A short-term hyperlocal meteorological forecast tool for mitigating bridge risks

A short-term hyperlocal meteorological forecast is an important tool for mitigating bridge risks as it empowers bridge operators and maintenance teams with timely and accurate weather predictions. Accurate predictions enable proactive responses to adverse conditions. Bridges are particularly vulnerable to extreme weather events, which can compromise their structural integrity and pose significant hazards. For instance, heavy rain can lead to flooding and erosion around bridge foundations, high winds can impose dynamic loads that strain the bridge’s superstructure, freezing temperatures can cause ice accumulation and expansion within concrete or steel components, and fog can severely limit visibility for drivers, increasing the likelihood of accidents. This study presents a short-term hyperlocal meteorological forecast framework designed to assist in the real-time decision-making processes on the bridge. The proposed framework leverages cutting-edge machine learning algorithms, integrating a blend of local and global sensor systems to continuously collect real-time data. The data also forms a comprehensive dataset for the training process. The capacity of the framework to provide real-time weather insights for one geographic location is evaluated. In this context, this study critically examines one potential application of the proposed framework in predicting wind gusts, scrutinizing its versatility and efficacy in addressing diverse weather-related challenges faced by bridge management. Furthermore, the potential of the framework to enhance the resilience of transportation networks by minimizing disruptions and structural damage caused by adverse weather events is discussed and quantified.