# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
###### Abstract
These studies did not aim to obtain scour depth but rather to detect its presence by identifying changes in the dynamic properties of the structure as an index that reveals the existence of scour. Alternatively, to evaluate the performance of scour-critical bridges, a soil-pile-structure interaction analysis was performed [20]. Some researchers have validated their approaches with experiments, [21; 22; 23] and therefore gathered more trusted results. Many studies with scour indexes were explained in detail in Section 2.1.1 with their approaches, model features, and outcomes. Finally, there are probabilistic approaches that overcome the uncertainties of deterministic approaches summarized above. Uncertainties of scour identification problems include insufficiencies of visual inspections, assumptions, and estimations of parameters (geometric properties, hydraulic parameters, etc.). Since the accuracy of the deterministic models is highly dependent on the input, these might lead to over or underestimation of scour. Probabilistic scour models investigate the probability of structural failure under scour [24; 25; 26; 27; 28]. The limit state function is defined as the residual resistance of the structure after load effects are compensated; where load, resistance, and therefore limit state function are dependent on random variables [28]. To solve the probabilistic failure problem, there are different types of reliability methods, for example risk-based, numerical, approximation, and semi-probabilistic designs [28]. Changing environmental conditions such as climate change effects might invalidate initial assumptions and cause unexpected rainfall regimes, floods, and therefore scour issues. The effects of such uncertainty, i.e., gradual change of flow properties was investigated by [24] and probabilistic scour failure analysis was performed. The researchers statistically analyzed the maximum annual flow of a UK river and aimed to predict local pier scour with Monte Carlo simulation. Simultaneous gradual increments of flow variability and the mean of the flow resulted in the greatest scour depth predictions. Another study investigated scour around a complex bridge pier and adopted a reliability analysis with an optimization method with a semi-probabilistic design [25]. Using this method, these researchers developed a safety factor to reach the desired safety level of the foundation design. Additionally, their sensitivity analysis revealed that pier width and correction factor were the most effective variables on the reliability of bridge pier against scour. A very remarkable uncertainty is caused by the memory effects, i.e., the accumulated scouring effects throughout the long service life of bridges [26]. Using a Markovian approach, scour depth changes aimed to be observed and scour depth domain was decomposed into multiple discrete states for various flood events. The research showed that after the first years of prediction, in time, the probabilistic scour depth distribution evolved and reached a stationary value [26]. The probabilistic local scour analysis of a bridge pier in a clay and sand mixture sediment was performed in five different reliability methods [27]. These simulation-based methods were line sampling, subset simulation, importance sampling, Monte Carlo simulation, and directional simulation. The most accurate results were obtained through subset simulation.