Sequential minimal optimization for local scour around bridge piers

Abstract

Accurate determination of scour depth (d_s) around bridge piers is a major concern and an essential criterion in the safe and economical design of bridge pier foundation. The estimation of d_s by the conventional empirical methods is difficult due to the very complex mechanism of the 3D flow around the bridge piers. This paper proposes the Sequential Minimal Optimization Regression (SMOREG) approach for local pier scour depth estimation. Additionally, Gradient Boosted (GBM), K-Nearest Neighbors (K-NN), and Random Forest (RF) methods were developed to compare the statistical performance of SMOREG. The numerous reliable databases from the literature includes six input parameters such as pier width (b), pier length (l), skew of the pier to approach flow (θ), mean velocity (v), flow depth (y), the particle size for which 50 percent of the bed material (D₅₀), and an output parameter d_s. It revealed that the SMOREG can build a relationship between d_s and flow characteristics and provides an estimation with an R-value of 0.85 and a mean absolute error (MAE) of 0.35. The comparison between models developed in this study showed that SMOREG and RF gave higher prediction performance than GBM and K-NN with respect to synchronic evaluation between RMSE, R, and Standard Deviation. The sensitivity analysis were also performed to determine the efficiency of each input parameter in the estimation of d_s. It is found that pier width and mean velocity of the flow are the most effective parameters than the other parameters to estimate d_s. The SMOREG models for sensitivity yielded MAE values in the range of 0.34–0.39.

Keywords:

• Bridge pier
• gradient boosted method
• scour depth
• sensitivity analysis
• SMOREG

Disclosure statement

No potential conflict of interest was reported by the author(s).