# Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data
###### Abstract  
Throughout the lifespan of a bridge, morphological changes in the riverbed affect the variable action-imposed loads on the structure. This emphasizes the need for accurate and reliable data that can be used in model-based projections targeted for the identification of risk associated with bridge failure induced by scour. The aim of this paper is to provide an analysis of scour depth estimation on large sand-bed rivers under the clear water regime, detect the most influential (i.e., explanatory) variables, and examine the relationship between them and scour depth as a response variable. A dataset used for the analysis was obtained from the United States Geological Survey's extensive field database of local scour at bridge piers, i.e., the Pier-Scour Database (PSDB-2014). The original database was filtered to exclude the data that did not reflect large sand-bed rivers, and several influential variables were omitted by using the principal component analysis. This reduction process resulted in 10 influential variables that were used in multiple non-linear regression scour modeling (MNLR). Two MNLR models (i.e., non-dimensional and dimensional models) were prepared for scour estimation; however, the dimensional model slightly overperformed the other one. According to the Pearson correlation coefficients (\(r\)), the most influential variables for estimating scour depth were as follows: Effective pier width (\(r\) = 0.625), flow depth (\(r\) = 0.492), and critical and local velocity (\(r\) = 0.474 and \(r\) = 0.436), respectively. In the compounded hydraulic-sediment category, critical velocity had the greatest impact (i.e., the highest correlation coefficient) on scour depth in comparison to densitetric Froude and critical Froude numbers that were characterized by correlation coefficients of \(r\) = 0.427 and \(r\) = 0.323, respectively. The remaining four variables (local and critical bed shear stress, Froude number, and particle Reynolds number) exhibited a very weak correlation with scour depth, with \(r\) < 0.3.  
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Throughout the lifespan of the bridge, the riverbed morphology changes, which in turn affects the variable action-imposed loads on the structure [3] and increases the risk of failure [4]. Numerous researchers have identified scour as the most common cause of failure among all other hydraulic-related causes [5; 6; 7]. Climate change has a significant impact on bridge safety since their long life span does not allow a reliable estimation of the impact of climate change [8; 9]. The annual costs of adaptation to reduce the risk of bridge scour under projected future climate change scenarios are estimated at \(\xi\)541 million in Europe for the period 2040-2070 [10]. Therefore, potential threats to bridge safety have been identified during maintenance, which ensures their resilience [11; 12] and reduces potential direct and indirect losses [13]. Obstacles to reliable estimation of potential scour are numerous: the unknown accuracy of field data, the uncertainty of laboratory data upscaled to the prototype scale, and an overall lack of understanding of the interaction between the turbulent flow and the erodible riverbed. Scour is initiated when the flow velocity exceeds a threshold for entrainment of sediment, and it lasts until after the flood peak [14]. Thereafter, as the flood stage recedes, the scour hole refills and its depth decreases [15; 16]. As scour is the most common cause of bridge failure, there is an apparent need for accurate and reliable data that can be used in model-based projections of the risks associated with bridge scour. Most of the existing scour prediction models available in the literature are derived from laboratory data [17]. Using a pump and a tail gate in the laboratory provides controlled hydraulic conditions for experimental data collection. Establishing a specific flow environment in a laboratory limits the range of laboratory conclusions [18].