Early Scour Damage Detection Using Drive-By Monitoring Data through Supervised Learning

Early detection of damage is crucial for maintaining the safety and integrity of infrastructure. Scour at bridge foundations is particularly critical, as it poses a significant risk of collapse, which is heightened by the increasing frequency of extreme weather events such as floods. This paper introduces a supervised learning approach for early scour damage detection using drive-by monitoring data considering a small number of vehicle passages. A convolutional neural network model is employed to classify different levels of scour damage using two methodologies. The first methodology incorporates only vehicle acceleration measurements and the second methodology includes both acceleration and vehicle speed information. To handle data variability and neural network randomness, classification accuracy is evaluated through confusion matrices and boxplots. The model’s performance in classifying scour scenarios is assessed from sensors positioned on the car body and the front bogie of the first vehicle. The results show high accuracy in detecting scour for the sensor positioned in the car body, achieving approximately 100% accuracy in classifying all scenarios when vehicle speed information is included.