Proper functioning and safety of anchor elements are decisive for the overall performance of a structural system. A possible failure mode for anchor loaded in shear is the concrete breakout failure. Concrete related failure mode poses a significant safety issue, since they may develop abruptly/brittle, without preceding signs of damage. Consequently, accurate prediction of the concrete breakout strength of anchors in shear is crucial. This study proposes two machine learning models — a Gaussian process regression (GPR) and a support vector machine (SVM) model — for predicting the concrete breakout capacity of single anchors in shear. To this end, experimental strength of 366 tests on single anchors with concrete edge breakout failures were collected from literature to establish the experimental database to train and test the models. 70% of the data were used for the model training, and the rest were used for the model testing. Shear influence factors such as the concrete strength, the anchor diameter, the embedment depth (technically the influence length), and the concrete edge distance were taken as the model input variables. The generated GPR and SVM prediction models yielded a determination coefficient R² = 0.99 for both the training and testing data sets. Predictions from the developed models were compared to that of the other existing models (Eurocode 2, ACI 318 and Grosser) to validate their performance. The developed GPR and SVM models provided a better prediction of the experimentally observed shear strength, compared to the existing models. The predictions obtained from the GPR model are the most accurate, yielding a value of 5.6 mean absolute error when tested.

锚固元件的正常功能和安全性对于结构系统的整体性能至关重要。剪切力作用下锚固的一种可能的破坏模式是混凝土的破裂破坏。与混凝土有关的失效模式带来了重大的安全问题，因为它们可能突然/脆化发展，而没有事先的损坏迹象。因此，准确预测锚固件在剪切中的混凝土抗折强度至关重要。这项研究提出了两个机器学习模型-高斯过程回归（GPR）模型和支持向量机（SVM）模型-用于预测剪切中单个锚的混凝土破坏能力。为此，从文献中收集了对具有混凝土边缘断裂破坏的单锚进行366次试验的实验强度，以建立用于训练和测试模型的实验数据库。70％的数据用于模型训练，其余数据用于模型测试。将混凝土强度，锚固直径，埋深（技术上为影响长度）和混凝土边距等剪切影响因素作为模型输入变量。生成的GPR和SVM预测模型得出确定系数 对于训练和测试数据集，R ² = 0.99。将已开发模型的预测与其他现有模型（Eurocode 2，ACI 318和Grosser）的预测进行比较，以验证其性能。与现有模型相比，已开发的GPR和SVM模型可以更好地预测实验观察到的剪切强度。从GPR模型获得的预测是最准确的，经测试得出的平均绝对误差值为5.6。