DocumentCode :
690406
Title :
Time Series Prediction Model of Concrete Corrosion in Sulfuric Based on SVM
Author :
Yang Gao ; Zhigang Song
Author_Institution :
Fac. of Civil Eng., Kunming Univ. of Sci. & Technol., Kunming, China
fYear :
2013
fDate :
14-15 Dec. 2013
Firstpage :
560
Lastpage :
563
Abstract :
In this paper, a long-team immersion of concrete in dilute sulfuric acid is carried out. On the basis of the experimental data, a time serious prediction model of concrete corrosion in sulfuric based on support vector machine (SVM) is developed. The design steps and learning algorithm are also given. Comparing with the test result, this model has good predictive function, with the suitable reconstructed space matrix and prediction step length. Using the latter 20 groups of test data which data spaces are reconstructed as the prediction set, most of the prediction relative error(Er) can be controlled within 15%, and only a small amount are within 30%. It implies that this model can be a new powerful way for study of concrete corrosion resistance.
Keywords :
concrete; corrosion resistance; learning (artificial intelligence); materials science computing; matrix algebra; support vector machines; time series; SVM; concrete corrosion resistance; dilute sulfuric acid; learning algorithm; prediction relative error; prediction step length; predictive function; reconstructed space matrix; support vector machines; time series prediction model; Concrete; Data models; Genetic algorithms; Kernel; Predictive models; Support vector machines; Time series analysis; Time serious; concrete; prediction model; sulfuric; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Sciences and Applications (CSA), 2013 International Conference on
Conference_Location :
Wuhan
Type :
conf
DOI :
10.1109/CSA.2013.136
Filename :
6835663
Link To Document :
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