Traffic and weather driven hybrid digital twin for bridge monitoring

TL;DR

This paper introduces a hybrid digital twin framework for bridge monitoring that combines traffic and weather data from existing sources to enable cost-effective, real-time condition assessment and predictive maintenance of aging bridges.

Contribution

The novel framework integrates computer vision, traffic flow modeling, weather data, and machine learning to monitor bridge health without dedicated sensors.

Findings

Effective use of existing infrastructure for bridge monitoring

Accurate prediction of fatigue indicators and maintenance needs

Robust uncertainty quantification through Monte Carlo simulations

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 $\rho(x,t)$ 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.