Traffic and weather driven hybrid digital twin for bridge monitoring
This work addresses predictive maintenance for aging, high-traffic bridges in harsh climates, offering a cost-effective solution by leveraging existing infrastructure, though it is incremental in combining established methods.
The paper tackled bridge condition monitoring by developing a hybrid digital twin framework that uses existing traffic cameras and weather data to predict fatigue and maintenance needs, demonstrating it on a 99-year-old bridge under high traffic and harsh winter conditions with cost-effective predictive maintenance results.
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 $Ï(x,t)$ 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.