Traffic and weather driven hybrid digital twin for bridge monitoring

Phani Raja Bharath Balijepalli; Bulent Soykan; Veeraraghava Raju Hasti

cs.AIMar 14, 2026

Traffic and weather driven hybrid digital twin for bridge monitoring

Authors: Phani Raja Bharath Balijepalli, Bulent Soykan, Veeraraghava Raju Hasti

Organizations: University of Central Florida, Orlando, FL, USA

Abstract

A hybrid digital twin framework is presented for bridge condition monitoring using existing traffic cameras and weather APIs, reducing reliance on dedicated sensor installations. The approach is demonstrated on the Peace Bridge (99 years in service) under high traffic demand and harsh winter exposure. The framework fuses three near-real-time streams: YOLOv8 computer vision from a bridge-deck camera estimates vehicle counts, traffic density, and load proxies; a Lighthill--Whitham--Richards (LWR) model propagates density $ρ(x,t)$ and detects deceleration-driven shockwaves linked to repetitive loading and fatigue accumulation; and weather APIs provide deterioration drivers including temperature cycling, freeze-thaw activity, precipitation-related corrosion potential, and wind effects. Monte Carlo simulation quantifies uncertainty across traffic-environment scenarios, while Random Forest models map fused features to fatigue indicators and maintenance classification. The framework demonstrates utilizing existing infrastructure for cost-effective predictive maintenance of aging, high-traffic bridges in harsh climates.

Figures & tables

Panel of Figure 1 — Figure 1: High-level hybrid digital twin workflow for camera- and weather-driven bridge condition monitoring.

Figure 2: Time-varying structural reliability index ( $\beta$ ) from the hybrid digital twin: simulated ( $\beta_{\mathrm{sim}}$ ), observation-derived ( $\beta_{\mathrm{obs}}$ ), and the composite primary series ( $\beta_{\mathrm{primary}}$ ), which defaults to $\beta_{\mathrm{obs}}$ when available and $\beta_{\mathrm{sim}}$ otherwise. All three remain above the target threshold ( $\beta=3.0$ ) throughout the monitoring window.

Figure 3: Simulated shockwave speeds from the LWR traffic flow model compared against observed proxies inferred from abrupt density and speed transitions. The model reproduces the timing and relative magnitude of major stop–go event; high-amplitude spikes in the observed signal are attributed to measurement variability rather than physical wave speeds.

Figure 4: Probability distribution of fatigue scores from Monte Carlo simulation under stochastic traffic demand, with threshold markers at fatigue scores of 50 (safe) and 70 (monitor). The distribution characterizes the range of structural demand outcomes across randomized traffic realizations.

Structural Health Monitoring

Structural Health Monitoring uses sensor data and computational models to detect, locate, and assess damage in infrastructure and engineered systems.

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Sep 25, 2026J/K move · Enter open · S save

A Vehicle-Integrated Approach to Digital Twin Deployment for Bridges Through Drive-By Sensing

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Structural Health Monitoring Digital Twins

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Ageing bridge infrastructure is a growing global concern, yet conventional Structural Health Monitoring (SHM) systems are costly and difficult to scale, and routine visual inspections remain subjective. Drive-by, or indirect, bridge inspection, in which a sensorised vehicle recovers structural information from vehicle-bridge interaction (VBI) and vehicle-road interaction (VRI) responses, offers a scalable alternative. However, key challenges remain unresolved, including separating bridge responses from road roughness, detecting damage under normal traffic, and generalising across diverse bridge types. This paper presents a vehicle-integrated digital twin framework that unifies physics-based modelling and machine learning for continuous monitoring of bridge and road conditions. The framework comprises three pillars. First, surrogate models of VBI and VRI are constructed using a Fourier Neural Operator that learns function-to-function mappings from operating conditions to vehicle responses. Trained on both simulated and field data, these surrogates deliver millisecond-scale inference, replacing computationally intensive full-order analyses. Second, the design of a custom electric inspection vehicle, its sensor layout, and signal processing chain are optimised through Bayesian optimisation to maximise bridge information yield while suppressing road and vehicle noise. Unsupervised damage-assessment pipelines based on adversarial autoencoders, matrix profiles, and transformer architectures have been developed and validated to process the resulting vehicle data. Third, the complete workflow is validated through coordinated multi-site field trials in Australia and Japan, covering a range of bridge types, traffic conditions, and environmental settings.

Abstract

Structural Health Monitoring

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