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Scour is a natural phenomenon in alluvial streams around hydraulic structures due to the removal of bed material and often leads to their failure. Scour due to jets are the most prevalent form of scour seen downstream of hydraulic structures. This paper presents a detailed review of scour processes with respect to the effect of various parameters on scour under different jet conditions. Different aspects of the scour hole characteristics under various applications of jets have been reviewed, which include (1) scour process; (2) the effect of parameters such as impinging height, jet impact angle, aeration, size of sediment particle, and sediment gradation; (3) flow characteristics within the scour hole; and (4) temporal variation and time taken to reach an asymptotic state of scour depth. Moreover, the experimental findings of various researchers on the development of the scour profile geometry and their applicability to large-scale conditions have been summarized. [ABSTRACT FROM AUTHOR]

The scouring process near spur dikes could jeopardize the stability of riverbanks. Thus, accurate estimation of the maximum scour depth near spur dikes is crucial in river engineering. However, due to the complexity of the scour phenomenon around these structures, it has been a challenge to accurately estimate the maximum scour depth. Few efforts have been made to develop a machine learning (ML) approach for such a purpose. In this study, two novel multilayer stacked generalization frameworks are developed to model the scour depth near a spur dike in a uniform sediment condition. Stacked tree-based frameworks consist of three standalone ML approaches, including multivariate adaptive regression spline, multigene genetic programming, and kernel extreme learning machine as the first layer and the boosting regression tree (BRT) and bagging regression tree (BGT) as meta learners. A total of 186 data points were collected from previous experimental studies, and 32 flume experiments were further conducted under clear water conditions at the Indian Institute of Technology (IIT) Roorkee Laboratory in this study. The performances of the models were assessed using various statistical metrics [e.g., correlation coefficient (R), root-mean-square error (RMSE), and mean absolute percentage error (MAPE)], graphical criteria, and some existing empirical equations. The modeling results demonstrated that the stacked BRT and BGT frameworks were superior to all the standalone ML approaches (R=0.9786 , RMSE=0.1654 , and MAPE=11.68 for BRT; and R=0.9742 , RMSE=0.1831 , and MAPE =11.29 for BGT). This finding was also confirmed by a comparison between the empirical correlations and the artificial intelligence models. In addition, the sensitivity analysis proved that the mean sediment size ratio (L/d50) was the most influential variable in estimating the scour depth near spur dikes. [ABSTRACT FROM AUTHOR]

The authors stated in the introduction section that artificial intelligence (AI) approaches, rather than empirical equations, offer a suitable platform to predict the scour depth at culvert outlets with a reasonable level of accuracy. The authors are congratulated for providing an alternative gene-expression programming, based on the evolutionary polynomial regression and a model tree approach, to predict local scour depth at culvert outlets. [Extracted from the article]

An accurate prediction of scour depth around bridge piers is crucial for economical and safe design of bridge pier foundations. The main objective of the present study is to identify the influencing cohesive parameters and their effects on the local scour processes around bridge piers, depending on various proportions of clay–sand–gravel mixtures. Twenty experimental tests were performed in a channel 25 m long and 1.0 m wide for this purpose. Runs lasted from 16 to 40 h. It was noted from the experimental work that an increment of clay fraction significantly reduces the scour depth around bridge piers. It was also observed that the initiation of scour occurred at the sides of the pier where separation of flow occurred. Typically, the maximum scour depth at the equilibrium stage was still observed at the sides of the pier. A dimensional analysis was used to propose mathematical relationships assessing the temporal scour depth variation at the wake and sides of the pier. The developed relationships yielded reasonable results with maximum error of two folds for 95.22% of total data sets for scour depth at the wake and 92.57% of the total data sets for scour depth at the sides of the pier. [ABSTRACT FROM AUTHOR]