用现有摄像头和天气数据构建桥梁数字孪生，实现低成本预测性维护。
Traffic and weather driven hybrid digital twin for bridge monitoring
- 融合视觉识别、交通流模型与天气数据，实时监测桥梁状态。
- 在99年老桥上验证，可识别疲劳累积的冲击波与腐蚀风险。
- 适合需要降本增效的老旧高交通量桥梁运维团队使用。
本文提出一种混合数字孪生框架，利用现有交通摄像头和天气API实现桥梁状态监测，减少对专用传感器的依赖。该方法在服役99年的和平大桥上验证，该桥面临高交通量和严寒冬季。框架融合三路近实时数据：基于YOLOv8的桥面摄像头图像分析车辆数量、交通密度及荷载代理指标；Lighthill-Whitham-Richards（LWR）模型传播密度ρ(x,t)，检测由减速引发的冲击波，关联重复荷载与疲劳累积；天气API提供温度循环、冻融活动、降水相关腐蚀潜力及风力影响等劣化驱动因素。通过蒙特卡洛模拟量化交通-环境场景下的不确定性，随机森林模型将融合特征映射至疲劳指标与维护等级。结果表明，该框架可有效利用现有基础设施，实现对老旧、高交通量、严寒气候下桥梁的低成本预测性维护。
原文摘要 · Abstract (English)
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.
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