Shear design of RC beams with and without stirrups using laboratory experiments is difficult or even impossible as a large number of variables need to be considered simultaneously, such as the span-to-depth ratio, web width and reinforcement ratio. In addition, due to the complex shear failure mechanism, empirical approaches for shear design are derived within the boundaries of their own testing regimes. Thus, the generalization ability and applicability of these approaches are limited. To address this issue, this study uses machine learning approaches for shear design. A random forest model is constructed to predict the shear strength of RC beams. The hyperparameters of RF are tuned using beetle antennae search algorithm modified by Levy flight and inertia weight. The developed model is trained on two data sets of RC beams with and without stirrups containing 194 and 1849 samples, respectively. The obtained model has high prediction accuracy with correlation coefficients of 0.9367 and 0.9424 on these two test data sets, respectively. The proposed method is powerful and efficient in shear design of RC beams with and without stirrups and therefore paves the way to intelligent construction.

中文翻译：

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带箍筋和不带箍筋钢筋混凝土梁的机器学习辅助抗剪强度预测

使用实验室试验对带箍筋和不带箍筋的钢筋混凝土梁进行剪力设计是困难的，甚至是不可能的，因为需要同时考虑大量变量，例如跨深比、腹板宽度和配筋率。此外，由于复杂的剪切破坏机制，剪切设计的经验方法是在它们自己的测试范围内得出的。因此，这些方法的泛化能力和适用性是有限的。为了解决这个问题，本研究使用机器学习方法进行剪切设计。构建随机森林模型来预测钢筋混凝土梁的抗剪强度。使用由 Levy 飞行和惯性权重修改的甲虫天线搜索算法调整 RF 的超参数。开发的模型在两个分别包含 194 和 1849 个样本的带箍筋和不带箍筋的 RC 梁数据集上进行训练。得到的模型具有较高的预测精度，在这两个测试数据集上的相关系数分别为0.9367和0.9424。所提出的方法在带箍筋和不带箍筋的钢筋混凝土梁的剪切设计中是强大而有效的，因此为智能施工铺平了道路。