# 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 implementation of the developed sensors was problematic; some devices could not be used after a major flood or regular maintenance was required. This might prevent the device from being an economical solution in the long-term. Durability problems might be overcome by improving the mechanical properties of the materials used to build the sensor. Another issue was that some devices were proven not to be effective in taking accurate measurements. The studies using the latest technology like unmanned aerial vehicles equipped with three-axis high-resolution GPS units were promising. However, the location errors need to be reduced. Developed sensors should be tested in the field to provide more robust information about their outcomes. Indirect Monitoring-Based Studies  
This section includes only indirect-monitoring-based studies listed in Table 1. Vehicle/bridge dynamic interaction models were used to test the drive-by or indirect monitoring concept. Vehicles were generally modeled as half or quarter cars, representing one or two axles of a vehicle. The quarter car was particularly unrealistic but its simplicity had value in that it kept the focus on the bridge structure and did not require assumptions on axle spacing and other vehicle properties. It should be noted that the half-car was also simplistic but did serve to introduce some inter-axle effects that have a significant influence. The studies addressed in the scope of this section processed the acceleration signals due to multiple train passes. [56] used a dynamic model that considered vehicle bridge interactions; a quarter car model of the train and two simple supported bridges with a shared pier. Zero mean base wavelet was chosen as \"Mexican Hat\" and a set of wavelets were derived from this base by modifying it with position and scale parameters. Coefficients are results of continuous wavelet transformation and these relate the analyzing wavelet with the specific region in the signal--they give signs of specific frequencies that are valid in the signal for a given time point. The existence of scour was proven by finding the difference in wavelet coefficients between healthy and scoured cases. The dynamic model of [19] consisted of a simple vehicle and a simple supported Euler-Bernoulli beam representing the bridge. Acceleration of the vehicle was obtained using equations of motion and Hilbert transform was applied to accelerations to find bridge mode shapes that were already known to be sensitive to the damping of the structure. [19] investigated the effect of damping on the mode shape and degree and rearranged the formula that calculates mode shape squares using the amplitude of the Hilbert transform. The difference between scoured and healthy cases' mode shapes was the scour indicator. The researchers also verified their algorithm numerically with two laboratory experiments. [37] and [57] also used wavelet coefficients. The dynamic model of [37] first analyzed the vehicle and then computed beam local and global mass and stiffness matrices using finite element method. The forces were distributed to the degree of freedoms using shape functions which considered the location of the vehicle that changed every time step. The chosen base was Morlet wavelet. Scoured case was simulated in the model by reducing the stiffness of the bridge pier. For each of the 10, 20, and 30% of scour cases, 200 vehicle runs were generated in the dynamic model. Continuous wavelet transformation was applied to accelerations of both healthy and scoured cases and the differences of the coefficients indicated scour. [18] generated a vehicle-bridge-soil interaction model. The horizontal accelerations due to passing vehicle were recorded from the top of the pier. They extracted mode shapes out of acceleration measurements through an Eigen value analysis of a highway bridge. Three different levels of soil stiffness were considered to represent dense, medium-dense, and loose sand. For all types of soil, lateral frequencies were calculated for zero and 10-m scour.