Failure mode (FM) and bearing capacity of reinforced concrete (RC) columns are key concerns in structural design and/or performance assessment procedures. The failure types, i.e., flexure, shear, or mix of the above two, will greatly affect the capacity and ductility of the structure. Meanwhile, the design methodologies for structures of different failure types will be totally different. Therefore, developing efficient and reliable methods to identify the FM and predict the corresponding capacity is of special importance for structural design/assessment management. In this paper, an intelligent approach is presented for FM classification and bearing capacity prediction of RC columns based on the ensemble machine learning techniques. The most typical ensemble learning method, adaptive boosting (AdaBoost) algorithm, is adopted for both classification and regression (prediction) problems. Totally 254 cyclic loading tests of RC columns are collected. The geometric dimensions, reinforcing details, material properties are set as the input variables, while the failure types (for classification problem) and peak capacity forces (for regression problem) are set as the output variables. The results indicate that the model generated by the AdaBoost learning algorithm has a very high accuracy for both FM classification (accuracy = 0.96) and capacity prediction (R² = 0.98). Different learning algorithms are also compared and the results show that ensemble learning (especially AdaBoost) has better performance than single learning. In addition, the bearing capacity predicted by the AdaBoost is also compared to that by the empirical formulas provided by the design codes, which shows an obvious superior of the proposed method. In summary, the machine learning technique, especially the ensemble learning, can provide an alternate to the conventional mechanics-driven models in structural design in this big data time.

失效模式（FM）和钢筋混凝土（RC）柱的承载力是结构设计和/或性能评估程序中的关键问题。破坏类型，即挠曲，剪切或以上两种的混合，将极大地影响结构的承载力和延展性。同时，不同故障类型的结构的设计方法将完全不同。因此，开发有效，可靠的方法来识别FM并预测相应的能力对于结构设计/评估管理尤为重要。本文提出了一种基于整体机器学习技术的FM分类和RC柱承载力预测的智能方法。最典型的整体学习方法是自适应增强（AdaBoost）算法，用于分类和回归（预测）问题。总共进行了254次RC柱的循环载荷测试。几何尺寸，补强细节，材料特性设置为输入变量，而破坏类型（用于分类问题）和峰值承载力（用于回归问题）设置为输出变量。结果表明，由AdaBoost学习算法生成的模型对于FM分类（精度= 0.96）和容量预测（R 而故障类型（用于分类问题）和峰值容量力（用于回归问题）被设置为输出变量。结果表明，由AdaBoost学习算法生成的模型对于FM分类（精度= 0.96）和容量预测（R 而故障类型（用于分类问题）和峰值容量力（用于回归问题）被设置为输出变量。结果表明，由AdaBoost学习算法生成的模型对于FM分类（准确性= 0.96）和容量预测（R²  = 0.98）。还比较了不同的学习算法，结果表明，集成学习（尤其是AdaBoost）比单个学习具有更好的性能。此外，还将AdaBoost预测的承载能力与设计规范提供的经验公式进行了比较，这表明了该方法的明显优越性。总之，在这种大数据时代，机器学习技术（尤其是集成学习）可以为结构设计中的传统机械驱动模型提供替代方法。