Artificial Neural Network Analysis of Concrete Carbonation under Sustained Loads

摘要：

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 stressed concrete. In order to generate the training and testing data for the ANNs, an accelerated carbonation experiment was carried out for stressed concrete specimens. Based on the experimental results, the BPNN and RBFNN models which all take the stress level of concrete, water-cement ratio, cement-fine aggregate ratio, 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.

关键词： neural network stressed concrete carbonation depth predicting

作者: Hui Li Chunhua Lu

作者单位: School of Computer Science and Telecommunication Engineering Jiangsu University Zhenjiang, China Faculty of Science Jiangsu University Zhenjiang, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 160-164

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）