# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
### Methods, Properties, and Main Outcomes of Studies
#### 2.1.1 Cluster 1--Conventional Monitoring-Based Approaches to Detect Scour
Since applying the Hilbert transform to the frequency response of a linear structure regenerates the original frequency response, any distortion could be an indicator of non-linearity. The Frequency Domain Decomposition method [61] identifies very close and repeated modes. [47] stated that the approach also makes the extrapolation of dynamic parameters possible with no information about excitation in the beginning. After calculating the continuous wavelet transform matrix from raw data, singular values and singular vectors are the products of the singular value decomposition used to find damping ratios, frequencies, and mode shapes [62]. Wavelet transformations were defined as visual tools to assess signals more easily and the computation of these transformations is a prerequisite when a signal will be modified selectively or its pattern will be sought [63]. A wavelet function has zero mean, changes with a dimensionless time parameter, and is located in both time and frequency domains, while the convolution of discrete sequence with the translated and scaled status of the wavelet transformation was defined as continuous wavelet transformation [64]. The selection of the wavelet transformation is important. A decentralized modal analysis is engaging the sectors of mode shapes which are computed at distinct sensor locations [65; 66; 67]. The sensors were accelerometers in the research of [23] and mode shape amplitudes were computed using Frequency Domain Decomposition explained above. Eigenvalue analysis is performed to obtain mode shapes, amplitudes, and frequencies of the system. [58] noted that since this method includes a large computational volume in a large number of degrees of freedom systems, efficient algorithms were generated in time. Solutions for the eigenvalue problem were categorized under vector iteration, transformation, and polynomial iteration methods. All the studies listed in Table 1 have numerical models except for [49]. There were a lot of studies focused on using accelerations of the bridge to identify scour. They adopted different frequency analysis methods to interpret acceleration signals to obtain mode shapes or frequencies and finally set a scour index to identify scoured cases. In [42], the Mode Shape Ratio was applied to acceleration signals and utilized as an indicator of scour. Acceleration signals of two points on the bridge were used to gather amplitudes of mode shapes. Mode shape ratio was the ratio of these amplitudes. Energy harvesting devices were used in [43], which produce electricity from piezoelectric material's strain changes. These were used to identify scour-based frequency shifts of the bridge. [46] and [18] investigated the influence of scour at different points on eigen frequencies. To obtain mode shapes from acceleration signals of the structure, [47] and [17] used the Frequency Domain Decomposition method. Without information on the input motion, it was possible to evaluate the dynamic parameters in this method. Decentralized modal analysis enabled [23] to derive the modal amplitudes of sensors at different locations. To detect bridge pile scour, [54] utilized the change of the shape of horizontally-displaced modes and flexibility deformations of a frame-type bridge. Continuous wavelet transformation of a signal was utilized to process acceleration signals in the research of [56], [57], and [37]. By comparing responses to the features of a base wavelet, this method could detect transient changes in frequency signals [68]. To process acceleration signals, [19] proposed a closed-form model derivation. Based on instantaneous amplitudes calculated for a target bridge frequency, mode-shape squares were obtained and a damage index was computed.