A Unified Digital Twin Framework for Autonomous Lifecycle Management of Civil Infrastructure: Integrating Cyber–Physical Systems, AI-Driven Predictive Analytics, and Real-Time Optimization
TL;DR
A unified DT framework that couples a cyber-physical sensing and communication layer, an entropy-weighted multi-sensor fusion scheme, a hybrid physics-plus-machine-learning surrogate, extended Kalman assimilation for continuous model updating, and a constrained multi-objective optimisation layer that closes the loop from measurement to intervention across all five lifecycle phases of an asset.
Abstract
Civil infrastructure assets are managed today through inspection cycles and calendar-based interventions that observe an asset only at discrete instants, so deterioration between visits is inferred rather than measured. Digital Twin (DT) research has addressed parts of this problem, but published frameworks remain confined either to a single lifecycle phase or to a single enabling technology, and few report reproducible validation against public benchmarks. This paper presents a unified DT framework that couples a cyber-physical sensing and communication layer, an entropy-weighted multi-sensor fusion scheme, a hybrid physics-plus-machine-learning surrogate, extended Kalman assimilation for continuous model updating, and a constrained multi-objective optimisation layer that closes the loop from measurement to intervention across all five lifecycle phases of an asset. The contribution is threefold: a phase-invariant state formulation in which design, construction, operation, maintenance and end-of-life are expressed as a single evolving state trajectory; an adaptive physics-to-data blending coefficient calibrated on held-out data rather than fixed a priori; and a reliability-constrained decision layer in which maintenance actions are selected subject to an explicit failure-probability bound. The framework is exercised on a provisional synthetic dataset generated deterministically from a calibrated finite element model of a three-span reinforced concrete girder bridge, and is benchmarked against traditional, IoT-only and AI-only baselines under an identical protocol. All reported results are simulation outcomes; no field validation, laboratory validation or real-time deployment has been performed. The DT configuration attains 96.0% detection accuracy, with a 95% Wilson interval of 95.0 to 96.8, and an area under the curve of 0.98, against 90.0% and 0.92 for the strongest AI-only baseline, every pairwise difference being significant at p below 0.001 after Holm-Bonferroni adjustment. Remaining useful life error falls from 4.80 to 1.30 years, median end-to-end latency from 250 ms to 120 ms, and the annual failure rate from 0.080 to 0.020, raising twenty-year reliability from 0.202 to 0.670 with a discounted break-even at year 5.8. Detection accuracy remains at or above 92% across high-temperature, heavy-load and seismic operating states. A component ablation attributes the margin principally to environmental normalisation at 7.4 percentage points, the physics constraint at 6.0 and continuous assimilation at 4.8, rather than to model capacity, which is held constant against the AI-only baseline. Deployment barriers - interoperability, cybersecurity, cost, power and validation under uncertainty - are quantified and mapped to mitigation measures and residual risk, and a reproducibility statement specifies the dataset generator, splits, hyperparameters, seeds and computing environment, from which every reported figure is exactly reproducible.