# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
### Synthesis of the Results
#### 2.2.1 Cluster 1--Synthesis of Conventional Monitoring-Based Studies to Detect Scour  
First, sensors used for direct monitoring have been improved with developing technology. The device types were not only limited to inclinometers and velocity sensors, but also micro cameras, wireless transmitters, fiber-optic sensors, and even Unmanned Aerial Vehicle-deployed smart rocks. Studies with alternative sensing mechanism sensors in this review processed the signals gathered via bed-level image recognition, instantaneous frequencies, distribution of magnetic field, and porosity of the soil. Although these types of devices provided direct information, and real-time monitoring of scour and eliminated the need for underwater inspections, monitoring also involved some challenges. These were implementation difficulties and maintenance requirements that might be overcome through the better design of the devices. Additionally, debris and saline water were challenges in some cases. Direct monitoring-based studies which use traditional sensors such as accelerometers and global positioning system devices were able to detect the presence of scour through changes in frequency and mode shape. These occurred due to the decrement in the foundation stiffness, as a consequence of scour. Direct monitoring-based studies were advantageous since they did not require knowledge of vehicle forces, or detailed geometrical and mechanical information about the bridge. Alternatively, energy harvesting devices are being increasingly utilized as monitoring sensors. They can detect frequency changes and produce energy out of this vibration at the same time. They have clear advantages over regular accelerometers in terms of the source of the energy. However, there were limitations of loading sensitivities and possible errors in the presence of multiple scoured piers. Second, it was possible to identify and locate different percentages of scour by processing the vehicle signals. In these studies, (i.e., indirect monitoring-based research) signals due to multiple vehicle passages were processed to obtain mode shapes or frequencies, or scour was indicated by the differences between operating deflection shapes or continuous wavelet transform coefficients between healthy and scoured cases. Mode shape ratio methods were used in many studies and were able to detect scour successfully. Very significant changes in the mode shape ratios were observed for the scoured stages. Yet, the sensitivities due to vehicle load and random errors due to the variability of the ambient loading are issues that needed to be resolved. Specifically, the mean normalized mode shape method of [63] was successful in detecting scour at a single pier. However, it was not an efficient solution when multiple piers were scoured at the same time. Investigating multiple scours of bridge piers through numerical methods could be a future study. Furthermore, frequency domain decomposition was a frequently applied method to process acceleration signals and can be applied to gather modal properties and frequencies of the bridge. Hilbert Huang transform was another signal analysis method proven to be efficient in decomposing acceleration signals to mode functions. However, due to the very short duration of the data segment, there is insufficient data. Scour detection based on mode shape methods appeared to be more robust. Some of the leading advantages of these indirect monitoring are the need for fewer sensors, eliminating the need for special monitoring devices, underwater examinations, and the occasional challenges of placing sensors. Furthermore, the service conditions of the infrastructure were not interrupted and the instrumenting vehicle was a more economical option than direct monitoring. On the other hand, the most common limitations were changing vehicle parameters (such as mass and speed) and the need for large numbers of vehicle runs.