# Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data
###### Abstract
Furthermore, upscaling to field prototype bridge piers is a daunting task that can lead to unreliable extrapolated data. It is fair to say that laboratory-based scour models are adequate only for the limited range of variables for which they were developed. With the aim of avoiding extrapolation issues, the accuracy of these models should be additionally confirmed with field measurements. Therefore, it is necessary to gain a perspective on scour potential under field conditions [19]. Regardless of the data collection method, most of the scour prediction equations are based on conventional regression methods to provide explicit expressions that are easy-to-use and readily applicable to practice. However, conventional methods are limited to conditions similar to those under which the data were collected. This leads to biased results that often overestimate scour depth, i.e., a so-called conservative approach [20; 21]. To describe the scour process, the scour database should be extensive and structured because many local variables are required to capture the complex flow environment in the bridge opening. Recently, artificial intelligence (AI) has become a prominent tool that has the capability to capture complex scour processes governed by a large number of influencing variables. Recent studies have used AI techniques for scour prediction. [PERSON] et al. [22] developed four machine-learning models: a back propagation neural network (BP NN), a genetic algorithm neural network (GA NN), a convolutional neural network (CNN), and a deep belief network (DBN NN) to predict local scour based on a combination of laboratory tests and field observations. [PERSON] et al. [23] developed scour equations using a genetic algorithm (GA) on a large experimental dataset. [PERSON] [24] applied the adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) methods for pier scour assessment. [PERSON] et al. [25] implemented gene expression programming (GEP) to derive a new equation for pier scour estimation in a bed of a mixture of clay and sand. To evaluate the performances of developed AI models, the abovementioned authors conducted comparisons with traditional methods (regression technique, dimensional analysis, and functional relations) and concluded that AI methods showed better scour depth prediction. Although AI-based models have proven to be more accurate than conventional models, they have a tendency to overfit data. Generally, AI models learn to fit the selected data set as closely as possible, but learning the model for a particular data set might make it inappropriate for unseen data. Moreover, in the case of insufficient scouring of input data, it might occur that the AI model has a biased outcome due to overfitting issues. The United States Geological Survey (USGS) has published an extensive field database of local scour at bridge piers, \"A Pier-Scour Database\" [26], collected for 270 bridges spanning over 433 rivers in 29 countries, resulting in a total of 1858 measurements of flow, bridge geometry, and river morphology. The database is structured as a digital spreadsheet with measurements organized as rows and values of variables under each measurement organized in columns, e.g., scour depth around bridge piers (\(d_{s}\)), pier width (\(b\)), pier length (\(l\)), angle of flow attack (\(\theta\)), effective pier width (\(b_{\theta f}\)), pier type and nose shape, approach flow depth (\(y\)), approach flow velocity (\(v\)), bed material type, median sediment size (\(d_{50}\)), geometric standard deviation of sediment particles (\(\sigma_{\mathrm{g}}\)), approximate recurrence interval for measured flow rate (\(RI\)), stream slope (\(S\)), date, type, time, and location of measurements, etc. As this extensive PSDB-2014 database has been available since 2014, it has been used in several studies focusing on pier scour. [PERSON] and [PERSON] [18] aimed to define the maximum scour depth in South Carolina by developing an envelope curve.