# 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  
This section briefly introduces commonly used machine learning algorithms. The introduction is followed by detailed information about the research noted in Table 3; characteristics of the input data used for training or validating, main and assisting algorithms used, and the scour property sought. Studies were mainly focused on estimation of the scour depth around the piers using machine learning algorithms. The characteristics of black-box models and their prediction capabilities enabled machine learning algorithms very handy tools for scour --damage formed by various parameters which require detailed investigations. Introducing the basics of some of the commonly-used machine learning algorithms and the optimization algorithms for hyperparameter selection might ease understanding the studies given in this section. Before training the main algorithm, the majority of the machine learning algorithms specify a group of hyperparameters, whose determination could remarkably affect the efficiency of the resultant model [69]. Optimization algorithms are also used for assisting the search for the value of a parameter that is involved in two different predictions/calculations for the same structure or for searching the optimal hidden neuron number. Gradient-based and heuristic methods are two main structural optimization techniques [70]; in gradient-based ones, the direction of the search, i.e., the gradient, has to be described before seeking the best solution. However, gradient-based approaches might get stuck the in local optimum before they access the global optimum, execute insufficient in coping with the structures with nonlinear, hidden, and implement constraints, and a few of them include detailed optimization constraints [71; 72]. Therefore, for overcoming such limitations in complex problems, heuristic methods were introduced. By solving actual problems of life simply, quickly, and in an applicable and efficient way, the utilization, requirement, and prestige of heuristic techniques have quickly boosted [70]. Some of the well-known heuristic optimization methods applied to civil engineering problems are the genetic algorithm, ant colony algorithm, particle swarm optimization, and simulated annealing [73]. First, because of being simple and easy to work with and having minimum requirements, genetic algorithms have been preferred extensively to solve numerous problems by imitating basic rules of evolution and natural genetic science [74]. These algorithms can solve the problem quickly and are handy for extensive problems, nevertheless there is no guarantee for the best solution in the end of the analysis [75]. Secondly, art colony algorithms are the most frequently utilized to solve NP-hard problems: for these kinds of problems, well-known algorithms assuring to detect an optimal result contain worst case scenario case convolution [76]. Art colony algorithms are used to optimize construction time and cost [77], task scheduling [78], and design problems [79]. Furthermore, in particle swarm optimization, the current location of every particle is updated by a vector of velocity, according to the social attitudes of individuals; when the swarm readjusts itself to the ambient by reappearing in the advantageous areas that were explored before [80]. An artificial neural network includes a couple of units of neurons (or layers, joints), one to three inexplicit (hidden) neuron layers, and a last unit of resultant neurons [81]. These algorithms do not need to go through lots of statistical training; however, they have some limitations such as a high volume of network training calculation, and the necessity of a preprocess for independent variables or predictor [82]. Still, they can help reduce the uncertainties of the problem. They eliminate the need for a clear description of the physical links between bridge scour and numerous affects and they provide better results than empirical formulations owing to small errors and correlation coefficients [22].