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.

摘要

准确确定桥墩周围的冲刷深度 ( _ds )是桥墩基础安全经济设计中的一个主要问题和基本标准。由于桥墩周围 3D 流动的机制非常复杂，传统的经验方法_难以估计ds 。本文提出了用于局部码头冲刷深度估计的顺序最小优化回归 (SMOREG) 方法。此外，还开发了梯度提升 (GBM)、K-最近邻 (K-NN) 和随机森林 (RF) 方法来比较 SMOREG 的统计性能。来自文献的众多可靠数据库包括六个输入参数，例如墩宽（b），墩长（l)、桥墩接近流量的倾斜度 (θ)、平均速度 ( v )、流动深度 ( y )、50% 的床材料的粒径 ( D ₅₀ )以及输出参数ds _。它揭示了 SMOREG 可以在d _s和流动特性之间建立关系，并提供R值为 0.85 和平均绝对误差 (MAE) 为 0.35 的估计值。本研究中开发的模型之间的比较表明，在 RMSE、 R之间的同步评估方面，SMOREG 和 RF 比 GBM 和 K-NN 具有更高的预测性能和标准差。还进行了敏感性分析以确定每个输入参数在d _s估计中的效率。发现桥墩宽度和平均流速是估计ds_的最有效参数。灵敏度的 SMOREG 模型产生的 MAE 值在 0.34-0.39 范围内。