Deep foundations are very important elements in the routine design of railways and bridges when the loads applied due to these important structures are higher than the bearing capacity of the soil. However, the methods currently available to calculate the bearing capacity of driven piles embedded in clay have been developed based on empirical factors derived from limited tests. Hence, further assessment of these methods and the development of new methods are urgently required. This paper discusses the development of a new robust model to calculate the skin friction capacity of driven piles using the multi-objective evolutionary polynomial regression (MOGA-EPR) analysis. The paper also evaluates the accuracy of the available analytical methods. Real field results of skin friction capacity of driven piles have been used to achieve the objectives of the study. The results showed that the MOGA-EPR predicts the skin friction of driven piles with an excellent accuracy and better than the available analytical methods, with a mean absolute error ($\mathit{MAE}$), a root mean square error ($\mathit{RMSE}$), mean ($\mu$), a standard deviation ($\sigma$), a coefficient of determination ($R^2$), the variance account for ($\mathit{VAF}$) and $a20-index$ of 3.4, 4.6, 1.03, 0.24, 0.98, 99, and 0.75, respectively, for the training data, and 4.2, 5.3, 1.12, 0.15, 0.91, 97 and 0.77, respectively, for testing data. In addition, a novel model to predict the skin friction capacity of driven piles has been proposed based on the MOGA-EPR analysis and this model can be used by engineers and researcher with confidence. The evaluation of the analytical methods illustrated that the Lambda method accuracy is better than the Alpha and Beta methods as this method scored a less mean error ($\mathit{MAE}$ = 7.8 and $\mathit{RMSE}$ = 12.5), a less standard deviation ($\sigma$ = 0.21), a higher coefficient of determination ($R^2$ = 0.91), and higher value for the variance account for ($\mathit{VAF}$ = 89) compared with the other analytical methods. In addition, the Beta method scored lowest compared with the other analytical methods with $\mathit{MAE}$, $\mathit{RMSE}$, $\mu$, $\sigma$, $R^2$, $\mathit{VAF}$ and $a20-index$ of 17.2, 28.0, 1.07, 1.00, 0.55, 40 and 0.37, respectively. The findings of this study will help to achieve robust calculations of pile capacity and reduce uncertainty associated with the choice of the analytical method used in the design of driven piles in clay.

当这些重要结构所施加的载荷高于土壤的承载力时，深层基础在铁路和桥梁的常规设计中是非常重要的元素。但是，基于有限试验得出的经验因素，已经开发出了当前可用于计算嵌在粘土中的打入桩的承载力的方法。因此，迫切需要对这些方法进行进一步评估并开发新方法。本文讨论了使用多目标进化多项式回归（MOGA-EPR）分析来计算打入桩的表皮摩擦能力的新鲁棒模型的开发。本文还评估了可用分析方法的准确性。打入桩的皮肤摩擦能力的实际结果已用于实现研究目的。结果表明，MOGA-EPR能够以优异的精度预测打入桩的皮肤摩擦，并且比现有的分析方法更好，平均绝对误差为（$\mathit{MAE}$），均方根误差（$\mathit{RMSE}$）， 意思 （$\mu$），标准偏差（$\sigma$），确定系数（${\mathrm{[R}}^2$），则差异账户为（$\mathit{VAF}$）和 $\mathrm{一种}20--\mathrm{一世}ñdËX$对于训练数据，分别为3.4、4.6、1.03、0.24、0.98、99和0.75，对于测试数据分别为4.2、5.3、1.12、0.15、0.91、97和0.77。此外，基于MOGA-EPR分析，提出了一种新型的预测打入桩的皮肤摩擦能力的模型，该模型可供工程师和研究人员放心使用。分析方法的评估表明，Lambda方法的准确性优于Alpha和Beta方法，因为该方法的平均误差较小（$\mathit{MAE}$ = 7.8并且 $\mathit{RMSE}$ = 12.5），标准偏差较小（$\sigma$ = 0.21），较高的确定系数（${\mathrm{[R}}^2$ = 0.91），且较高的值用于（$\mathit{VAF}$ = 89）与其他分析方法相比。此外，与其他分析方法相比，Beta方法得分最低$\mathit{MAE}$， $\mathit{RMSE}$， $\mu$， $\sigma$， ${\mathrm{[R}}^2$， $\mathit{VAF}$ 和 $\mathrm{一种}20--\mathrm{一世}ñdËX$分别为17.2、28.0、1.07、1.00、0.55、40和0.37。这项研究的结果将有助于实现对桩容量的可靠计算，并减少与在粘土中打桩设计中使用的分析方法的选择相关的不确定性。