NDLI: Artificial neural network analysis of concrete carbonation under sustained loads

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Artificial neural network analysis of concrete carbonation under sustained loads

Content Provider	IEEE Xplore Digital Library
Author	Hui Li Chunhua Lu
Copyright Year	2010
Description	Author affiliation: Faculty of Science, Jiangsu University, Zhenjiang, China (Chunhua Lu) \|\| School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China (Hui Li)
Abstract	Two artificial neural networks (ANN), backpropagation 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.
File Size	653620
File Format	PDF
ISBN	9781424472352
e-ISBN	9781424472376
DOI	10.1109/ICCASM.2010.5622849
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predicting Biological system modeling Predictive models Bismuth Concrete Stressed concrete Neural network Acceleration Carbonation depth Load modeling
Content Type	Text
Resource Type	Article

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