Estimation of Tensile Capacity of Single Anchors Including Edge Effect Using Neural Networks

Abstract

The demand for more flexibility in the design and strengthening of concrete structures has resulted in an increased use of anchoring systems. Adhesive anchors are widely used in seismic strengthening applications to add new structural members or sections to existing concrete members. To safely design such anchors, it is very important to know their tensile capacity under axial tensile forces. This paper explores the pullout capacity of single adhesive anchors loaded in tension in un-cracked concrete. To this end 142 single anchor tests including edge effect (located near a concrete edge) were obtained from literature. The formulated three-layered artificial neural network method (ANN) was trained using 75% of the data set by using different learning algorithms. The methods were tested with the remaining 25% of the data set. The variables taken into account in this study are anchor diameter, embedment length, concrete body height and edge distance. It was determined that experimental data can be estimated to a notably close extent via the ANN model.