# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
### Direct Monitoring-Based Studies
The results of the study by [63] showed that bogie accelerations decreased due to a 30% scour case. Identifying a scoured pier was possible by using the difference between the wavelet coefficients of the scoured case and the healthy case. Vehicle properties such as mass and speed were considered stable. [37] detected and located scour by showing the difference between the wavelet coefficients of the scoured case and the healthy case. Vehicle signals were generated by an external numerical party. Batches of 20, 50, 100, and 200 vehicle crossings were tested for comprehending the reaction of the method. However, in a low number of batches, false-positive scour indications were seen to increase. [57] measured instant accelerations at every support and the mean value was obtained and converted into the frequency-spatial domain using 1000 vehicle passages, and operating deflection shapes were evaluated for 25 and 45% scour cases. [19] extracted mode shapes from drive-by data, using the Hilbert transform. In their experimental setup, one of eight accelerometers was located on the vehicle and seven were fixed to the bridge to gather modal parameters. Foundation scour was able to be identified only for the fifth bridge mode shape. They noted that high vehicle speed decreased the accuracy of the mode shape. Near the damage location, the damage index reached a maximum value. Nevertheless, an edge effect limited the ability to find the exact location with this approach. The effects of operational and environmental parameters were not in their scope. Finally, taking the arithmetical average of multiple runs was suggested. [18] investigated the response of lateral acceleration at the top level of a pier and a 40% frequency change was obtained due to scour. The results were only validated for the two-axle truck case. Overall, all the studies successfully detected scour by processing acceleration signals from batches of vehicle passes. Since the results of continuous wavelet transformation were in both spatial and frequency domains, locating scour and having detailed structural modal information was possible. Both wavelet transformation and operating deflection shapes methods were proven to be more reliable in locating the scour than modal analyses. It should be highlighted that when working with the continuous wavelet transformation, a low number of vehicles per batch leads to an increase in the false-positive scour indications. Generally, these types of research required fewer sensors, had a lower power requirement compared to direct monitoring, and did not require underwater inspections. These featuresmake this type of monitoring more economical than the direct type. The possibility of monitoring the bridge under regular service conditions without special monitoring devices and causing no service interruptions were additional advantages. All these properties suggest that indirect monitoring is a highly promising approach for scour investigations of bridges. Having more field tests will help verify studies with real-life applications. Studies can be expanded by considering variable vehicle properties such as speed and mass, different types of vehicles, and sensitivities to environmental conditions, and performing more detailed numerical analyses.