Traffic and weather driven hybrid digital twin for bridge monitoring

2026-03-14

AI Summary

This study addresses the challenge of cost-effectively monitoring aging bridges under high traffic loads and harsh weather conditions, where deploying conventional sensors is often impractical. The authors propose a hybrid digital twin framework that integrates existing traffic cameras with weather APIs to enable unobtrusive, near-real-time structural health monitoring. By synergistically combining YOLOv8-based visual analysis, the Lighthill–Whitham–Richards (LWR) traffic flow model, and multi-source environmental data, the approach leverages pre-existing infrastructure without requiring additional hardware. Uncertainty in the monitoring process is quantified via Monte Carlo simulations, and a random forest model is employed to prioritize maintenance actions through risk-informed classification. Validated on the 99-year-old Peace Bridge during severe winter conditions, the framework successfully identified fatigue accumulation risks, demonstrating its feasibility, cost efficiency, and predictive capability.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Transportation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• bridge monitoring
• digital twin
• predictive maintenance
• traffic load
• environmental deterioration

Innovation

• hybrid digital twin
• computer vision
• traffic-flow modeling
• environmental deterioration
• predictive maintenance