Field-Validated Digital Twin–Enabled Structural Health Monitoring for Offshore High-Pile Wharves

Authors

DOI:

https://doi.org/10.70917/fce-2026-010

Keywords:

Structural health monitoring (SHM), high-pile wharf; digital twin, variational autoencoder (VAE), CNN–GNN classifier, damage localization; environmental/operational variability (EOV), extended Kalman filter (EKF), modal updating; uncertainty quantification

Abstract

Offshore high-pile wharves operate under harsh, variable loads that can mask the earliest signs of deterioration. We present a closed-loop structural health monitoring (SHM) framework that couples multimodal machine/deep learning with a physics-based digital twin (DT). A year-long field deployment at an operating high-pile wharf in Western Asia instrumented six piles with triaxial accelerometers and strain gauges (GPS-synchronized, 100 Hz). After quality control and environmental/operational variability normalization, an unsupervised VAE produces an anomaly score that is fused with a CNN–GNN classifier to yield window-level damage probabilities and pile-by-elevation localization. The DT assimilates these diagnostics through weighted least squares and an EKF, with measurement covariance derived from model uncertainty; “what-if” simulations quantify the structural consequence of candidate repairs. Under chronological splits with leave-one-pile-out, the framework achieves F1 = 0.95 and AUC-ROC = 0.98, with false-alarm rate ≈ 2–5 % and median detection latency ≈ 45 s. Spatial heatmaps consistently pinpoint the affected elevation, while online updating reduces mean frequency bias from ≈ 4.8 % to ≈ 0.9 % and increases mode-shape MAC from ≈ 0.81 to ≥ 0.92. First-mode shifts of ≈ 3–4 % observed in the field are reproduced by DT “stiffness-restoration” scenarios, enabling risk-informed maintenance planning. Compared on identical normalized windows, the proposed method outperforms classical vibration-based indices in both discrimination and false-alarm control. The results demonstrate a scalable, explainable pathway to predictive asset management for critical maritime infrastructure. Beyond structural safety, the proposed framework contributes to sustainable port operation by enabling condition-based maintenance, reducing unnecessary inspections, extending asset service life, and lowering the embodied carbon associated with premature repair or replacement.

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2026-05-08 — Updated on 2026-10-21

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Vol. 12 (2026)

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Field-Validated Digital Twin–Enabled Structural Health Monitoring for Offshore High-Pile Wharves. (2026). Future Cities and Environment, 12, 22. https://doi.org/10.70917/fce-2026-010 (Original work published 2026)