# 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
A value of 0.002 for the standard deviation indicated scour and the accuracy was \(\pm\) 2.5 cm. Instability index developed by [44] was able to rapidly evaluate bridge safety conditions. A caisson-type experiment with 10 sensors was performed. These sensors were located at the center of every deck panel and the cap beam of a pier, as well as 14 velocity sensors for the pile group. Pier 1 was always in a state of instability. Before the pile started to incline too much, the instability index changed significantly relative to its original state. Their study could be used as an early warning system but was limited to the specific types of foundations tested, i.e., caissons. Moreover, [48] and [49] calculated scour height, i.e., the exposed length of the rod calculated via a time history analysis in the frequency domain voltage induced by the hydrodynamic effect. Sensors were tested in the sand surrounding each pier, in a laboratory. They observed that as exposed length increased, natural frequencies diminished. The measurements could last throughout the scour duration, and the sensors had a low cost. To improve the accuracy, [48] emphasized the need for full-scale tests and [49] pointed out the requirements for more realistic simulations in a flume. Later research by [50] compared measured and observed scour depths and showed that the sensor results were accurate. At the same time, they revealed difficulties in the implementation of sensors in real-life applications, and the debris effect could be a challenge for the proposed system. Magnetic field-based algorithms were developed for locating and tracking smart rocks moving around scour holes by [51] and [55]. [51] conducted two smart rocks--one direction-unknown and one direction-known at a bridge pier (upstream). Tracing the vertical displacements of the smart rock and monitoring the process of the peak scour depth was possible. Compared to former studies, higher accuracy, validation that considered a geomagnetic field, and calibration tests were supplied. Nevertheless, the measurement error was enhanced because of the instability of the direction-known rock during the field test. The reason was due to the effects of changing ambient magnetic field, caused by environmental factors. Picking the measurement points and stations (to locate the smart rock) close to the pier was suggested to diminish errors because the location had an intense magnetic field due to steel reinforcement. In the study of [55], real-time monitoring was performed for more accurate results. Before implementing the smart rock, magnetic field and coordinates were measured by an Unmanned Aerial Vehicle. The Unmanned Aerial Vehicle could detect the location with an error of less than 36 cm (verified by total station measurements). In the study of [52], different permittivity values corresponded to pre- and post-scour conditions. To test the application, six instrumented probes were installed in the bed segment and fixed on the flume floor. As scour developed, sensors' permittivity increased. The higher apparent permittivity is believed to be related to the reduced soil density of the re-deposited sediment around the sensor. It was an economical, realistic, and real-time monitoring method, but was not efficient in saline water. Overall, a lot of direct monitoring-based scour detection studies were focused on developing monitoring systems such as smart rocks, piezoelectric rod sensors, image-recognizing micro cameras, or smart probes instrumented with electromagnetic sensors. Sensor-based studies were advantageous since they provide direct information on scour depth. Some researchers compared their results with empirical scour formulas and showed that the formulas overestimated scour, which made the proposed sensors more economical solutions. Despite being more economical alternatives to traditional monitoring devices, a couple of issues narrowed down the application fields of newly developed sensors.