objections. Section 13 states the framework’s limitations and validation pathway, before the Grand Research

Agenda and a concluding set of claims.

Necessary and sufficient conditions for Infrastructure Intelligence, together with a quantitative

formalization intended as a basis for future measurement rather than a validated instrument (Section

4).

5).

Four falsifiable propositions, each with explicit variables, candidate datasets, and applicable

validation methods (Section 9).

An illustrative bridge structural-health-monitoring application that traces the framework from current

practice through to expected engineering benefit (Section 8).

A phased, institutionally feasible transition pathway spanning policy, standards, procurement,

and certification (Section 10).

A ten-item Grand Research Agenda for 2025–2050 (Section 14).

A framework paper earns its standing not by inventing concepts from nothing but by showing precisely

behave but is silent on how a professional discipline bearing legal liability should intervene in a system

whose components now include learning algorithms that adapt faster than their human supervisors can

comprehend. CAS theory assumes an observer outside the system; infrastructure intelligence collapses the

observer into the system.

Socio-technical systems (STS) theory [9, 33] correctly insists that technical artefacts and social institutions

co-evolve. Its limit is the implicit assumption that the technical subsystem is a passive tool wielded by

social actors. When the technical subsystem becomes an autonomous decision-maker, the symmetry STS

theory presumes between human and artefact dissolves: the artefact is no longer mediated by human agency

but is itself an agent. STS theory has no vocabulary for a technical actor with goals.

Resilience theory [7, 12] re-framed engineering from resisting disturbance to absorbing and recovering from

method for procuring or certifying a structure that should improve under stress. This is not, however, a

purely theoretical vacuum: a nascent technical literature has begun formalizing antifragile design principles

for closed-loop traffic-control, robotic, and cyber-physical systems [3], and it is a natural next step named

explicitly in our Grand Research Agenda, Challenge 2, to test whether these formalisms transfer to load-

bearing civil structures.

Cyber-physical systems (CPS) and systems engineering theory [13, 17] provide the architecture for tightly

coupled computation and physical process. Their limit is philosophical: both assume design-time

completeness that the system’s requirements can be specified before it is built. Infrastructure intelligence is

defined by the abandonment of this assumption.

pathways support infrastructure investment when the future probability distribution of loads and demands

cannot be specified in advance, with an established track record in water and coastal planning. DMDU

supplies the operational vocabulary Proposition 1 (Section 9) needs adaptation tipping points, monitored

signposts, contingency pathways and is the closest existing analogue to the adaptation-rate criterion

Adaptive governance theory [8, 28] and sustainability transitions theory [9, 21] describe how institutions

learn and how socio-technical regimes shift. Both assume governance operates at human deliberative speed.

Neither contemplates a regime in which the governed system acts faster than any deliberative body can

convene.

Infrastructure systems theory [1, 19] supplies interdependency and cascade analysis but retains the asset

ontology this paper rejects.

The pattern is consistent: each theory assumes one of an external observer, a passive technical substrate, a

stationary disturbance regime, design-time completeness, or human-speed deliberation. Infrastructure

intelligence violates all five simultaneously. The Infrastructure Intelligence Framework developed below is

the asset is for is known and stable; (2) stationarity the environment of loads and demands fluctuates

within a stable, knowable distribution; (3) deterministic degradation deterioration follows curves estimable

from material science; and (4) separability each asset can be analyzed, financed, and managed as an isolable

unit.

The paradigm is a child of the mid-twentieth century. Post-war reconstruction and the great infrastructure

expansion of 1945–1975 rewarded exactly the capabilities the Asset Paradigm formalizes: the rapid,

standardized, high-volume delivery of discrete works to deterministic specifications [27]. The intellectual

scaffolding arrived in parallel: the codification of limit-state design, the institutionalization of the factor of

safety, and decisively the importation of stationarity as an unexamined axiom of hydrological and structural

Uncertainty-native operation: its decision procedures represent and act under irreducible uncertainty rather

than assuming a known environment.

instrumentation, not as a validated measurement instrument, which would itself require the expert-elicitation

= 1], (1)

where II

(t) = 1 if and only if system i possesses Infrastructure Intelligence at time t in the strict, necessary-

and-sufficient sense of Section 4.3; sensing S

(t) is a precondition for a data-updated M

(t) rather than an

independent necessary condition, consistent with Table 1.

For the graded maturity classification of Section 5, where the question is not whether a system qualifies but

how far along the evolution axis it sits, we propose a companion continuous index,

via the Delphi and comparative-case procedures of Section 5. Equation 2 is deliberately weaker than

Equation 1: it supports ranking and stage-boundary estimation, but because it is a weighted sum it can assign

a high score to a system that is high on some variables and zero on others, which Equation 1’s conjunction

correctly excludes. The two are intended to be used together: Equation 1 to classify, Equation 2 to rank

within a class.

Proposition 1 in Section 9 can now be stated in these terms. Let α

updating rate and let

β denote the rate of drift of the environment’s governing distribution (e.g., the rate of change of design-

We now develop the paper’s principal analytical instrument: a five-stage model of infrastructure evolution.

We stress at the outset that this is not a technology-maturity ladder. The stages are differentiated by the

locus of decision-making authority and the governance structure, not by the sophistication of the hardware.

A bridge stuffed with sensors that changes nothing about who decides remains Infrastructure 1.0 with

telemetry.

deterministic, codified ex ante. Uncertainty model: safety factors absorb a stationary envelope. Human

role: designer-commander. Governance: hierarchical, code-compliant. Failure mode: brittle exceedance of

design loads. Resilience: robustness via over-specification. Vulnerability: unmodelled non-stationary

decide. Knowledge model: predictive, learned. Uncertainty: modelled probabilistically. Human role:

supervisor. Governance: socio-technical. Failure mode: automation bias, model misspecification.

Vulnerability: brittle confidence in learned models outside their training distribution.

Knowledge model: reflexive, self-updating. Uncertainty: treated as irreducible and managed. Human

role: steward, setting goals and constraints. Governance: adaptive, polycentric. Failure mode: goal

misalignment, adaptation to the wrong objective. Resilience: graceful degradation, antifragile potential.

Vulnerability: value misspecification.

constitutional limits. Knowledge model: generative, self-revising. Uncertainty: native. Human role:

constitutional designer and final arbiter. Governance: machine-speed with human constitutional oversight.

5.0 ramp-metering over a 1.0 deck. The model treats stage as a property of decision functions, not whole

assets, so hybridity is expected, not anomalous. Necessary condition for stage n + 1: the decision authority

demonstrably resides one level further from design-time human specification than at stage n. Sufficient

condition: that authority is exercised in operation under uncertainty. Boundary condition: the human role

transforms qualitatively (commander → monitor → supervisor → steward→ constitutional arbiter). Failure

condition: a stage is unstable when its governance structure lags its decision locus.

A five-level, decision-authority-defined staging is not without precedent. The closest analogue is SAE

International’s taxonomy of driving-automation levels [29], which defines six levels (0–5) by the allocation

of the dynamic driving task between human and system, explicitly rejecting sensor count or software

are not directly transferable they describe organizational process maturity, not decision authority over a

physical system but the precedent supports two choices made here: that five levels is a defensible resolution

for an engineering maturity model (finer-grained models have generally not achieved adoption, since

assessors rarely discriminate more than five to seven ordinal levels reliably), and that stage boundaries

should be qualitative changes in control structure rather than thresholds on a continuous score, which is why

Equation 1 rather than Equation 2 anchors the stage definitions.