# Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data
## 3 Results
### Comparison with Different Scour Models
The authors have retained almost all observed measurements (1249 of 1858), except for those that lack information regarding bridge pier shape. However, they have included only 4 variables: \(b\), \(y\), pier shape nose correction factor (\(K_{2}\)), and live-bed vs. clear water correction factor (\(K_{1}\)). Considering that their approach was similar to ours (the same database and the same modeling technique), their equation almost matches the dimensional model developed in this study (Equation (17)). According to Figure 5, there are similarities between the performance of our model and that of the AL model that can be assigned to the fact that in the AL model there was no restrictive data filtering, i.e., almost the whole span of measurements was considered. [PERSON] and [PERSON] [28] (RM abbreviated) developed the GEP model for scour depth estimation using the PSDB-2014 database. In order to develop a unique and universal scour equation, the authors integrated laboratory and field datasets to obtain larger variable ranges. Even though the RM model was developed using the GEP method and on the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Dataset** & **Method** & **Equation** \\ \hline Annad and Lefkir, 2022 [19] & field (PSDB-2014) & MNLR & \(d_{s}=0.318\cdot K_{1}\cdot K_{2}\cdot k_{2}\cdot k_{2}\cdot k_{2}\cdot k_{2}\) \\ \hline Rahd and 2022 [28] & field (PSDB-2014) & field (PSDB-2014) & GEP & \(d_{s}=y\left[\left(\frac{b}{r^{2}\ln\delta y}\right)+\left(\frac{b}{r^{2} \ln\delta y}\right)+\left(\frac{b}{\ln\delta y}\right)\right]\) & (12) \\ \hline [PERSON] and Jalal, 2021 [50] & numerical on a large scale & GEP & \(b\left[7.24\cdot\frac{b}{r^{2}}-\left(\frac{b}{r}\right)^{2}\right]\) & \(\frac{b}{r^{2}}+\frac{b}{r^{2}}+\frac{b}{r^{2}}-\frac{b}{r^{2}}+\left(\frac{b} {r^{2}}\right)^{2}\) & (13) \\ \hline [PERSON] and [PERSON], 1979 [38] & field and laboratory & conventional & \(d_{s}=184\cdot b\left(\frac{b}{r^{2}}\right)^{0.3}(F_{tr})^{0.25}\) & (14) \\ \hline [PERSON] et al., 2010 [37] & field & MNLR & \(d_{s}=1.82\cdot y\left(\frac{c_{0}}{r}\right)^{0.062\cdot\left(\frac{b}{r} \  
combined nature of the data (lab and field), the model has a similar performance in comparison to the model developed in this study, probably due to the application of the PSDB-2014 database. Assuming that the type of technique selected for model development does not affect scour prediction performance as much as the filtering of the dataset, the performances of the RM and AL models are expected to be very close, as shown in Figure 5.  
[PERSON] and [PERSON] [50] (HJ abbreviated) numerically simulated scour around the bridge pier on a real scale to evaluate the performance of predicting scour depth with the GEP model. They used a numerical dataset consisting of 243 observations collected in a clear-water regime and calibrated with the results from the Melville laboratory model [51]. The authors carried out a sensitivity analysis of influential variables and showed that \(y\) has the greatest influence on the predicted \(d_{s}\), followed by the ratio of velocities \(v/v_{c}\), the ratio of pier width \(B/b\), the pier Froude number \(Fr_{pir}\), and finally the pier shape factor \(K_{s}\) with the weakest influence. According to Figure 5, the HJ model tends to underpredict scour depth. Unfortunately, only ranges of nondimensional variables are given in their study, so the real span of variable values remains unknown. However, it can be assumed that [PERSON] and [PERSON] [50] collected numerical data in terms of lower water depths since flow depth turned out to be the most significant variable. This assumption is supported by the work of [PERSON] and [PERSON] [52], as they claim that flow depth does not play an important role in scour when the \(y/b_{\phi}\) ratio is above 2.6. The final difference is that they have taken into account the ratio of pier width \(B/b\), so it can be assumed that contraction scour has a more important role in their dataset.