# 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 loading sensitivity to vehicle bridge interaction responses, random errors of ambient loading were main limitations of the study. Moreover, applying the developed approach to abutment by using the first mode was not possible. The approach was extended to a three-span bridge but a steady trend could not be obtained. In another study of the author, [46] noted that as the scour got more severe, the mean-normalized mode shape values at piers decrease. For example, pier 1 changed from 0.62 to 0.35 when 30% scour was simulated at pier 3. The experimental setup of the study had accelerometers located at two of the piers and the midspan of the bridge model, and 24.5% and 44.9% scour were observed. Comparedwith the healthy case, mean-normalized mode shape increased at the scoured pier while it decreased in the other piers. Their work eliminated the need for knowledge of vehicle excitation forces, material, and geometrical information of the bridge. However, when multiple piers had scour at the same time, the approach was not successful. Another numerical study by [31] showed that the permittivity and porosity of the soil increased when scour was present. The sensors were installed along four-meter-long scour probes in the experimental setup. One probe was for measuring the total scour at one pier while the other one was measuring constriction scour at the middle of the channel. A length of 30 cm scour was measured by the probe in the middle of the channel after a peak flood. When the empirical scour formulas and the experimental scour depth were compared, overestimation of formulas was revealed. Additionally, if the scour hole was not uniform or located at a very specific point, they suggested that sensor response and the effects of suspended sediments on the values of dielectric permittivity should be examined in a laboratory. In the research of [53], a 30% reduction in the values of the first three natural frequencies was observed compared to the scoured case. The renewable energy sources of the monitoring devices (wind, heat, electromagnetic field, and light) were a virtue of this study. However, the approach was only validated for particular structural types. Furthermore, [54] showed that vertical mode shapes were not sensitive to scour, and increasing pile heights resulted in a decrease in flexural stiffness and horizontally-displaced mode frequencies for four scour cases relative to the healthy case. The experimental model had 11 uniformly distributed unidirectional Integrated Electronics Piezoelectric accelerometers. The authors concluded that in theory, the modal curvature changes and bending deflections could be used to detect scour because of their low sensitivity to the irregularity of the obtained mode shapes. However, the method was not reliable enough to locate the scour in practice. [23] found that 25% and 45% of scour decreased the stiffness of the foundation at the central and the right-side piers and reduced mode amplitudes locally and globally. Additionally, the average change in the central pier was 7.2% and 16.8%, respectively. They used seven re-deployable accelerometers: four at the midspan and three at the piers in their laboratory test setup. They found reductions in the mode amplitudes at pier locations and an increase in the amplitudes of non-scoured piers. As foundation stiffness decreased, the difference in the root means square between healthy and scoured modes increased linearly. Their study did not require previous information about the structural behavior and used only two sensors that were sequentially moved along a bridge to detect mode shapes. However, the study was limited to one-dimensional models and simplified experiments. [45] managed to locate and measure scour by adopting the UWFBG technique that was able to sense changes in the wavelength of 0.05-0.06 nm. This change is related to strain changes of the fibers using an empirical formula. The effects of water buoyancy and loosening sand on signals were easily excluded.