Artificial neural networks application to predict bond steel-concrete in pull-out tests

Abstract

This study aims the possibility of using the pull-out test results - bond tests steel-concrete, that has been successfully carried out by the research group APULOT since 2008 [¹]. This research demonstrates that the correlation between bond stress and concrete compressive strength allows estimate concrete compressive strength. However to obtain adequate answers testing of bond steel-concrete is necessary to control the settings test. This paper aims to correlate the results of bond tests of type pull-out with its variables by using Artificial Neural Networks (ANN). Though an ANN is possible to correlate the known input data (age rupture, anchorage length, covering and compressive strength of concrete) with control parameters (bond stress steel-concrete). To generate the model it is necessary to train the neural network using a database with known input and output parameters. This allows estimating the correlation between the neurons in each layer. This paper shows the modeling of an ANN capable of performing a nonlinear approach to estimate the concrete compressive strength using the results of steel-concrete bond tests.

Keywords:
bond steel-concrete; artificial neural networks; pull-out test; concrete strength; APULOT test