Complex vortex flow patterns around bridge piers, especially during floods, cause scour process that can result in the failure of foundations. Abutment scour is a complex three-dimensional phenomenon that is difficult to predict especially with traditional formulas obtained using empirical approaches such as regressions. This paper presents a test of a standalone Kstar model with five novel hybrid algorithm of bagging (BA-Kstar), dagging (DA-Kstar), random committee (RC-Kstar), random subspace (RS-Kstar), and weighted instance handler wrapper (WIHW-Kstar) to predict scour depth (d_s) for clear water condition. The dataset consists of 99 scour depth data from flume experiments (Dey and Barbhuiya, 2005) using abutment shapes such as vertical, semicircular and 45° wing. Four dimensionless parameter of relative flow depth (h/l), excess abutment Froude number (Fe), relative sediment size (d₅₀/l) and relative submergence (d₅₀/h) were considered for the prediction of relative scour depth (d_s/l). A portion of the dataset was used for the calibration (70%), and the remaining used for model validation. Pearson correlation coefficients helped deciding relevance of the input parameters combination and finally four different combinations of input parameters were used. The performance of the models was assessed visually and with quantitative metrics. Overall, the best input combination for vertical abutment shape is the combination of F_e, d₅₀/l and h/l, while for semicircular and 45° wing the combination of the Fe and d₅₀/l is the most effective input parameter combination. Our results show that incorporating Fe, d₅₀/l and h/l lead to higher performance while involving d₅₀/h reduced the models prediction power for vertical abutment shape and for semicircular and 45° wing involving h/l and d₅₀/h lead to more error. The WIHW-Kstar provided the highest performance in scour depth prediction around vertical abutment shape while RC-Kstar model outperform of other models for scour depth prediction around semicircular and 45° wing.

桥墩周围的复杂涡流模式，尤其是在洪水期间，会导致冲刷过程，从而导致地基破坏。基台冲刷是一个复杂的三维现象，尤其是使用经验方法（例如回归）获得的传统公式很难预测。本文介绍了一个独立的Kstar模型的测试，该模型具有袋装（BA-Kstar），拖曳（DA-Kstar），随机委员会（RC-Kstar），随机子空间（RS-Kstar）和加权实例处理程序的五种新颖混合算法包装器（WIHW-Kstar）来预测冲刷深度（d _s），以确保水质清澈。该数据集包含来自水槽实验的99个冲刷深度数据（Dey和Barbhuiya，2005年），使用的是基台形状，例如垂直，半圆形和45°机翼。为预测相对冲刷深度（d），考虑了相对流深度（h / l），桥台富德数（Fe），相对沉积物尺寸（d ₅₀ / l）和相对淹没（d ₅₀ / h）的四个无因次参数。_小号/升）。数据集的一部分用于校准（70％），其余部分用于模型验证。皮尔逊相关系数有助于确定输入参数组合的相关性，最后使用四种不同的输入参数组合。通过视觉和定量指标评估模型的性能。总体而言，垂直基台形状的最佳输入组合是F _e，d ₅₀ / l和h / l的组合，而对于半圆形和45°机翼，Fe和d ₅₀ / l的组合是最有效的输入参数组合。我们的结果表明，将Fe，d ₅₀ / l和h / l结合使用可提高性能，而涉及d ₅₀ / h则降低了垂直基台形状以及半圆形和45°机翼涉及h / l和d ₅₀ / h的模型预测能力导致更多错误。WIHW-Kstar在垂直基台形状周围的冲刷深度预测中提供了最高的性能，而RC-Kstar模型在半圆和45°机翼周围的冲刷深度预测方面优于其他模型。