Title: Strength Evaluation of Single Adhesive Concrete Anchors Under Tensile Load Using Artificial Neural Networks

Author(s): A.M. Said and S.E. Robinson

Publication: Symposium Paper

Volume: 283

Issue:

Appears on pages(s): 1-20

Keywords: adhesive, anchors, concrete, neural network, post-installed, bond failure

DOI: 10.14359/51683773

Date: 3/1/2012

Abstract:
Adhesive-bonded anchors are increasingly adopted as structural fasteners for connections to hardened concrete. Due to their reliance on chemical bond, the tensile capacity of adhesive anchors is uniquely dependant on a number of factors [1]. These factors include the geometric parameters of the anchorage system, installation conditions, and adhesive bond strength which is manufacture dependent. Due to the complexity of these factors and their interaction in contributing to the tensile capacity of adhesive concrete anchors, it has proved to be difficult to evaluate their tensile strength. The design guidelines of anchorages using cast-in-place and post-installed mechanical anchors is discussed in ACI 318-08, Appendix D [2]. While, bonded anchors are used extensively in practice, they have not yet been incorporated into the design provisions of ACI 318-08 [3]. The worldwide database containing 2,878 tests of the anchors’ tensile capacity was provided to the authors by Dr. Ronald A. Cook, of ACI Committee 355 on Anchorage to Concrete. The aim of this study is to estimate the tensile strength of concrete adhesive anchors in uncracked concrete using artificial neural networks (ANNs) subject to bond failure and the effect of different parameters on it. As a result of this study, the ANN model will be able to capture the complex relationship between the adhesive bond stress and geometric parameters that compose the anchorage system.