Prediction of Concrete Breakout Strength of Single Anchors in Shear

Abstract

This study proposes a machine learning algorithim—a Gaussian process regression (GPR)—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 model. 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 predictive model yielded a determination coefficient ${\text{R}}^{2}$ = 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 and ACI 318) to validate its performance. The developed model provided a better prediction of the experimentally observed shear strength, compared to the existing models, yielding low mean absolute error, low bias and variability when tested.