Over-height vehicle impact severity assessment for through-plate girder railroad bridges

Abstract Low clearance railroad bridges in the United States are prone to frequent impacts from over-height vehicles, which can lead to structural damage and disruptions in railroad bridge service. Current practice mandates the closure of bridges after an impact is reported. However, this approach results in traffic delays and loss of revenue for railroad owners in cases of minor impacts, which occur far more frequently than major ones. While researchers have developed approaches to detect such impacts using acceleration responses obtained from sensors mounted on the bridge, the severity is generally difficult to ascertain. Therefore, there is a need for a reliable method to assess both the occurrence and severity of impacts. Although previous studies have used permanent displacement thresholds to rate impact severity, measuring permanent displacement is challenging and too expensive to be scalable. To address these challenges, this study proposes the use of an artificial neural network model to assess impact severity based on the impact impulse, peak acceleration, and spectral energy of detected impacts. The performance of the model is further validated using both simulated and field-collected impact data from a through-plate girder railroad bridge in northern Illinois. The results demonstrate that the developed approach reliably detects and determines the severity of impacts. This solution allows railroad owners to better prioritize the allocation of limited resources towards the inspection of bridges after major impacts.