LAUSR

Abstract Understanding the shear behavior of post-installed anchors embedded in thin concrete members is considered complicated due to the many parameters involved including embedment depth, edge distance, concrete strength, anchor diameter, and member thickness. This study utilizes the Artificial Neural Network (ANN) concept to predict the capacity and behavior of adhesive and screw anchors with full-thickness embedment in thin concrete members and compare it with the adopted design model in ACI 318 standard. First, A multilayered feed-forward ANN model trained with Bayesian Regularization training algorithm is developed and compared against the proposed model based on the concrete capacity design method (CCD). Then, a parametric study is presented to evaluate the contribution of each variable to the shear capacity and ensure generalization. The study shows that ANN model provides a higher level of accuracy in predicting the shear capacity than CCD model by reducing the coefficient of variation from 18% to 11%. In addition, the parametric study shows that the CCD model does not capture the nonlinear effect of the variables on the capacity accurately and does not include the interaction effect between variables. For instance, CCD shows that the member thickness has a nearly linear effect on the capacity while ANN shows higher nonlinearity. In addition, the study presents a reliability analysis to assess the shift in safety level by adopting the ANN model. The ANN provided an average increase in reliability index by 32%. This study is considered the first study to utilize ANN to anchors embedded in thin concrete members with full embedment depth.