Concrete breakout strength of single anchors in tension using neural networks

Abstract

A feed forward neural network model for evaluating the concrete breakout strength of single cast-in and post-installed mechanical anchors in tension is presented. The nodes of the neural network input layer represent the embedment depth, anchor head diameter, concrete strength and anchor installation system, and the neural network output is the tensile capacity of anchors as governed by the concrete breakout. Three different techniques have been adopted to represent the anchor installation system in the neural network input layer. The training, validation and testing of the developed networks were based on a database of 451 experimental tests obtained from previous laboratory anchor tests. Testing of the trained neural network indicates good predictions of the concrete breakout strength of cast-in and post-installed mechanical anchors in tension. The relationships between the concrete breakout strength of anchors and different influencing parameters obtained from the trained neural networks were in general agreement with those of the ACI 318-02 for cast-in and post-installed mechanical anchors. It has been shown that the concrete breakout strength of anchors in tension is approximately proportional to the embedment depth of 1.5 power and marginally affected by changing the anchor head diameter.