# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour  
Both machine learning and conventional monitoring approaches are based on measured or simulated data. However, the way of processing the input data creates the main difference between these two approaches. Conventional methods require the creation of models, whereas machine learning methods eliminate the need of knowing the relationship between input and output [29]. Even though it is possible to generate simulated signals, field (full-scale) measurements provide more realistic information. Hence, many different types of sensors have been developed to collect signals. Sensors used for signal monitoring were classified under three main categories: kinematic, ambient, and mechanical property collectors [30]. Kinematic properties are accelerations, velocities, and displacements; ambient properties include features such as temperature, wind, and soil permeability; while mechanical properties are strain, stress, and shear/bending/torsional deformations. Accelerometers, strain gauges, and displacement gauges are the most widely-used monitoring sensors. Narrowing down to sensors to monitor scour was explained under the two following main clusters [31]: the ones using devices that measure the depth and those utilizing changes in the dynamic properties of the structure. They listed depth monitoring devices as follows:  
1. Single-use devices;
2. Pulse or radar devices;
3. Fiber Bragg grating sensors;
4. Buried or data-driven equipment;
5. Sound wave appliances;
6. Electrical conductivity devices. They noted that accelerometers and tiltmeters were some of the sensors that use changes in the dynamic properties of the bridge. Bridge scour monitoring devices were visualized in Figure 1[32]. With developing technology, monitoring devices have improved and alternative systems have been generated. A Scour Monitoring Decision Framework was developed, which was based on software [33]. This framework assisted the Minnesota Department of Transportation engineers to determine the most adequate fixed scour mon itoring equipment for the specific bridge they were working on. It decreased potential issues related to the sensor chosen by the user and supplied warnings on the occasion of abnormal scour activity in the field. One of the recent experimental monitoring studies investigated the consequences of scour for bridge frequencies [34]. The outcomes of the theoretical model corresponded with the laboratory outcomes. The numerical background of another study was used to decide the location of the sensors and the limitations of the railway bridge monitoring [35]. Data collection systems include several other elements rather than sensor technology. The main elements when instrumenting a data acquisition scheme were listed as [36]:  
1. Sensors;
2. Sensor data collection topologies;
3. Wireless connection;
4. Power supply;
5. Synchronizing the data obtained from a set of sensors;
6. Environmental effects and data;
7. Collection and processing systems. The data collection system has to be adequately implemented according to the structure and service conditions. For large-scale bridges, locating, implementing, and maintaining sensors should be well planned since some parts of the structure could be inaccessible, such as the deck bottom of the suspension bridges. Although indirect monitoring techniques do not interrupt service conditions, for direct monitoring systems, service conditions are important factors to be considered. There can be time restrictions during the day or parts of the structure that cannot be closed to service for data acquisition. After obtaining data through sensors, wireless connections are used to transfer signals. Wireless sensing units were leveled up when combined with low-cost mobile devices and these units are still being developed for better software and hardware [37]. Figure 1: Scour monitoring devices - copied from [32].