Table of Contents
Advances in Artificial Neural Systems
Volume 2009, Article ID 193139, 8 pages
Research Article

Predicting Carbonation Depth of Prestressed Concrete under Different Stress States Using Artificial Neural Network

Department of Civil Engineering, Jiangsu University, Zhenjiang 212013, China

Received 17 February 2009; Revised 7 October 2009; Accepted 25 November 2009

Academic Editor: Alfredo Weitzenfeld

Copyright © 2009 Chunhua Lu and Ronggui Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Two artificial neural networks (ANN), back-propagation neural network (BPNN) and the radial basis function neural network (RBFNN), are proposed to predict the carbonation depth of prestressed concrete. In order to generate the training and testing data for the ANNs, an accelerated carbonation experiment was carried out, and the influence of stress level of concrete on carbonation process was taken into account especially. Then, based on the experimental results, the BPNN and RBFNN models which all take the stress level of concrete, water-cement ratio, cement-fine aggregate, cement-coarse aggregate ratio and testing age as input parameters were built and all the training and testing work was performed in MATLAB. It can be found that the two ANN models seem to have a high prediction and generalization capability in evaluation of carbonation depth, and the largest absolute percentage errors of BPNN and RBFNN are 10.88% and 8.46%, respectively. The RBFNN model shows a better prediction precision in comparison to BPNN model.