# 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
#### 2.1.2 Cluster 2--Machine Learning-Based Research
The tests were performed under clean water conditions on various complex piers with different geometric parameters such as plan dimensions of the pier and the piles, water depth, distance of the pile to the edge of the pile cap, flow direction, the distance between piles, and pile cap elevation [126]. Experimental setups were such adjusted that the effects of flow depth, sediment size, and contraction on piers were eliminated. Data sources of [111] were from field measurements of 14 distinct bridges in Canada, Pakistan, and India. Four different data labels were available--sharp, round, cylindrical, and square-shaped bridge piers' scour depth measurements. There were also sub-labels of this scour depth data--narrow, wide, and intermediate-sized piers' measurements. Finally, the sub-labels were grouped under five dimensionless categories of scour depth to flow depth ratio, Froude number, median diameter to flow depth ratio, pier length to flow depth ratio, standard deviation of bed size grain diameter, and pier diameter to flow depth ratio. Later in another study, [112] collected the input data from three different studies. Two of the studies performed the experiments in a four-meter-long flume and controlled the mean sediment size (0.98 mm) and pipe diameters (0.016m). The measurements were taken for various pile plan layouts (1:2, 4:1 etc.). The third study of data resource used a smaller sediment size (0.80 mm) and pipe diameters (0.06m and 0.041m) but a longer flume (46m). In another study, [109] used the same data as they used in 2017 [113]. Moreover, the data set of [117] included both scour conditions of clear-water and live-bed. The effect of the independent parameters flow depth, velocity of approach, duration of flow, median sediment size was investigated on predicting scour. There were also four different data labels of sharp-nosed, round-nosed, rectangular, and circular piers' scour depth measurements. [118] adopted field scour depth measurements gathered from 79 sites in 17 states of USA for prediction of equilibrium scour depth. They excluded scour in group-type bridges and cohesive bed material, moderate, and substantial debris effects. The parameters considered were the width and the shape factor of the pier, skew of the pier to approach flow, size of the grain in the bed, gradation of bed material, and depth and velocity of the flow. [121] used laboratory data which includes four datasets from the Hydrotech Research Institute of National Taiwan University. Complex pier foundation scour measurements were taken in a sand bed. Parameters considered were soil-covering height, median grain size, standard deviation of river bed material, radio of the mean velocity of the sediment to the critical velocity, flow depth, pier width which is perpendicular to the flow direction, the width of the pile cap, and pile cap-pier face to face distance. [124] combined two sets of field data--from bridge scour data management system (BSDMS) and FHWA documentation by USGS. Departing from the information of different direction of vortices (and therefore scour developments) in upstream and downstream, their first data label was location. Velocity and flow depth of approach, median grain size, gradation of sediment, and scour depth were other data labels. To improve the overall quality of the input, data with missing location or other properties listed above were excluded. [85] picked 232 upstream scour length measurements from another study [127] that collected scour observations in 79 different rivers several states of USA. Data included skew of the pier to approach flow, pier shape factor, pier dimensions, flow velocity and depth, grain size, and gradation of bed material. Only raw data were used to obtain a better performance. In testing the dataset, root mean square values of both neural networks in the scope of the research were compared to obtain the ideal values for various parameters defined by user.